pax_global_header00006660000000000000000000000064151127675640014530gustar00rootroot0000000000000052 comment=d2c74dcceb3d5b5ebfd7e55657a06ac2e2124e80 symfc-1.6.0/000077500000000000000000000000001511276756400126555ustar00rootroot00000000000000symfc-1.6.0/.github/000077500000000000000000000000001511276756400142155ustar00rootroot00000000000000symfc-1.6.0/.github/workflows/000077500000000000000000000000001511276756400162525ustar00rootroot00000000000000symfc-1.6.0/.github/workflows/publish-gh-pages.yml000066400000000000000000000020771511276756400221420ustar00rootroot00000000000000name: publish symfc gh-pages on: push: branches: [publish-gh-pages] jobs: docs: runs-on: ubuntu-latest defaults: run: shell: bash -l {0} strategy: matrix: python-version: ["3.12"] steps: - uses: actions/checkout@v4 - uses: conda-incubator/setup-miniconda@v3 with: auto-update-conda: true channels: conda-forge channel-priority: strict python-version: ${{ matrix.python-version }} - name: Install dependencies run: | conda activate test conda install --yes -c conda-forge python=${{ matrix.python-version }} conda install --yes -c conda-forge sphinx-book-theme linkify-it-py myst-parser sphinxcontrib-bibtex ipython - name: Build run: | conda activate test sphinx-build doc docs_build - name: Deploy docs at develop branch if: ${{ github.ref == 'refs/heads/publish-gh-pages' }} uses: peaceiris/actions-gh-pages@v4 with: github_token: ${{ secrets.GITHUB_TOKEN }} publish_dir: ./docs_build symfc-1.6.0/.github/workflows/publish-to-test-pypi.yml000066400000000000000000000020401511276756400230130ustar00rootroot00000000000000name: publish PyPI and TestPyPI on: push: branches: - main - rc jobs: build-linux: runs-on: ubuntu-latest strategy: matrix: python-version: [3.12, ] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Make sdist run: | pip install numpy scipy build python -m build - name: Publish package to TestPyPI if: github.event_name == 'push' && startsWith(github.ref, 'refs/heads/rc') uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.TEST_PYPI_API_TOKEN }} repository_url: https://test.pypi.org/legacy/ - name: Publish package to PyPI if: github.event_name == 'push' && startsWith(github.ref, 'refs/heads/main') uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_API_TOKEN }} symfc-1.6.0/.github/workflows/tests.yml000066400000000000000000000021231511276756400201350ustar00rootroot00000000000000name: symfc test using conda-forge environment on: pull_request: branches: [ develop ] jobs: build-linux: runs-on: ubuntu-latest defaults: run: shell: bash -l {0} strategy: matrix: python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] steps: - uses: actions/checkout@v4 - uses: conda-incubator/setup-miniconda@v3 with: auto-update-conda: true channels: conda-forge channel-priority: strict python-version: ${{ matrix.python-version }} - name: Install dependencies run: | conda activate test conda install --yes -c conda-forge python=${{ matrix.python-version }} conda install --yes -c conda-forge spglib scipy numpy pytest codecov pytest-cov - name: Setup symfc run: | conda activate test pip install -e . -vvv - name: Test with pytest run: | conda activate test pytest -v --cov=./ --cov-report=xml - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v3 with: verbose: true symfc-1.6.0/.gitignore000066400000000000000000000060271511276756400146520ustar00rootroot00000000000000# Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/#use-with-ide .pdm.toml # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ .vscode _build/ symfc-1.6.0/.pre-commit-config.yaml000066400000000000000000000007201511276756400171350ustar00rootroot00000000000000# See https://pre-commit.com for more informatio # See https://pre-commit.com/hooks.html for more hooks repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v6.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml exclude: ^conda/ - id: check-added-large-files - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.14.6 hooks: - id: ruff args: [ "--fix", "--show-fixes" ] - id: ruff-format symfc-1.6.0/LICENSE000066400000000000000000000027341511276756400136700ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2023, symfc project Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. symfc-1.6.0/MANIFEST.in000066400000000000000000000000141511276756400144060ustar00rootroot00000000000000graft tests symfc-1.6.0/README.md000066400000000000000000000031021511276756400141300ustar00rootroot00000000000000# symfc ## What does symfc do? Atomic vibrations in crystals are often conveniently described using the phonon model. In this model, the crystal potential is expanded into a Taylor series with respect to atomic displacements from their equilibrium positions, and the expansion coefficients are referred to as force constants. Predicting phonon properties through computer simulations is becoming increasingly popular, with the supercell approach being one of the techniques employed for phonon calculations. In this method, force constants are derived from datasets of atomic forces and displacements obtained from supercell snapshots, which feature various configurations of atomic displacements. While force constants possess specific symmetries, those computed from displacement-force datasets often do not adhere to these symmetries due to factors such as numerical noise or approximations used. Symfc is a software designed to compute force constants from displacement-force datasets in the supercell approach, ensuring they meet the required symmetry constraints. ## Citation of symfc "Projector-based efficient estimation of force constants", A. Seko and A. Togo, Phys. Rev. B, **110**, 214302 (2024) [[doi](https://doi.org/10.1103/PhysRevB.110.214302)] [[arxiv](https://arxiv.org/abs/2403.03588)]. ``` @article{PhysRevB.110.214302, title = {Projector-based efficient estimation of force constants}, author = {Seko, Atsuto and Togo, Atsushi}, journal = {Phys. Rev. B}, volume = {110}, issue = {21}, pages = {214302}, numpages = {18}, year = {2024}, month = {Dec}, } ``` symfc-1.6.0/doc/000077500000000000000000000000001511276756400134225ustar00rootroot00000000000000symfc-1.6.0/doc/Makefile000066400000000000000000000011721511276756400150630ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = _build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) symfc-1.6.0/doc/changelog.md000066400000000000000000000027001511276756400156720ustar00rootroot00000000000000(changelog)= # Change Log ## Nov-30-2025: Version 1.6.0 - Sparse solver for FC2. - Deprecated `displacements` and `forces` parameters for instantiating the `Symfc` class. Use its attributes after instantiation. ## Jul-21-2025: Version 1.5.4 - Improve eigensolver for large projector. - Check symmetric property of projector and make it symmetric unless the symmetry is largely broken. ## Jul-15-2025: Version 1.5.3 - More optimization for basis set computations. ## Jul-14-2025: Version 1.5.2 - Improve memory efficiency and calculation performance for basis set computations using block matrix tree. ## Jul-10-2025: Version 1.5.1 - Improve memory efficiency and calculation performance for basis set computations. The current implementation shows particular effectiveness with `use_mkl=True` (requires sparse-dot-mkl) for large systems. ## Jul-3-2025: Version 1.5.0 - Improve memory efficiency in computing basis sets ## Jun-26-2025: Version 1.4.1 - Small fix for specific case ## May-31-2025: Version 1.4.0 - Maintenance release after refactoring ## Feb-25-2025: Version 1.3.4 - Maintenance release after refactoring ## Feb-11-2025: Version 1.3.3 - Fix minor translational invariance issues in O2, O3, and O4 ## Feb-10-2025: Version 1.3.2 - Use numerically stable version of O3 translational invariance ## Feb-5-2025: Version 1.3.1 - Enabled cutoff parameter for fc2. ## Feb-4-2025: Version 1.3.0 - Add `Symfc.estimate_basis_set()`. symfc-1.6.0/doc/conf.py000066400000000000000000000026541511276756400147300ustar00rootroot00000000000000# Configuration file for the Sphinx documentation builder. # # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = "symfc" copyright = "2024, symfc project" author = "Atsuto Seko" version = "1.6" release = "1.6.0" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ "sphinx.ext.mathjax", "myst_parser", "IPython.sphinxext.ipython_console_highlighting", "IPython.sphinxext.ipython_directive", "sphinx.ext.extlinks", ] myst_enable_extensions = ["linkify", "dollarmath", "amsmath"] templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "sphinx_book_theme" html_static_path = ["_static"] html_title = "Symfc v.%s" % release extlinks = { "issue": ("https://github.com/symfc/symfc/issues/%s", "issue %s"), "path": ("https://github.com/symfc/symfc/tree/develop/%s", "%s"), "user": ("https://github.com/%s", "%s"), } symfc-1.6.0/doc/index.md000066400000000000000000000027571511276756400150660ustar00rootroot00000000000000# Symfc Symfc is a Python-based force-constants solver that uses the supercell approach. By employing an efficient projector-based algorithm that leverages crystal and force-constant symmetries, it significantly reduces computational and memory requirements. The code accepts displacement–force datasets as input and outputs supercell force constants. Symfc supports the calculation of second-, third-, and fourth-order force constants. ## Usage Detailed documentation will be provided soon. In the meantime, please refer to `api_symfc.py` for more information. Additionally, an [example implementation](https://github.com/phonopy/phonopy/blob/master/phonopy/interface/symfc.py) can be found in the phonopy code, particularly in the `SymfcFCSolver._initialize` method. ## License BSD-3-Clause. ## Citation of symfc "Projector-based efficient estimation of force constants", A. Seko and A. Togo, Phys. Rev. B, **110**, 214302 (2024) [[doi](https://doi.org/10.1103/PhysRevB.110.214302)] [[arxiv](https://arxiv.org/abs/2403.03588)]. ``` @article{PhysRevB.110.214302, title = {Projector-based efficient estimation of force constants}, author = {Seko, Atsuto and Togo, Atsushi}, journal = {Phys. Rev. B}, volume = {110}, issue = {21}, pages = {214302}, numpages = {18}, year = {2024}, month = {Dec}, } ``` ## Contributors - {user}`Atsuto Seko ` (Kyoto university) - {user}`Atsushi Togo ` (National Institute for Materials Science) ```{toctree} :hidden: install changelog ``` symfc-1.6.0/doc/install.md000066400000000000000000000007361511276756400154200ustar00rootroot00000000000000# Installation PyPI and conda-forge packages are available. - https://pypi.org/project/symfc - https://anaconda.org/conda-forge/symfc ## Requirement - numpy - scipy - spglib (optional) ## Installation from source code A simplest installation using conda-forge packages: ```bash % conda create -n symfc -c conda-forge % conda activate symfc % conda install -c conda-forge numpy scipy spglib % git clone https://github.com/symfc/symfc.git % cd symfc % pip install -e . ``` symfc-1.6.0/examples/000077500000000000000000000000001511276756400144735ustar00rootroot00000000000000symfc-1.6.0/examples/NaCl444-rd/000077500000000000000000000000001511276756400161475ustar00rootroot00000000000000symfc-1.6.0/examples/NaCl444-rd/README.md000066400000000000000000000004541511276756400174310ustar00rootroot00000000000000# NaCl example The python example is found in `calc-fc.py`. Force constants are calculated from 20 supercell snapshots. The crystal structure, displacement-force dataset, and parameters needed for non-analytical term correction are found in `phonopy_NaCl444_rd.yaml.xz` in the phonopy-yaml format. symfc-1.6.0/examples/NaCl444-rd/calc-fc.py000066400000000000000000000006051511276756400200120ustar00rootroot00000000000000"""Calculate force constants using symfc.""" import phonopy from symfc import Symfc ph = phonopy.load("phonopy_NaCl444_rd.yaml.xz", produce_fc=False) symfc = Symfc( ph.supercell, displacements=ph.dataset["displacements"], forces=ph.dataset["forces"], log_level=1, ).run(orders=[2]) ph.force_constants = symfc.force_constants[2] ph.auto_band_structure(plot=True).show() symfc-1.6.0/examples/NaCl444-rd/phonopy_NaCl444_rd.yaml.xz000066400000000000000000013142701511276756400230150ustar00rootroot000000000000007zXZִF!t/够]8 DKIa2×M}0h/tt=oǫt#fkM{(A w6uB!D3 q7(r +3ُ_LdhSDoq>uR/ ؘ 8׊QыMi(k0;U3%\Vzj m+ 8OA)Pp^Q2g.WQlA+n(kX_zr/yH@d >k}ä$:1lf8ܤ+])8 p;~ZWl:"^5qhߘOV4 QQ|1C}[l:S)@K"ATwNF 榘iˀѠԺRX ^UoVc 48::B&*o$-K&\ +;mW<>Z[L.BXCi ~%~ 6&t^7p>]?Q=mD̄>`;82A(pl ƣy4]rf6#BCi I.8e{=I$n4׵5}uɮ\E-9ϖ7P/Pzq[icjh+8~Դ;qAU|d#y>&a%cͨwEzŕ.ЩV|`k"1ar\ӴlP{@ChRJLxa?\4a8qG7gN8rxM re(᠋&f)et#݊1dK;pn5ۓ'9}a;_+0Y3?8oFM腁W1=q}۹3<LJf zG ݡpWȾ)4Mqq7`Ey"H<4C5Т 6J5# _输4?si[F, A;P\b@{ݧwĠVtJ$w~gDueT^ނ`vFC(/bՌChm"gQ3 r|dj9&s8b7ZVnA= 9&OV1Kt\4+.Ixd; 9v,Z !*ڡI ŷ@E`0Hl))1Gǣ/Zc>WpP wdTib}AlZt`@V&fx>y`E 7-ȋ|j-p\dăĹG6=dj%pa>(l6{(y#;oUDvVuу 2&|zB uʥѫT+~baoS4x=lS]p`Ʊ?c=_u< qn~wS|5m t1,7k %D^ʧՒEP|zr ;34u 2$jk'c~D H_@6ZOd+Ph,1d@EyxaaS~%DIVFx kvȮ_֑5{zGS`H:(RoF3PpG9$ևNYp9ب!J)=JS,&T/u\{t {8gpMe|T M Ǵظ=6oqh"RrVXpUK9ǩ \."uz!ԬU'=-$ux?F,4V!ihѼT\EStբý9i{is c?-uHOi%`_/ Ɗ7[ 0fiMP,4ɫXb5fWstqGw]qZ_5R#]؎,}v zr vx9ykszN1&>]rD,uuiy:ׂIOЮLyg5o;zSY7H{67|x(X w@7"08,{MMzK'EUͫ JYu u#vтOk#4xxmнZѠ΋F@Se2=Yk̮pnM1ecu=R WD!#⍭H08lR9\BlSMz-n| `W>s48 OtAY6?#V6~1Y3Uq27+@d)]UmAb<8մU$G:_ZV FA)I^4.7Tx#q9SDEf?\}1ڄa>ƥ.J. 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RHJR>$ՠX/mEӥ1^uaN6U7rEDYu9Ɩ j[p{d5*EBDAYa,Bتߜ:$S?W-.DmHaj/Yׅzf:.Dx^!ִ6c0z [?ҐFbhb /t5W#͗9w Ω5*0 oJ+(1ޫov\u#2Haf0KQ d92jBHG's.xʒyf8j-HY ˇQVz+`{`/$hO]{EIr@Бu1ߒ*V$-I)4צŃ|$ tݘd++:YU=g}Hz*CöINOD>~DWz;\?g)Pp<_/1ǫ9$>6Wݓ{`ZSU*nRR=uWnAA,@`Kԥj[sj)_>>i" >[b./]eD6*(?0"2YrYX|r90$ K1ʦuꡜnIA{ml$j7' r&55M= 61.0"] build-backend = "setuptools.build_meta" [project] name = "symfc" dynamic = ["version"] readme = {file = "README.md", content-type = "text/markdown"} description = "This is the symfc module." authors = [ { name = "Atsuto Seko", email = "seko@cms.mtl.kyoto-u.ac.jp" } ] maintainers = [ {name = "Atsushi Togo", email = "atztogo@gmail.com"} ] requires-python = ">=3.9" dependencies = [ "numpy", "scipy != 1.11.0, != 1.11.1, != 1.11.2", "spglib>=2.5", ] license-files = ["LICENSE"] [project.urls] "Homepage" = "https://github.com/symfc/symfc" [tool.setuptools.dynamic] version = { attr = "symfc.version.__version__" } [tool.ruff] line-length = 88 select = [ "F", # Flake8 "B", # Black "I", # isort "E", # pycodestyle-errors "D", # pydocstyle ] extend-ignore = [ "D417", "D100", ] [tool.ruff.lint.pydocstyle] convention = "numpy" symfc-1.6.0/src/000077500000000000000000000000001511276756400134445ustar00rootroot00000000000000symfc-1.6.0/src/symfc/000077500000000000000000000000001511276756400145655ustar00rootroot00000000000000symfc-1.6.0/src/symfc/__init__.py000066400000000000000000000002601511276756400166740ustar00rootroot00000000000000"""Forcd constants calculation code: Symfc.""" from .api_symfc import Symfc, eigh, eigsh from .version import __version__ __all__ = ["Symfc", "__version__", "eigh", "eigsh"] symfc-1.6.0/src/symfc/api_symfc.py000066400000000000000000000552311511276756400171170ustar00rootroot00000000000000"""Symfc API.""" from __future__ import annotations import warnings from collections.abc import Sequence from typing import cast import numpy as np from numpy.typing import NDArray from scipy.sparse import csr_array from symfc.basis_sets import FCBasisSetBase, FCBasisSetO2, FCBasisSetO3, FCBasisSetO4 from symfc.solvers import ( FCSolverO2, FCSolverO2O3, FCSolverO2O3O4, FCSolverO3, FCSolverO3O4, FCSolverO4, FCSparseSolverO2, ) from symfc.utils.eig_tools import ( eigh_projector, eigsh_projector, eigsh_projector_sumrule, ) from symfc.utils.utils import SymfcAtoms class Symfc: """Symfc API.""" def __init__( self, supercell: SymfcAtoms, displacements: NDArray | None = None, forces: NDArray | None = None, spacegroup_operations: dict | None = None, cutoff: dict[int, float] | None = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. displacements : ndarray, optional, will be deprecated around v1.7 Displacements of supercell atoms. shape=(n_snapshot, natom, 3), dtype='double', order='C' forces : ndarray, optional, will be deprecated around v1.7 Forces of supercell atoms. shape=(n_snapshot, natom, 3), dtype='double', order='C' spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like cutoff : dict[int, float], optional Cutoff radii in angstrom for FC3 and FC4, by default None. For example, {3: 4.0, 4: 4.0} use_mkl : bool, optional Use MKL library, by default False. log_level : int, optional Log level, by default 0. """ self._supercell = supercell if displacements is not None: warnings.warn( ( "displacements argument in __init__ will be deprecated around v1.7." " Use displacements attribute instead." ), DeprecationWarning, stacklevel=2, ) self._displacements = displacements if forces is not None: warnings.warn( ( "forces argument in __init__ will be deprecated around v1.7. " "Use forces attribute instead." ), DeprecationWarning, stacklevel=2, ) self._forces = forces self._spacegroup_operations = spacegroup_operations self._use_mkl = use_mkl self._log_level = log_level self._basis_set: dict[int, FCBasisSetBase] = {} self._force_constants: dict[int, NDArray] = {} self._prepare_cutoff(cutoff) self._atoms_fd: dict = {} self._displacements_fd: dict = {} @property def supercell(self) -> SymfcAtoms: """Return supercell.""" return self._supercell @property def p2s_map(self) -> NDArray | None: """Return indices of translationally independent atoms.""" if self._basis_set: return next(iter(self._basis_set.values())).p2s_map else: raise ValueError("No FCBasisSet set is not set.") @property def basis_set(self) -> dict[int, FCBasisSetBase]: """Setter and getter of basis set. dict[FCBasisSet] The key is the order of basis set in int. """ return self._basis_set @basis_set.setter def basis_set(self, basis_set): self._basis_set = basis_set @property def force_constants(self) -> dict[int, NDArray]: """Return force constants. Returns ------- dict[NDArray] The key is the order of force_constants in int. """ return self._force_constants @property def displacements(self) -> NDArray | None: """Setter and getter of supercell displacements. ndarray shape=(n_snapshot, natom, 3), dtype='double', order='C' """ return self._displacements @displacements.setter def displacements(self, displacements: NDArray | Sequence): self._displacements = np.array(displacements, dtype="double", order="C") @property def displacements_fd(self) -> dict: """Setter and getter of supercell displacements for finite displacements. dict[int, ndarray] key: order value: shape=(n_snapshot, 3), dtype='double', order='C' """ return self._displacements_fd @displacements_fd.setter def displacements_fd(self, displacements: dict): self._displacements_fd = displacements @property def atoms_fd(self) -> dict: """Setter and getter of atoms with displacements for finite displacements. dict[int, ndarray] key: order value: shape=(n_snapshot), dtype='int', order='C' """ return self._atoms_fd @atoms_fd.setter def atoms_fd(self, atoms: dict): self._atoms_fd = atoms @property def forces(self) -> NDArray | None: """Setter and getter of supercell forces. ndarray shape=(n_snapshot, natom, 3), dtype='double', order='C' """ return self._forces @forces.setter def forces(self, forces: NDArray | Sequence): self._forces = np.array(forces, dtype="double", order="C") def run( self, max_order: int | None = None, orders: list | None = None, is_compact_fc: bool = True, batch_size: int = 100, ) -> Symfc: """Run basis set and force constants calculation. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. is_compact_fc: bool Return compact force constants. batch_size : int, optional Batch size in solvers, by default 100. """ if ( self._displacements is not None and self._forces is not None ) or self.use_fd: self.compute_basis_set(max_order=max_order, orders=orders) self.solve( max_order=max_order, orders=orders, is_compact_fc=is_compact_fc, batch_size=batch_size, ) return self @property def use_fd(self) -> bool: """Return whether finite displacement method is used.""" return bool(self._displacements_fd) and bool(self._atoms_fd) def solve( self, max_order: int | None = None, orders: list | None = None, is_compact_fc: bool = True, batch_size: int = 100, ) -> Symfc: """Calculate force constants. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. is_compact_fc: bool Return compact force constants. batch_size : int, optional Batch size in solvers, by default 100. """ if self.use_fd: self.solve_sparse( max_order=max_order, orders=orders, is_compact_fc=is_compact_fc, ) else: self.solve_dense( max_order=max_order, orders=orders, is_compact_fc=is_compact_fc, batch_size=batch_size, ) return self def solve_sparse( self, max_order: int | None = None, orders: list | None = None, is_compact_fc: bool = True, ) -> Symfc: """Calculate force constants. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. is_compact_fc: bool Return compact force constants. batch_size : int, optional Batch size in solvers, by default 100. """ self._check_dataset() _orders = self._check_orders(max_order, orders) if self._atoms_fd is None: raise RuntimeError("Atoms not found.") if self._displacements_fd is None: raise RuntimeError("Displacements not found.") if self._forces is None: raise RuntimeError("Forces not found.") if _orders == (2,): basis_set_o2: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[2]) solver_o2 = FCSparseSolverO2( basis_set_o2, use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._atoms_fd[2], self._displacements_fd[2], self._forces) if is_compact_fc: if solver_o2.compact_fc is not None: self._force_constants[2] = solver_o2.compact_fc else: raise RuntimeError("Failed to obtain compact force constants") else: if solver_o2.full_fc is not None: self._force_constants[2] = solver_o2.full_fc else: raise RuntimeError("Failed to obtain full force constants") else: raise RuntimeError("Sparse FD solver not supported.") return self def solve_dense( self, max_order: int | None = None, orders: list | None = None, is_compact_fc: bool = True, batch_size: int = 100, ) -> Symfc: """Calculate force constants. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. is_compact_fc: bool Return compact force constants. batch_size : int, optional Batch size in solvers, by default 100. """ self._check_dataset() _orders = self._check_orders(max_order, orders) if self._displacements is None: raise RuntimeError("Displacements not found.") if self._forces is None: raise RuntimeError("Forces not found.") if _orders == (2,): basis_set_o2: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[2]) solver_o2 = FCSolverO2( basis_set_o2, use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces) if is_compact_fc: if solver_o2.compact_fc is not None: self._force_constants[2] = solver_o2.compact_fc else: raise RuntimeError("Failed to obtain compact force constants") else: if solver_o2.full_fc is not None: self._force_constants[2] = solver_o2.full_fc else: raise RuntimeError("Failed to obtain full force constants") elif _orders == (3,): basis_set_o3: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[3]) solver_o3 = FCSolverO3( basis_set_o3, use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces) if is_compact_fc: if solver_o3.compact_fc is not None: self._force_constants[3] = solver_o3.compact_fc else: raise RuntimeError("Failed to obtain compact force constants") else: if solver_o3.full_fc is not None: self._force_constants[3] = solver_o3.full_fc else: raise RuntimeError("Failed to obtain full force constants") elif _orders == (4,): basis_set_o4: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[4]) solver_o4 = FCSolverO4( basis_set_o4, use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces) if is_compact_fc: if solver_o4.compact_fc is not None: self._force_constants[4] = solver_o4.compact_fc else: raise RuntimeError("Failed to obtain compact force constants") else: if solver_o4.full_fc is not None: self._force_constants[4] = solver_o4.full_fc else: raise RuntimeError("Failed to obtain full force constants") elif _orders == (2, 3): basis_set_o2: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[2]) basis_set_o3: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[3]) solver_o2o3 = FCSolverO2O3( [basis_set_o2, basis_set_o3], use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces, batch_size=batch_size) if is_compact_fc and solver_o2o3.compact_fc is not None: fc2, fc3 = solver_o2o3.compact_fc elif solver_o2o3.full_fc is not None: fc2, fc3 = solver_o2o3.full_fc else: raise RuntimeError("Failed to obtain force constants") self._force_constants[2] = fc2 self._force_constants[3] = fc3 elif _orders == (3, 4): basis_set_o3: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[3]) basis_set_o4: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[4]) solver_o3o4 = FCSolverO3O4( [basis_set_o3, basis_set_o4], use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces, batch_size=batch_size) if is_compact_fc and solver_o3o4.compact_fc is not None: fc3, fc4 = solver_o3o4.compact_fc elif solver_o3o4.full_fc is not None: fc3, fc4 = solver_o3o4.full_fc else: raise RuntimeError("Failed to obtain force constants") self._force_constants[3] = fc3 self._force_constants[4] = fc4 elif _orders == (2, 3, 4): basis_set_o2: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[2]) basis_set_o3: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[3]) basis_set_o4: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[4]) solver_o2o3o4 = FCSolverO2O3O4( [basis_set_o2, basis_set_o3, basis_set_o4], use_mkl=self._use_mkl, log_level=self._log_level, ).solve(self._displacements, self._forces, batch_size=batch_size) if is_compact_fc and solver_o2o3o4.compact_fc is not None: fc2, fc3, fc4 = solver_o2o3o4.compact_fc elif solver_o2o3o4.full_fc is not None: fc2, fc3, fc4 = solver_o2o3o4.full_fc else: raise RuntimeError("Failed to obtain force constants") self._force_constants[2] = fc2 self._force_constants[3] = fc3 self._force_constants[4] = fc4 return self def compute_basis_set( self, max_order: int | None = None, orders: list | None = None, ) -> Symfc: """Run basis set calculations. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. """ for order in self._check_orders(max_order, orders): if order == 2: basis_set_o2 = FCBasisSetO2( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[2], use_mkl=self._use_mkl, log_level=self._log_level, ).run() self._basis_set[2] = basis_set_o2 elif order == 3: basis_set_o3 = FCBasisSetO3( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[3], use_mkl=self._use_mkl, log_level=self._log_level, ).run() self._basis_set[3] = basis_set_o3 elif order == 4: basis_set_o4 = FCBasisSetO4( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[4], use_mkl=self._use_mkl, log_level=self._log_level, ).run() self._basis_set[4] = basis_set_o4 return self def estimate_basis_size( self, max_order: int | None = None, orders: list | None = None, ) -> dict: """Estimate the size of basis set. Parameters ---------- max_order : int Maximum fc order. orders: list Orders of force constants. Returns ------- dict : Estimates of basis set sizes for each order. The key of dict is the order. """ basis_size_estimates = {} for order in self._check_orders(max_order, orders): if order < 2 or order > 4: raise NotImplementedError( "Only fc2, fc3 and fc4 basis sets are implemented." ) if order == 2: basis_size_estimates[order] = FCBasisSetO2( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[2], use_mkl=self._use_mkl, log_level=self._log_level, ).estimate_basis_size() elif order == 3: basis_size_estimates[order] = FCBasisSetO3( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[3], use_mkl=self._use_mkl, log_level=self._log_level, ).estimate_basis_size() elif order == 4: basis_size_estimates[order] = FCBasisSetO4( self._supercell, spacegroup_operations=self._spacegroup_operations, cutoff=self._cutoff[4], use_mkl=self._use_mkl, log_level=self._log_level, ).estimate_basis_size() return basis_size_estimates def _check_orders(self, max_order: int | None, orders: list | None) -> tuple: if max_order is None and orders is None: raise RuntimeError("Maximum order and orders not found.") if max_order is not None: if max_order not in (2, 3, 4): raise NotImplementedError( "Only fc2, fc3 and fc4 basis sets are implemented." ) _orders = tuple(list(range(2, max_order + 1))) elif orders is not None: _orders = tuple(sorted(orders)) if _orders not in [(2,), (3,), (4,), (2, 3), (3, 4), (2, 3, 4)]: raise RuntimeError("Invalid FC orders.") return _orders def _check_dataset(self): if self._displacements is None and self._displacements_fd is None: raise RuntimeError("Dispalcements not found.") if self._displacements_fd is not None: if self._atoms_fd is None: raise RuntimeError("Atoms not found.") if self._forces is None: raise RuntimeError("Forces not found.") if self._displacements is not None: if self._displacements.shape != self._forces.shape: raise RuntimeError("Shape mismatch between dispalcements and forces.") if self._displacements.ndim != 3 or self._displacements.shape[1:] != ( len(self._supercell), 3, ): raise RuntimeError( "Inconsistent array shape of displacements " f"{self._displacements.shape} with respect to supercell " f"{len(self._supercell)}." ) if self._forces.ndim != 3 or self._forces.shape[1:] != ( len(self._supercell), 3, ): raise RuntimeError( "Inconsistent array shape of forces " f"{self._forces.shape} with respect to supercell " f"{len(self._supercell)}." ) def _prepare_cutoff(self, cutoff): if cutoff is None: self._cutoff = {2: None, 3: None, 4: None} else: self._cutoff = {} for order in (2, 3, 4): if order in cutoff: self._cutoff[order] = cutoff[order] else: self._cutoff[order] = None def eigh( p: NDArray, atol: float = 1e-8, rtol: float = 0.0, log_level: int = 0, ) -> NDArray: """Solve eigenvalue problem for projector in numpy ndarray. Parameters ---------- p: NDArray Projection matrix to be solved. atol : float, optional atol used in np.isclose. rtol : float, optional rtol used in np.isclose. log_level : int, optional Log level, by default 0. Return ------ Eigenvectors with eigenvalues = 1.0 in NDArray format. Eigenvectors with eigenvalues < 1.0 are eliminated. """ return eigh_projector(p, atol=atol, rtol=rtol, verbose=log_level > 0) def eigsh( p: csr_array, atol: float = 1e-8, rtol: float = 0.0, is_large_block: bool = False, log_level: int = 0, ) -> csr_array | NDArray: """Solve eigenvalue problem for projector in scipy sparse csr_array. Parameters ---------- p: csr_array Projection matrix to be solved. atol : float, optional atol used in np.isclose. rtol : float, optional rtol used in np.isclose. is_large_block: bool, optional Use an algorithm for solving projector with large block matrices. log_level : int, optional Log level, by default 0. Return ------ Eigenvectors with eigenvalues = 1.0. If is_large_block is True, eigenvectors in NDArray are returned. Otherwise, eigenvectors in csr_array are returned. Eigenvectors with eigenvalues < 1.0 are eliminated. """ if is_large_block: return eigsh_projector_sumrule(p, atol=atol, rtol=rtol, verbose=log_level > 0) return eigsh_projector(p, atol=atol, rtol=rtol, verbose=log_level > 0) symfc-1.6.0/src/symfc/basis_sets/000077500000000000000000000000001511276756400167245ustar00rootroot00000000000000symfc-1.6.0/src/symfc/basis_sets/__init__.py000066400000000000000000000005701511276756400210370ustar00rootroot00000000000000"""Symmetry adapted basis sets of force constants.""" from .basis_sets_base import FCBasisSetBase from .basis_sets_O1 import FCBasisSetO1 from .basis_sets_O2 import FCBasisSetO2 from .basis_sets_O3 import FCBasisSetO3 from .basis_sets_O4 import FCBasisSetO4 __all__ = [ "FCBasisSetBase", "FCBasisSetO1", "FCBasisSetO2", "FCBasisSetO3", "FCBasisSetO4", ] symfc-1.6.0/src/symfc/basis_sets/basis_sets_O1.py000066400000000000000000000101001511276756400217640ustar00rootroot00000000000000"""Symmetry adapted basis sets of 1st order force constants.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.spg_reps import SpgRepsO1 from symfc.utils.eig_tools import eigsh_projector from symfc.utils.translation_tools_O1 import compressed_projector_sum_rules from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O1 import ( get_compr_coset_reps_sum, get_lat_trans_compr_matrix, get_lat_trans_decompr_indices, ) from .basis_sets_base import FCBasisSetBase class FCBasisSetO1(FCBasisSetBase): """Dense symmetry adapted basis set for 1st order force constants. Attributes ---------- basis_set : ndarray Compressed force constants basis set. The first dimension n_x (< n_a) is given as a result of compression, which is depends on the system. shape=(n_x, n_bases), dtype='double' full_basis_set : ndarray Full (decompressed) force constants basis set. shape=(N * 3, n_bases), dtype='double' translation_permutations : ndarray Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms), dtype=int. """ def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like log_level : int, optional Log level. Default is 0. """ super().__init__(supercell, use_mkl=use_mkl, log_level=log_level) self._spg_reps = SpgRepsO1( supercell, spacegroup_operations=spacegroup_operations ) self._n_a_compression_matrix: Optional[csr_array] = None self._basis_set: Optional[np.ndarray] = None # Unused in O1, just dummy variable to satisfy the base class self._atomic_decompr_idx: np.ndarray @property def full_basis_set(self) -> Optional[csr_array]: """Return full (decompressed) basis set. shape=(N*3, n_bases), dtype='double'. Data in first dimension is ordered by (N,3). """ return self._full_basis_set @property def compact_compression_matrix(self) -> Optional[np.ndarray]: """Return compression matrix for compact basis set.""" pass @property def compression_matrix(self) -> Optional[np.ndarray]: """Return compression matrix.""" pass @property def atomic_decompr_idx(self) -> np.ndarray: """Return atomic permutations by lattice translations.""" raise NotImplementedError( "Atomic decompression indices are not defined for O1 basis sets." ) def run(self) -> FCBasisSetO1: """Compute compressed force constants basis set.""" c_trans = self._get_c_trans() coset_reps_sum = get_compr_coset_reps_sum(self._spg_reps) # type: ignore proj_rt = coset_reps_sum if len(proj_rt.data) == 0: raise ValueError("No basis vectors exist.") c_rt = eigsh_projector(proj_rt, verbose=self._log_level > 0) compress_mat = c_trans @ c_rt proj = compressed_projector_sum_rules(compress_mat, self._natom) basis_set = eigsh_projector(proj, verbose=self._log_level > 0) self._full_basis_set = compress_mat @ basis_set self._basis_set = basis_set.toarray() return self def _get_c_trans(self) -> csr_array: trans_perms = self._spg_reps.translation_permutations n_lp, N = trans_perms.shape decompr_idx = get_lat_trans_decompr_indices(trans_perms) c_trans = get_lat_trans_compr_matrix(decompr_idx, N, n_lp) return c_trans symfc-1.6.0/src/symfc/basis_sets/basis_sets_O2.py000066400000000000000000000151021511276756400217740ustar00rootroot00000000000000"""Symmetry adapted basis sets of 2nd order force constants.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.spg_reps import SpgRepsO2 from symfc.utils.eig_tools import ( eigsh_projector, eigsh_projector_sumrule, ) from symfc.utils.matrix import BlockMatrixNode try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from symfc.utils.permutation_tools_O2 import compr_permutation_lat_trans_O2 from symfc.utils.rotation_tools_O2 import complementary_compr_projector_rot_sum_rules_O2 from symfc.utils.translation_tools_O2 import compressed_projector_sum_rules_O2 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( _get_atomic_lat_trans_decompr_indices, get_compr_coset_projector_O2, get_lat_trans_compr_matrix_O2, ) from .basis_sets_base import FCBasisSetBase class FCBasisSetO2(FCBasisSetBase): r"""Symmetry adapted basis set for 2nd order force constants. Attributes ---------- basis_set : ndarray Compressed force constants basis set. The first dimension n_compr (<< 9 * N ** 2, \sim n_bases) is given as a result of compression, which depends on the system. shape=(n_compr, n_bases), dtype='double' n_a_compression_matrix : csr_array Compression matrix compressed by lattice translation. The basis set compressed only by lattice translation is obtained by n_a_compression_matrix @ basis_set. shape=(n_a * N * 9, n_compr), dtype='double' translation_permutations : ndarray Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms), dtype=int. """ def __init__( self, supercell: SymfcAtoms, cutoff: Optional[float] = None, spacegroup_operations: Optional[dict] = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. cutoff: float Cutoff distance in angstroms. Default is None. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like use_mkl : bool Use MKL or not. Default is False. log_level : int, optional Log level. Default is 0. """ super().__init__(supercell, cutoff=cutoff, use_mkl=use_mkl, log_level=log_level) self._spg_reps = SpgRepsO2( supercell, spacegroup_operations=spacegroup_operations ) trans_perms = self._spg_reps.translation_permutations self._atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) self._n_a_compression_matrix: Optional[csr_array] = None self._basis_set: Optional[np.ndarray] = None self._blocked_basis_set: Optional[BlockMatrixNode] = None @property def compression_matrix(self) -> Optional[csr_array]: """Return compression matrix. This expands fc basis_sets to (N*N*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed. Call run() method to compute it." ) trans_perms = self._spg_reps.translation_permutations c_trans = get_lat_trans_compr_matrix_O2(trans_perms) return dot_product_sparse( c_trans, self._n_a_compression_matrix, use_mkl=self._use_mkl ) @property def compact_compression_matrix(self) -> Optional[csr_array]: """Return compact compression matrix. This expands fc basis_sets to (n_a*N*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed. Call run() method to compute it." ) n_lp = self.translation_permutations.shape[0] return self._n_a_compression_matrix / np.sqrt(n_lp) def run(self, rotational_sum_rules: bool = False) -> FCBasisSetO2: """Compute compressed force constants basis set.""" trans_perms = self._spg_reps.translation_permutations c_pt = compr_permutation_lat_trans_O2( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=self._log_level > 0, ) proj_rpt = get_compr_coset_projector_O2( self._spg_reps, # type: ignore fc_cutoff=self._fc_cutoff, atomic_decompr_idx=self._atomic_decompr_idx, c_pt=c_pt, # verbose=self._log_level > 0, ) c_rpt = eigsh_projector(proj_rpt, verbose=self._log_level > 0) n_a_compress_mat = dot_product_sparse(c_pt, c_rpt, use_mkl=self._use_mkl) proj = compressed_projector_sum_rules_O2( trans_perms, n_a_compress_mat, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, use_mkl=self._use_mkl, # verbose=self._log_level > 0, ) if rotational_sum_rules: proj_rot_cmplt = complementary_compr_projector_rot_sum_rules_O2( self._supercell, trans_perms, n_a_compress_mat, use_mkl=self._use_mkl, ) proj -= proj_rot_cmplt eigvecs = eigsh_projector_sumrule( proj, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) self._blocked_basis_set = eigvecs self._n_a_compression_matrix = n_a_compress_mat return self def estimate_basis_size(self) -> int: """Estimate basis set size.""" if self._fc_cutoff is None: n_sym, N = self._spg_reps._permutations.shape basis_size_estimates = 9 * (N**2) / n_sym / 2 return int(np.round(basis_size_estimates).astype(int)) trans_perms = self._spg_reps.translation_permutations c_pt = compr_permutation_lat_trans_O2( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=False, ) n_sym_prim = len(self._spg_reps._unique_rotations) basis_size_estimates = c_pt.shape[1] / n_sym_prim # type: ignore return int(np.round(basis_size_estimates).astype(int)) symfc-1.6.0/src/symfc/basis_sets/basis_sets_O3.py000066400000000000000000000200471511276756400220010ustar00rootroot00000000000000"""Symmetry adapted basis sets of 3rd order force constants.""" from __future__ import annotations import time from typing import Optional, Union import numpy as np from scipy.sparse import coo_array, csr_array from symfc.spg_reps import SpgRepsO3 from symfc.utils.eig_tools import ( eigsh_projector, eigsh_projector_sumrule, ) from symfc.utils.matrix import BlockMatrixNode try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from symfc.utils.permutation_tools_O3 import compr_permutation_lat_trans_O3 from symfc.utils.translation_tools_O3 import compressed_projector_sum_rules_O3 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O3 import ( get_atomic_lat_trans_decompr_indices_O3, get_compr_coset_projector_O3, get_lat_trans_compr_matrix_O3, ) from . import FCBasisSetBase def print_sp_matrix_size(c: Union[csr_array, coo_array], header: str): """Show sparse matrix size.""" print(header, c.shape, len(c.data), flush=True) class FCBasisSetO3(FCBasisSetBase): r"""Symmetry adapted basis set for 3rd order force constants. Attributes ---------- basis_set : ndarray Compressed force constants basis set. The first dimension n_compr (<< 27 * N ** 3, \sim n_bases) is given as a result of compression, which depends on the system. shape=(n_compr, n_bases), dtype='double' n_a_compression_matrix : csr_array Compression matrix compressed by lattice translation. The basis set compressed only by lattice translation is obtained by n_a_compression_matrix @ basis_set. shape=(n_a * N * N * 27, n_compr), dtype='double' translation_permutations : ndarray Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms), dtype=int. """ def __init__( self, supercell: SymfcAtoms, cutoff: Optional[float] = None, spacegroup_operations: Optional[dict] = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. cutoff: float Cutoff distance in angstroms. Default is None. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like use_mkl : bool Use MKL or not. Default is False. log_level : int, optional Log level. Default is 0. """ super().__init__(supercell, cutoff=cutoff, use_mkl=use_mkl, log_level=log_level) self._spg_reps = SpgRepsO3( supercell, spacegroup_operations=spacegroup_operations ) trans_perms = self._spg_reps.translation_permutations self._atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) self._n_a_compression_matrix: Optional[csr_array] = None self._basis_set: Optional[np.ndarray] = None self._blocked_basis_set: Optional[BlockMatrixNode] = None @property def compression_matrix(self) -> Optional[csr_array]: """Return compression matrix. This expands fc basis_sets to (N*N*N*3*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed yet. " "Call run() method to compute it." ) trans_perms = self._spg_reps.translation_permutations c_trans = get_lat_trans_compr_matrix_O3(trans_perms) return dot_product_sparse( c_trans, self._n_a_compression_matrix, use_mkl=self._use_mkl ) @property def compact_compression_matrix(self) -> Optional[csr_array]: """Return compact compression matrix. This expands fc basis_sets to (n_a*N*N*3*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed yet. " "Call run() method to compute it." ) n_lp = self.translation_permutations.shape[0] return self._n_a_compression_matrix / np.sqrt(n_lp) def run(self) -> FCBasisSetO3: """Compute compressed force constants basis set.""" trans_perms = self._spg_reps.translation_permutations tt0 = time.time() c_pt = compr_permutation_lat_trans_O3( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=self._log_level > 0, ) if self._log_level: print(" c_pt (size) :", c_pt.shape, flush=True) tt2 = time.time() proj_rpt = get_compr_coset_projector_O3( self._spg_reps, # type: ignore fc_cutoff=self._fc_cutoff, atomic_decompr_idx=self._atomic_decompr_idx, c_pt=c_pt, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) tt3 = time.time() c_rpt = eigsh_projector(proj_rpt, verbose=self._log_level > 0) if self._log_level: print(" c_rpt (size) :", c_rpt.shape, flush=True) tt4 = time.time() n_a_compress_mat = dot_product_sparse(c_pt, c_rpt, use_mkl=self._use_mkl) tt5 = time.time() proj = compressed_projector_sum_rules_O3( trans_perms, n_a_compress_mat, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) tt6 = time.time() eigvecs = eigsh_projector_sumrule( proj, verbose=self._log_level > 0, use_mkl=self._use_mkl, ) if self._log_level: print("Final size of basis set:", eigvecs.shape, flush=True) tt7 = time.time() if self._log_level: print( "Time (perm @ ltrans) :", "{:.3f}".format(tt2 - tt0), flush=True, ) print( "Time (coset) :", "{:.3f}".format(tt3 - tt2), flush=True, ) print( "Time (eigh(coset @ perm @ ltrans)) :", "{:.3f}".format(tt4 - tt3), flush=True, ) print( "Time (c_pt @ c_rpt) :", "{:.3f}".format(tt5 - tt4), flush=True, ) print( "Time (proj(sum)) :", "{:.3f}".format(tt6 - tt5), flush=True, ) print( "Time (eigh(sum)) :", "{:.3f}".format(tt7 - tt6), flush=True, ) print("---", flush=True) print( "Time (Basis FC3) :", "{:.3f}".format(tt7 - tt0), flush=True, ) self._blocked_basis_set = eigvecs self._n_a_compression_matrix = n_a_compress_mat return self def estimate_basis_size(self) -> int: """Estimate basis set size.""" if self._fc_cutoff is None: n_sym, N = self._spg_reps._permutations.shape basis_size_estimates = 27 * (N**3) / n_sym / 6 return int(np.round(basis_size_estimates).astype(int)) trans_perms = self._spg_reps.translation_permutations c_pt = compr_permutation_lat_trans_O3( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=False, ) n_sym_prim = len(self._spg_reps._unique_rotations) basis_size_estimates = c_pt.shape[1] / n_sym_prim # type: ignore return int(np.round(basis_size_estimates).astype(int)) symfc-1.6.0/src/symfc/basis_sets/basis_sets_O4.py000066400000000000000000000175651511276756400220150ustar00rootroot00000000000000"""Symmetry adapted basis sets of 4th order force constants.""" from __future__ import annotations import time from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.spg_reps import SpgRepsO4 from symfc.utils.eig_tools import ( eigsh_projector, eigsh_projector_sumrule, ) from symfc.utils.matrix import BlockMatrixNode try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from symfc.utils.permutation_tools_O4 import compr_permutation_lat_trans_O4 from symfc.utils.translation_tools_O4 import compressed_projector_sum_rules_O4 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O4 import ( get_atomic_lat_trans_decompr_indices_O4, get_compr_coset_projector_O4, get_lat_trans_compr_matrix_O4, ) from . import FCBasisSetBase class FCBasisSetO4(FCBasisSetBase): r"""Symmetry adapted basis set for 4th order force constants. Attributes ---------- basis_set : ndarray Compressed force constants basis set. The first dimension n_compr (<< 81 * N ** 4, \sim n_bases) is given as a result of compression, which depends on the system. shape=(n_compr, n_bases), dtype='double' n_a_compression_matrix : csr_array Compression matrix compressed by lattice translation. The basis set compressed only by lattice translation is obtained by n_a_compression_matrix @ basis_set. shape=(n_a * N * N * N * 81, n_compr), dtype='double' translation_permutations : ndarray Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms), dtype=int. """ def __init__( self, supercell: SymfcAtoms, cutoff: Optional[float] = None, spacegroup_operations: Optional[dict] = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. cutoff: float Cutoff distance in angstroms. Default is None. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like use_mkl : bool Use MKL or not. Default is False. log_level : int, optional Log level. Default is 0. """ super().__init__(supercell, cutoff=cutoff, use_mkl=use_mkl, log_level=log_level) self._spg_reps = SpgRepsO4( supercell, spacegroup_operations=spacegroup_operations ) trans_perms = self._spg_reps.translation_permutations self._atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) self._n_a_compression_matrix: Optional[csr_array] = None self._basis_set: Optional[np.ndarray] = None self._blocked_basis_set: Optional[BlockMatrixNode] = None @property def compression_matrix(self) -> Optional[csr_array]: """Return compression matrix. This expands fc basis_sets to (N*N*N*N*3*3*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed yet. " "Call run() method to compute it." ) trans_perms = self._spg_reps.translation_permutations c_trans = get_lat_trans_compr_matrix_O4(trans_perms) return dot_product_sparse( c_trans, self._n_a_compression_matrix, use_mkl=self._use_mkl ) @property def compact_compression_matrix(self) -> Optional[csr_array]: """Return compact compression matrix. This expands fc basis_sets to (n_a*N*N*N*3*3*3*3, n_bases). """ if self._n_a_compression_matrix is None: raise ValueError( "Compression matrix is not computed yet. " "Call run() method to compute it." ) n_lp = self.translation_permutations.shape[0] return self._n_a_compression_matrix / np.sqrt(n_lp) def run(self) -> FCBasisSetO4: """Compute compressed force constants basis set.""" trans_perms = self._spg_reps.translation_permutations tt0 = time.time() c_pt = compr_permutation_lat_trans_O4( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=self._log_level > 0, ) if self._log_level: print(" c_pt (size) :", c_pt.shape, flush=True) tt2 = time.time() proj_rpt = get_compr_coset_projector_O4( self._spg_reps, # type: ignore fc_cutoff=self._fc_cutoff, atomic_decompr_idx=self._atomic_decompr_idx, c_pt=c_pt, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) tt3 = time.time() c_rpt = eigsh_projector(proj_rpt, verbose=self._log_level > 0) if self._log_level: print(" c_rpt (size) :", c_rpt.shape, flush=True) tt4 = time.time() n_a_compress_mat = dot_product_sparse(c_pt, c_rpt, use_mkl=self._use_mkl) tt5 = time.time() proj = compressed_projector_sum_rules_O4( trans_perms, n_a_compress_mat, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) tt6 = time.time() eigvecs = eigsh_projector_sumrule( proj, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) if self._log_level: print("Final size of basis set:", eigvecs.shape, flush=True) tt7 = time.time() if self._log_level: print( "Time (perm @ ltrans) :", "{:.3f}".format(tt2 - tt0), flush=True, ) print( "Time (coset) :", "{:.3f}".format(tt3 - tt2), flush=True, ) print( "Time (eigh(coset @ perm @ ltrans)) :", "{:.3f}".format(tt4 - tt3), flush=True, ) print( "Time (c_pt @ c_rpt) :", "{:.3f}".format(tt5 - tt4), flush=True, ) print( "Time (proj(sum)) :", "{:.3f}".format(tt6 - tt5), flush=True, ) print( "Time (eigh(sum)) :", "{:.3f}".format(tt7 - tt6), flush=True, ) print("---") print( "Time (Basis FC4) :", "{:.3f}".format(tt7 - tt0), flush=True, ) self._blocked_basis_set = eigvecs self._n_a_compression_matrix = n_a_compress_mat return self def estimate_basis_size(self) -> int: """Estimate basis set size.""" if self._fc_cutoff is None: n_sym, N = self._spg_reps._permutations.shape basis_size_estimates = 81 * (N**4) / n_sym / 24 return int(np.round(basis_size_estimates).astype(int)) trans_perms = self._spg_reps.translation_permutations c_pt = compr_permutation_lat_trans_O4( trans_perms, atomic_decompr_idx=self._atomic_decompr_idx, fc_cutoff=self._fc_cutoff, verbose=False, ) n_sym_prim = len(self._spg_reps._unique_rotations) basis_size_estimates = c_pt.shape[1] / n_sym_prim # type: ignore return int(np.round(basis_size_estimates).astype(int)) symfc-1.6.0/src/symfc/basis_sets/basis_sets_base.py000066400000000000000000000061301511276756400224270ustar00rootroot00000000000000"""Symmetry adapted basis sets of force constants.""" from __future__ import annotations from abc import ABC, abstractmethod from typing import Optional import numpy as np from symfc.spg_reps import SpgRepsBase from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.matrix import BlockMatrixNode from symfc.utils.utils import SymfcAtoms class FCBasisSetBase(ABC): """Abstract base class of symmetry adapted basis set for force constants.""" def __init__( self, supercell: SymfcAtoms, cutoff: Optional[float] = None, use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. cutoff: float Cutoff distance in angstroms. Default is None. use_mkl : bool Use MKL or not. Default is False. log_level : int, optional Log level. Default is 0. """ self._supercell = supercell self._natom = len(supercell) self._use_mkl = use_mkl self._log_level = log_level self._spg_reps: SpgRepsBase self._atomic_decompr_idx: np.ndarray self._basis_set: np.ndarray self._blocked_basis_set: BlockMatrixNode if cutoff is None: self._fc_cutoff = None else: self._fc_cutoff = FCCutoff(supercell, cutoff=cutoff) @property @abstractmethod def compact_compression_matrix(self) -> Optional[np.ndarray]: """Return compression matrix for compact basis set.""" pass @property @abstractmethod def compression_matrix(self) -> Optional[np.ndarray]: """Return compression matrix.""" pass @property def basis_set(self) -> Optional[np.ndarray]: """Return compressed basis set. shape=(n_c, n_bases), dtype='double'. """ return self._blocked_basis_set.recover() @property def blocked_basis_set(self) -> Optional[BlockMatrixNode]: """Return compressed basis set in blocked format.""" return self._blocked_basis_set @property def atomic_decompr_idx(self) -> np.ndarray: """Return atomic permutations by lattice translations.""" return self._atomic_decompr_idx @property def translation_permutations(self) -> np.ndarray: """Return permutations by lattice translation.""" if self._spg_reps is None: raise ValueError("SpgRepsBase is not set.") return self._spg_reps.translation_permutations @property def p2s_map(self) -> np.ndarray: """Return indices of translationally independent atoms.""" if self._spg_reps is None: raise ValueError("SpgRepsBase is not set.") if self._spg_reps.p2s_map is None: raise ValueError("p2s_map is not set.") return self._spg_reps.p2s_map @property def fc_cutoff(self) -> Optional[FCCutoff]: """Return force constants cutoff.""" return self._fc_cutoff @abstractmethod def run(self): """Run basis set calculation.""" pass symfc-1.6.0/src/symfc/solvers/000077500000000000000000000000001511276756400162625ustar00rootroot00000000000000symfc-1.6.0/src/symfc/solvers/__init__.py000066400000000000000000000010001511276756400203620ustar00rootroot00000000000000"""Force constants solvers.""" from .solver_base import FCSolverBase from .solver_O2 import FCSolverO2 from .solver_O2O3 import FCSolverO2O3 from .solver_O2O3O4 import FCSolverO2O3O4 from .solver_O3 import FCSolverO3 from .solver_O3O4 import FCSolverO3O4 from .solver_O4 import FCSolverO4 from .sparse_solver_O2 import FCSparseSolverO2 __all__ = [ "FCSolverBase", "FCSolverO2", "FCSolverO2O3", "FCSolverO2O3O4", "FCSolverO3", "FCSolverO4", "FCSolverO3O4", "FCSparseSolverO2", ] symfc-1.6.0/src/symfc/solvers/solver_O2.py000066400000000000000000000200451511276756400205070ustar00rootroot00000000000000"""2nd order force constants solver.""" from __future__ import annotations import time from typing import Literal, Optional import numpy as np from scipy.sparse import csr_array from symfc.basis_sets import FCBasisSetO2 from symfc.utils.matrix import block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO2(FCSolverBase): """Third order force constants solver.""" def __init__( self, basis_set: FCBasisSetO2, use_mkl: bool = False, log_level: int = 0, ): """Init method.""" self._basis_set: FCBasisSetO2 super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 100, ) -> FCSolverO2: """Solve coefficients of basis set from displacements and forces. Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- self : FCSolverO2 """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc2_basis = self._basis_set compress_mat_fc2 = fc2_basis.compact_compression_matrix basis_set_fc2 = fc2_basis.blocked_basis_set atomic_decompr_idx_fc2 = fc2_basis.atomic_decompr_idx self._coefs = run_solver_O2( d, f, compress_mat_fc2, basis_set_fc2, atomic_decompr_idx_fc2, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(N, N, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(n_a, N, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[np.ndarray]: fc2_basis = self._basis_set if self._coefs is None or fc2_basis.basis_set is None: return None if comp_mat_type == "full": comp_mat_fc2 = fc2_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc2 = fc2_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc2 = fc2_basis.blocked_basis_set.dot(self._coefs) fc2 = np.array( (comp_mat_fc2 @ fc2).reshape((-1, N, 3, 3)), dtype="double", order="C" ) return fc2 def reshape_nN33_nx_to_N3_n3nx(mat, N: int, n: int, n_batch: int = 1) -> csr_array: """Reorder and reshape a sparse matrix (nN33,nx)->(N3,n3nx). mat : csr_array Return reordered csr_matrix used for FC2. """ _, nx = mat.shape N3 = N * 3 n3nx = n * 3 * nx mat = mat.tocoo(copy=False) begin_batch, end_batch = get_batch_slice(len(mat.row), len(mat.row) // n_batch) for begin, end in zip(begin_batch, end_batch): div, rem = np.divmod(mat.row[begin:end], 9 * N) mat.col[begin:end] += div * 3 * nx div, rem = np.divmod(rem, 9) mat.row[begin:end] = div * 3 div, rem = np.divmod(rem, 3) mat.col[begin:end] += div * nx mat.row[begin:end] += rem mat.resize((N3, n3nx)) mat = mat.tocsr(copy=False) return mat def prepare_normal_equation_O2( disps: np.ndarray, forces: np.ndarray, compact_compress_mat_fc2, compress_eigvecs_fc2, atomic_decompr_idx_fc2: np.ndarray, batch_size: int = 100, use_sparse_disps: bool = False, use_mkl: bool = False, verbose: bool = False, ): r"""Calculate X.T @ X and X.T @ y. X = displacements @ compress_mat @ compress_eigvecs displacements (fc2): (n_samples, N3) compact_compress_mat_fc2: (n_aN33, n_compr) compress_eigvecs_fc2: (n_compr_fc2, n_basis_fc2) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 n_compr_fc2 = compact_compress_mat_fc2.shape[1] n_batch = 1 begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat22 = np.zeros((n_compr_fc2, n_compr_fc2), dtype=float) mat2y = np.zeros(n_compr_fc2, dtype=float) t_all1 = time.time() const_fc2 = -1.0 compact_compress_mat_fc2 *= const_fc2 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc2[begin_i * N : end_i * N, None] * 9 + np.arange(9)[None, :] ).reshape(-1) compr_mat_fc2 = reshape_nN33_nx_to_N3_n3nx( compact_compress_mat_fc2[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() X2 = dot_product_sparse( disps[begin:end], compr_mat_fc2, use_mkl=use_mkl, dense=not use_sparse_disps, ).reshape((-1, n_compr_fc2)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat22 += X2.T @ X2 mat2y += X2.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) del X2 if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) XTX = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc2, mat22) XTy = compress_eigvecs_fc2.transpose_dot(mat2y) compact_compress_mat_fc2 /= const_fc2 t_all2 = time.time() if verbose: print( " (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O2( disps: np.ndarray, forces: np.ndarray, compact_compress_mat_fc2, compress_eigvecs_fc2, atomic_decompr_idx_fc2: np.ndarray, batch_size: int = 100, use_sparse_disps: bool = False, use_mkl: bool = False, verbose: bool = False, ): """Estimate coeffs. in X @ coeffs = y. X = displacements_fc2 @ compress_mat_fc2 @ compress_eigvecs_fc2 Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc2) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O2( disps, forces, compact_compress_mat_fc2, compress_eigvecs_fc2, atomic_decompr_idx_fc2, batch_size=batch_size, use_sparse_disps=use_sparse_disps, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) return coefs symfc-1.6.0/src/symfc/solvers/solver_O2O3.py000066400000000000000000000322051511276756400207120ustar00rootroot00000000000000"""Solver of 2nd and 3rd order force constants simultaneously.""" from __future__ import annotations import time from collections.abc import Sequence from typing import Literal, Optional, Union, cast import numpy as np from scipy.sparse import csr_array from symfc.basis_sets import FCBasisSetO2, FCBasisSetO3 from symfc.solvers.solver_O2 import reshape_nN33_nx_to_N3_n3nx from symfc.utils.matrix import BlockMatrixNode, block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO2O3(FCSolverBase): """Simultaneous second and third order force constants solver.""" def __init__( self, basis_set: Sequence[Union[FCBasisSetO2, FCBasisSetO3]], use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- basis_set : Sequence of (FCBasisSetO2, FCBasisSetO3) First element must be FCBasisSetO2 and second must be FCBasisSetO3. use_mkl : bool, optional Use MKL if True. Default is False. log_level : int, optional Logging level. Default is 0. """ if len(basis_set) != 2: raise ValueError("basis_set must contain exactly 2 elements") if not isinstance(basis_set[0], FCBasisSetO2): raise TypeError("First element must be FCBasisSetO2") if not isinstance(basis_set[1], FCBasisSetO3): raise TypeError("Second element must be FCBasisSetO3") self._basis_set: Sequence[Union[FCBasisSetO2, FCBasisSetO3]] super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 100, ) -> FCSolverO2O3: """Solve force constants. Note ---- self._coefs = (coefs_fc2, coefs_fc3) Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- ndarray Force constants. shape=(n_a, N, 3, 3) or (N, N, 3, 3). See `is_compact_fc` parameter. dtype='double', order='C' """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc2_basis: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[0]) fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[1]) compress_mat_fc2 = fc2_basis.compact_compression_matrix basis_set_fc2 = fc2_basis.blocked_basis_set compress_mat_fc3 = fc3_basis.compact_compression_matrix basis_set_fc3 = fc3_basis.blocked_basis_set atomic_decompr_idx_fc2 = fc2_basis.atomic_decompr_idx atomic_decompr_idx_fc3 = fc3_basis.atomic_decompr_idx if ( compress_mat_fc2 is None or compress_mat_fc3 is None or basis_set_fc2 is None or basis_set_fc3 is None ): raise ValueError( "Compression matrices or basis sets are not set. " "Call run() method to compute them." ) self._coefs = run_solver_O2O3( d, f, compress_mat_fc2, compress_mat_fc3, basis_set_fc2, basis_set_fc3, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[tuple[np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray] shape=(N, N, 3, 3), dtype='double', order='C' shape=(N, N, N, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[tuple[np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray] shape=(n_a, N, 3, 3), dtype='double', order='C' shape=(n_a, N, N, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"], ) -> Optional[tuple[np.ndarray, np.ndarray]]: if self._coefs is None: return None fc2_basis: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[0]) fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[1]) if comp_mat_type == "full": comp_mat_fc2 = fc2_basis.compression_matrix comp_mat_fc3 = fc3_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc2 = fc2_basis.compact_compression_matrix comp_mat_fc3 = fc3_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc2 = fc2_basis.blocked_basis_set.dot(self._coefs[0]) fc2 = np.array( (comp_mat_fc2 @ fc2).reshape((-1, N, 3, 3)), dtype="double", order="C" ) fc3 = fc3_basis.blocked_basis_set.dot(self._coefs[1]) fc3 = np.array( (comp_mat_fc3 @ fc3).reshape((-1, N, N, 3, 3, 3)), dtype="double", order="C", ) return fc2, fc3 def set_disps_N3N3(disps, sparse=False): """Calculate Kronecker products of displacements. Parameter --------- disps: shape=(n_supercell, N3) Return ------ disps_2nd: shape=(n_supercell, N3N3) """ n_supercell = disps.shape[0] disps_2nd = (disps[:, :, None] * disps[:, None, :]).reshape((n_supercell, -1)) if sparse: return csr_array(disps_2nd) return disps_2nd def reshape_nNN333_nx_to_N3N3_n3nx(mat, N, n, n_batch=9): """Reorder and reshape a sparse matrix (nNN333,nx)->(N3N3,n3nx). Return reordered csr_matrix used for FC3. """ _, nx = mat.shape NN33 = N**2 * 9 n3nx = n * 3 * nx mat = mat.tocoo(copy=False) batch_size = len(mat.row) if len(mat.row) < n_batch else len(mat.row) // n_batch begin_batch, end_batch = get_batch_slice(len(mat.row), batch_size) for begin, end in zip(begin_batch, end_batch): div, rem = np.divmod(mat.row[begin:end], 27 * N * N) mat.col[begin:end] += div * 3 * nx div, rem = np.divmod(rem, 27 * N) mat.row[begin:end] = div * 9 * N div, rem = np.divmod(rem, 27) mat.row[begin:end] += div * 3 div, rem = np.divmod(rem, 9) mat.col[begin:end] += div * nx div, rem = np.divmod(rem, 3) mat.row[begin:end] += div * 3 * N + rem mat.resize((NN33, n3nx)) mat = mat.tocsr(copy=False) return mat def prepare_normal_equation_O2O3( disps: np.ndarray, forces: np.ndarray, compact_compress_mat_fc2: csr_array, compact_compress_mat_fc3: csr_array, compress_eigvecs_fc2: BlockMatrixNode, compress_eigvecs_fc3: BlockMatrixNode, atomic_decompr_idx_fc2: np.ndarray, atomic_decompr_idx_fc3: np.ndarray, batch_size: int = 100, use_mkl: bool = False, verbose: bool = False, ): r"""Calculate X.T @ X and X.T @ y. X = displacements @ compress_mat @ compress_eigvecs X = np.hstack([X_fc2, X_fc3]) displacements (fc2): (n_samples, N3) displacements (fc3): (n_samples, NN33) compact_compress_mat_fc2: (n_aN33, n_compr) compact_compress_mat_fc3: (n_aNN333, n_compr_fc3) compress_eigvecs_fc2: (n_compr_fc2, n_basis_fc2) compress_eigvecs_fc3: (n_compr_fc3, n_basis_fc3) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 NN = N * N n_compr_fc2 = compact_compress_mat_fc2.shape[1] # type: ignore n_compr_fc3 = compact_compress_mat_fc3.shape[1] # type: ignore n_batch = (N // 256 + 1) * (n_compr_fc3 // 30000 + 1) n_batch = min(N, n_batch) begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat22 = np.zeros((n_compr_fc2, n_compr_fc2), dtype=float) mat23 = np.zeros((n_compr_fc2, n_compr_fc3), dtype=float) mat33 = np.zeros((n_compr_fc3, n_compr_fc3), dtype=float) mat2y = np.zeros(n_compr_fc2, dtype=float) mat3y = np.zeros(n_compr_fc3, dtype=float) t_all1 = time.time() const_fc2 = -1.0 const_fc3 = -0.5 compact_compress_mat_fc2 *= const_fc2 compact_compress_mat_fc3 *= const_fc3 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc2[begin_i * N : end_i * N, None] * 9 + np.arange(9)[None, :] ).reshape(-1) compr_mat_fc2 = reshape_nN33_nx_to_N3_n3nx( compact_compress_mat_fc2[decompr_idx], N, n_atom_batch, ) decompr_idx = ( atomic_decompr_idx_fc3[begin_i * NN : end_i * NN, None] * 27 + np.arange(27)[None, :] ).reshape(-1) compr_mat_fc3 = reshape_nNN333_nx_to_N3N3_n3nx( compact_compress_mat_fc3[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() X2 = dot_product_sparse( disps[begin:end], compr_mat_fc2, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc2)) X3 = dot_product_sparse( set_disps_N3N3(disps[begin:end], sparse=False), compr_mat_fc3, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc3)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat22 += X2.T @ X2 mat23 += X2.T @ X3 mat33 += X3.T @ X3 mat2y += X2.T @ y mat3y += X3.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) del X3 if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) mat22 = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc2, mat22) mat23 = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc3, mat23) mat33 = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc3, mat33) mat2y = compress_eigvecs_fc2.transpose_dot(mat2y) mat3y = compress_eigvecs_fc3.transpose_dot(mat3y) XTX = np.block([[mat22, mat23], [mat23.T, mat33]]) XTy = np.hstack([mat2y, mat3y]) compact_compress_mat_fc2 /= const_fc2 compact_compress_mat_fc3 /= const_fc3 t_all2 = time.time() if verbose: print( "Time (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O2O3( disps: np.ndarray, forces: np.ndarray, compact_compress_mat_fc2: csr_array, compact_compress_mat_fc3: csr_array, compress_eigvecs_fc2: BlockMatrixNode, compress_eigvecs_fc3: BlockMatrixNode, atomic_decompr_idx_fc2: np.ndarray, atomic_decompr_idx_fc3: np.ndarray, batch_size: int = 100, use_mkl: bool = False, verbose: bool = False, ): """Estimate coeffs. in X @ coeffs = y. X_fc2 = displacements_fc2 @ compress_mat_fc2 @ compress_eigvecs_fc2 X_fc3 = displacements_fc3 @ compress_mat_fc3 @ compress_eigvecs_fc3 X = np.hstack([X_fc2, X_fc3]) Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc2 + N_basis_fc3) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O2O3( disps, forces, compact_compress_mat_fc2, compact_compress_mat_fc3, compress_eigvecs_fc2, compress_eigvecs_fc3, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, batch_size=batch_size, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) n_basis_fc2 = compress_eigvecs_fc2.shape[1] coefs_fc2, coefs_fc3 = coefs[:n_basis_fc2], coefs[n_basis_fc2:] return coefs_fc2, coefs_fc3 symfc-1.6.0/src/symfc/solvers/solver_O2O3O4.py000066400000000000000000000406421511276756400211210ustar00rootroot00000000000000"""Solver of 2nd, 3rd and 4th order force constants simultaneously.""" from __future__ import annotations import time from collections.abc import Sequence from typing import Literal, Optional, Union, cast import numpy as np from scipy.sparse import csr_array from symfc.basis_sets import FCBasisSetO2, FCBasisSetO3, FCBasisSetO4 from symfc.solvers.solver_O2 import reshape_nN33_nx_to_N3_n3nx from symfc.solvers.solver_O2O3 import reshape_nNN333_nx_to_N3N3_n3nx, set_disps_N3N3 from symfc.utils.matrix import block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO2O3O4(FCSolverBase): """Simultaneous second, third and fourth order force constants solver.""" def __init__( self, basis_set: Sequence[Union[FCBasisSetO2, FCBasisSetO3, FCBasisSetO4]], use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- basis_set : Sequence of (FCBasisSetO2, FCBasisSetO3, FCBasisSetO4) First element must be FCBasisSetO2, second must be FCBasisSetO3, and third must be FCBasisSetO4. use_mkl : bool, optional Use MKL if True. Default is False. log_level : int, optional Logging level. Default is 0. """ if len(basis_set) != 3: raise ValueError("basis_set must contain exactly 3 elements") if not isinstance(basis_set[0], FCBasisSetO2): raise TypeError("First element must be FCBasisSetO2") if not isinstance(basis_set[1], FCBasisSetO3): raise TypeError("Second element must be FCBasisSetO3") if not isinstance(basis_set[2], FCBasisSetO4): raise TypeError("Third element must be FCBasisSetO4") self._basis_set: Sequence[Union[FCBasisSetO2, FCBasisSetO3, FCBasisSetO4]] super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 36, ) -> FCSolverO2O3O4: """Solve force constants. Note ---- self._coefs = (coefs_fc2, coefs_fc3, coefs_fc4) Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- ndarray Force constants. shape=(n_a, N, N, N, 3, 3, 3, 3). See `is_compact_fc` parameter. dtype='double', order='C' """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc2_basis: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[0]) fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[1]) fc4_basis: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[2]) compress_mat_fc2 = fc2_basis.compact_compression_matrix basis_set_fc2 = fc2_basis.blocked_basis_set compress_mat_fc3 = fc3_basis.compact_compression_matrix basis_set_fc3 = fc3_basis.blocked_basis_set compress_mat_fc4 = fc4_basis.compact_compression_matrix basis_set_fc4 = fc4_basis.blocked_basis_set atomic_decompr_idx_fc2 = fc2_basis.atomic_decompr_idx atomic_decompr_idx_fc3 = fc3_basis.atomic_decompr_idx atomic_decompr_idx_fc4 = fc4_basis.atomic_decompr_idx self._coefs = run_solver_O2O3O4( d, f, compress_mat_fc2, compress_mat_fc3, compress_mat_fc4, basis_set_fc2, basis_set_fc3, basis_set_fc4, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[tuple[np.ndarray, np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray, np.ndarray] shape=(N, N, 3, 3), dtype='double', order='C' shape=(N, N, N, 3, 3, 3), dtype='double', order='C' shape=(N, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[tuple[np.ndarray, np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray, np.ndarray] shape=(n_a, N, 3, 3), dtype='double', order='C' shape=(n_a, N, N, 3, 3, 3), dtype='double', order='C' shape=(n_a, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[tuple[np.ndarray, np.ndarray, np.ndarray]]: if self._coefs is None: return None fc2_basis: FCBasisSetO2 = cast(FCBasisSetO2, self._basis_set[0]) fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[1]) fc4_basis: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[2]) if comp_mat_type == "full": comp_mat_fc2 = fc2_basis.compression_matrix comp_mat_fc3 = fc3_basis.compression_matrix comp_mat_fc4 = fc4_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc2 = fc2_basis.compact_compression_matrix comp_mat_fc3 = fc3_basis.compact_compression_matrix comp_mat_fc4 = fc4_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc2 = fc2_basis.blocked_basis_set.dot(self._coefs[0]) fc2 = np.array( (comp_mat_fc2 @ fc2).reshape((-1, N, 3, 3)), dtype="double", order="C" ) fc3 = fc3_basis.blocked_basis_set.dot(self._coefs[1]) fc3 = np.array( (comp_mat_fc3 @ fc3).reshape((-1, N, N, 3, 3, 3)), dtype="double", order="C", ) fc4 = fc4_basis.blocked_basis_set.dot(self._coefs[2]) fc4 = np.array( (comp_mat_fc4 @ fc4).reshape((-1, N, N, N, 3, 3, 3, 3)), dtype="double", order="C", ) return fc2, fc3, fc4 def set_disps_N3N3N3(disps, sparse=True, disps_N3N3=None): """Calculate Kronecker products of displacements. Parameter --------- disps: shape=(n_supercell, N3) Return ------ disps_3rd: shape=(n_supercell, N3N3N3) """ n_supercell = disps.shape[0] if disps_N3N3 is not None: disps_3rd = (disps_N3N3[:, :, None] * disps[:, None, :]).reshape( (n_supercell, -1) ) else: disps_3rd = ( disps[:, :, None, None] * disps[:, None, :, None] * disps[:, None, None, :] ).reshape((n_supercell, -1)) if sparse: return csr_array(disps_3rd) return disps_3rd def reshape_nNNN3333_nx_to_N3N3N3_n3nx(mat, N, n, n_batch=36): """Reorder and reshape a sparse matrix (nNNN3333,nx)->(N3N3N3,n3nx). Return reordered csr_matrix used for FC4. """ _, nx = mat.shape NNN333 = N**3 * 27 n3nx = n * 3 * nx mat = mat.tocoo(copy=False) begin_batch, end_batch = get_batch_slice(len(mat.row), len(mat.row) // n_batch) for begin, end in zip(begin_batch, end_batch): div, rem = np.divmod(mat.row[begin:end], 81 * N * N * N) mat.col[begin:end] += div * 3 * nx div, rem = np.divmod(rem, 81 * N * N) mat.row[begin:end] = div * 27 * N * N div, rem = np.divmod(rem, 81 * N) mat.row[begin:end] += div * 9 * N div, rem = np.divmod(rem, 81) mat.row[begin:end] += div * 3 div, rem = np.divmod(rem, 27) mat.col[begin:end] += div * nx div, rem = np.divmod(rem, 9) mat.row[begin:end] += div * 9 * N * N div, rem = np.divmod(rem, 3) mat.row[begin:end] += div * 3 * N + rem mat.resize((NNN333, n3nx)) mat = mat.tocsr(copy=False) return mat def prepare_normal_equation_O2O3O4( disps, forces, compact_compress_mat_fc2, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc2, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): r"""Calculate X.T @ X and X.T @ y. X = displacements @ compress_mat @ compress_eigvecs X = np.hstack([X_fc2, X_fc3, X_fc4]) displacements (fc2): (n_samples, N3) displacements (fc3): (n_samples, NN33) displacements (fc4): (n_samples, NNN333) compact_compress_mat_fc2: (n_aN33, n_compr) compact_compress_mat_fc3: (n_aNN333, n_compr_fc3) compact_compress_mat_fc4: (n_aNNN3333, n_compr_fc4) compress_eigvecs_fc2: (n_compr_fc2, n_basis_fc2) compress_eigvecs_fc3: (n_compr_fc3, n_basis_fc3) compress_eigvecs_fc4: (n_compr_fc4, n_basis_fc4) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 NN = N**2 NNN = N**3 # n_basis_fc2 = compress_eigvecs_fc2.shape[1] # n_basis_fc3 = compress_eigvecs_fc3.shape[1] # n_basis_fc4 = compress_eigvecs_fc4.shape[1] n_compr_fc2 = compact_compress_mat_fc2.shape[1] n_compr_fc3 = compact_compress_mat_fc3.shape[1] n_compr_fc4 = compact_compress_mat_fc4.shape[1] n_batch = (n_compr_fc3 // 10000 + n_compr_fc4 // 5000 + 1) * (N // 50 + 1) n_batch = min(N, n_batch) begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat22 = np.zeros((n_compr_fc2, n_compr_fc2), dtype=float) mat23 = np.zeros((n_compr_fc2, n_compr_fc3), dtype=float) mat24 = np.zeros((n_compr_fc2, n_compr_fc4), dtype=float) mat33 = np.zeros((n_compr_fc3, n_compr_fc3), dtype=float) mat34 = np.zeros((n_compr_fc3, n_compr_fc4), dtype=float) mat44 = np.zeros((n_compr_fc4, n_compr_fc4), dtype=float) mat2y = np.zeros(n_compr_fc2, dtype=float) mat3y = np.zeros(n_compr_fc3, dtype=float) mat4y = np.zeros(n_compr_fc4, dtype=float) t_all1 = time.time() const_fc2 = -1.0 const_fc3 = -0.5 const_fc4 = -1.0 / 6.0 compact_compress_mat_fc2 *= const_fc2 compact_compress_mat_fc3 *= const_fc3 compact_compress_mat_fc4 *= const_fc4 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc2[begin_i * N : end_i * N, None] * 9 + np.arange(9)[None, :] ).reshape(-1) compr_mat_fc2 = reshape_nN33_nx_to_N3_n3nx( compact_compress_mat_fc2[decompr_idx], N, n_atom_batch, ) decompr_idx = ( atomic_decompr_idx_fc3[begin_i * NN : end_i * NN, None] * 27 + np.arange(27)[None, :] ).reshape(-1) compr_mat_fc3 = reshape_nNN333_nx_to_N3N3_n3nx( compact_compress_mat_fc3[decompr_idx], N, n_atom_batch, ) decompr_idx = ( atomic_decompr_idx_fc4[begin_i * NNN : end_i * NNN, None] * 81 + np.arange(81)[None, :] ).reshape(-1) compr_mat_fc4 = reshape_nNNN3333_nx_to_N3N3N3_n3nx( compact_compress_mat_fc4[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() X2 = dot_product_sparse( disps[begin:end], compr_mat_fc2, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc2)) disps_N3N3 = set_disps_N3N3(disps[begin:end], sparse=False) X3 = dot_product_sparse( disps_N3N3, compr_mat_fc3, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc3)) X4 = dot_product_sparse( set_disps_N3N3N3(disps[begin:end], sparse=False, disps_N3N3=disps_N3N3), compr_mat_fc4, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc4)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat22 += X2.T @ X2 mat23 += X2.T @ X3 mat24 += X2.T @ X4 mat33 += X3.T @ X3 mat34 += X3.T @ X4 mat44 += X4.T @ X4 mat2y += X2.T @ y mat3y += X3.T @ y mat4y += X4.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) mat22 = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc2, mat22) mat23 = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc3, mat23) mat24 = block_matrix_sandwich(compress_eigvecs_fc2, compress_eigvecs_fc4, mat24) mat33 = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc3, mat33) mat34 = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc4, mat34) mat44 = block_matrix_sandwich(compress_eigvecs_fc4, compress_eigvecs_fc4, mat44) mat2y = compress_eigvecs_fc2.transpose_dot(mat2y) mat3y = compress_eigvecs_fc3.transpose_dot(mat3y) mat4y = compress_eigvecs_fc4.transpose_dot(mat4y) XTX = np.block( [[mat22, mat23, mat24], [mat23.T, mat33, mat34], [mat24.T, mat34.T, mat44]] ) XTy = np.hstack([mat2y, mat3y, mat4y]) compact_compress_mat_fc2 /= const_fc2 compact_compress_mat_fc3 /= const_fc3 compact_compress_mat_fc4 /= const_fc4 t_all2 = time.time() if verbose: print( "Time (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O2O3O4( disps, forces, compact_compress_mat_fc2, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc2, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): """Estimate coeffs. in X @ coeffs = y. X_fc2 = displacements_fc2 @ compress_mat_fc2 @ compress_eigvecs_fc2 X_fc3 = displacements_fc3 @ compress_mat_fc3 @ compress_eigvecs_fc3 X_fc4 = displacements_fc4 @ compress_mat_fc4 @ compress_eigvecs_fc4 X = np.hstack([X_fc2, X_fc3, X_fc4]) Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc2 + N_basis_fc3 + N_basis_fc4) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O2O3O4( disps, forces, compact_compress_mat_fc2, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc2, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc2, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) n_basis_fc2 = compress_eigvecs_fc2.shape[1] n_basis_fc3 = compress_eigvecs_fc3.shape[1] coefs_fc2, coefs_fc3, coefs_fc4 = ( coefs[:n_basis_fc2], coefs[n_basis_fc2 : n_basis_fc2 + n_basis_fc3], coefs[n_basis_fc2 + n_basis_fc3 :], ) return coefs_fc2, coefs_fc3, coefs_fc4 symfc-1.6.0/src/symfc/solvers/solver_O3.py000066400000000000000000000163761511276756400205240ustar00rootroot00000000000000"""Solver of 3rd order force constants.""" from __future__ import annotations import time from typing import Literal, Optional import numpy as np from symfc.basis_sets import FCBasisSetO3 from symfc.solvers.solver_O2O3 import reshape_nNN333_nx_to_N3N3_n3nx, set_disps_N3N3 from symfc.utils.matrix import block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO3(FCSolverBase): """Third order force constants solver.""" def __init__( self, basis_set: FCBasisSetO3, use_mkl: bool = False, log_level: int = 0, ): """Init method.""" self._basis_set: FCBasisSetO3 super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 100, ) -> FCSolverO3: """Solve force constants. Note ---- self._coefs = (coefs_fc3) Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- self : FCSolverO3 """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc3_basis = self._basis_set compress_mat_fc3 = fc3_basis.compact_compression_matrix basis_set_fc3 = fc3_basis.blocked_basis_set atomic_decompr_idx_fc3 = fc3_basis.atomic_decompr_idx self._coefs = run_solver_O3( d, f, compress_mat_fc3, basis_set_fc3, atomic_decompr_idx_fc3, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(N, N, N, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(n_a, N, N, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[np.ndarray]: fc3_basis = self._basis_set if self._coefs is None or fc3_basis.basis_set is None: return None if comp_mat_type == "full": comp_mat_fc3 = fc3_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc3 = fc3_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc3 = fc3_basis.blocked_basis_set.dot(self._coefs) fc3 = np.array( (comp_mat_fc3 @ fc3).reshape((-1, N, N, 3, 3, 3)), dtype="double", order="C", ) return fc3 def prepare_normal_equation_O3( disps, forces, compact_compress_mat_fc3, compress_eigvecs_fc3, atomic_decompr_idx_fc3, batch_size=100, use_mkl=False, verbose=False, ): r"""Calculate X.T @ X and X.T @ y. X = (displacements x displacements) @ compress_mat @ compress_eigvecs displacements (fc3): (n_samples, NN33) compact_compress_mat_fc3: (n_aNN333, n_compr_fc3) compress_eigvecs_fc3: (n_compr_fc3, n_basis_fc3) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 NN = N * N n_compr_fc3 = compact_compress_mat_fc3.shape[1] n_batch = (N // 256 + 1) * (n_compr_fc3 // 30000 + 1) n_batch = min(N, n_batch) begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat33 = np.zeros((n_compr_fc3, n_compr_fc3), dtype=float) mat3y = np.zeros(n_compr_fc3, dtype=float) t_all1 = time.time() const_fc3 = -0.5 compact_compress_mat_fc3 *= const_fc3 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc3[begin_i * NN : end_i * NN, None] * 27 + np.arange(27)[None, :] ).reshape(-1) compr_mat_fc3 = reshape_nNN333_nx_to_N3N3_n3nx( compact_compress_mat_fc3[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() X3 = dot_product_sparse( set_disps_N3N3(disps[begin:end], sparse=False), compr_mat_fc3, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc3)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat33 += X3.T @ X3 mat3y += X3.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) XTX = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc3, mat33) XTy = compress_eigvecs_fc3.transpose_dot(mat3y) compact_compress_mat_fc3 /= const_fc3 t_all2 = time.time() if verbose: print( "Time (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O3( disps, forces, compact_compress_mat_fc3, compress_eigvecs_fc3, atomic_decompr_idx_fc3, batch_size=100, use_mkl=False, verbose=False, ): """Estimate coeffs. in X @ coeffs = y. X = displacements_fc3 @ compress_mat_fc3 @ compress_eigvecs_fc3 Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc3) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O3( disps, forces, compact_compress_mat_fc3, compress_eigvecs_fc3, atomic_decompr_idx_fc3, batch_size=batch_size, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) return coefs symfc-1.6.0/src/symfc/solvers/solver_O3O4.py000066400000000000000000000264331511276756400207220ustar00rootroot00000000000000"""Solver of 3rd and 4th order force constants simultaneously.""" from __future__ import annotations import time from collections.abc import Sequence from typing import Literal, Optional, Union, cast import numpy as np from symfc.basis_sets import FCBasisSetO3, FCBasisSetO4 from symfc.solvers.solver_O2O3 import reshape_nNN333_nx_to_N3N3_n3nx, set_disps_N3N3 from symfc.solvers.solver_O2O3O4 import ( reshape_nNNN3333_nx_to_N3N3N3_n3nx, set_disps_N3N3N3, ) from symfc.utils.matrix import block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO3O4(FCSolverBase): """Simultaneous third and fourth order force constants solver.""" def __init__( self, basis_set: Sequence[Union[FCBasisSetO3, FCBasisSetO4]], use_mkl: bool = False, log_level: int = 0, ): """Init method. Parameters ---------- basis_set : Sequence of (FCBasisSetO3, FCBasisSetO4) First element must be FCBasisSetO3 and second must be FCBasisSetO4. use_mkl : bool, optional Use MKL if True. Default is False. log_level : int, optional Logging level. Default is 0. """ if len(basis_set) != 2: raise ValueError("basis_set must contain exactly 2 elements") if not isinstance(basis_set[0], FCBasisSetO3): raise TypeError("First element must be FCBasisSetO3") if not isinstance(basis_set[1], FCBasisSetO4): raise TypeError("Second element must be FCBasisSetO4") self._basis_set: Sequence[Union[FCBasisSetO3, FCBasisSetO4]] super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 36, ) -> FCSolverO3O4: """Solve force constants. Note ---- self._coefs = (coefs_fc3, coefs_fc4) Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- self : FCSolverO3O4 """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[0]) fc4_basis: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[1]) compress_mat_fc3 = fc3_basis.compact_compression_matrix basis_set_fc3 = fc3_basis.blocked_basis_set compress_mat_fc4 = fc4_basis.compact_compression_matrix basis_set_fc4 = fc4_basis.blocked_basis_set atomic_decompr_idx_fc3 = fc3_basis.atomic_decompr_idx atomic_decompr_idx_fc4 = fc4_basis.atomic_decompr_idx self._coefs = run_solver_O3O4( d, f, compress_mat_fc3, compress_mat_fc4, basis_set_fc3, basis_set_fc4, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[tuple[np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray] shape=(N, N, N, 3, 3, 3), dtype='double', order='C' shape=(N, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[tuple[np.ndarray, np.ndarray]]: """Return full force constants. Returns ------- tuple[np.ndarray, np.ndarray] shape=(n_a, N, N, 3, 3, 3), dtype='double', order='C' shape=(n_a, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[tuple[np.ndarray, np.ndarray]]: if self._coefs is None: return None fc3_basis: FCBasisSetO3 = cast(FCBasisSetO3, self._basis_set[0]) fc4_basis: FCBasisSetO4 = cast(FCBasisSetO4, self._basis_set[1]) if comp_mat_type == "full": comp_mat_fc3 = fc3_basis.compression_matrix comp_mat_fc4 = fc4_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc3 = fc3_basis.compact_compression_matrix comp_mat_fc4 = fc4_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc3 = fc3_basis.blocked_basis_set.dot(self._coefs[0]) fc3 = np.array( (comp_mat_fc3 @ fc3).reshape((-1, N, N, 3, 3, 3)), dtype="double", order="C", ) fc4 = fc4_basis.blocked_basis_set.dot(self._coefs[1]) fc4 = np.array( (comp_mat_fc4 @ fc4).reshape((-1, N, N, N, 3, 3, 3, 3)), dtype="double", order="C", ) return fc3, fc4 def prepare_normal_equation_O3O4( disps, forces, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): r"""Calculate X.T @ X and X.T @ y. X = displacements @ compress_mat @ compress_eigvecs X = np.hstack([X_fc3, X_fc4]) displacements (fc3): (n_samples, NN33) displacements (fc4): (n_samples, NNN333) compact_compress_mat_fc3: (n_aNN333, n_compr_fc3) compact_compress_mat_fc4: (n_aNNN3333, n_compr_fc4) compress_eigvecs_fc3: (n_compr_fc3, n_basis_fc3) compress_eigvecs_fc4: (n_compr_fc4, n_basis_fc4) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 NN = N**2 NNN = N**3 n_compr_fc3 = compact_compress_mat_fc3.shape[1] n_compr_fc4 = compact_compress_mat_fc4.shape[1] n_batch = (n_compr_fc3 // 10000 + n_compr_fc4 // 5000 + 1) * (N // 50 + 1) n_batch = min(N, n_batch) begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat33 = np.zeros((n_compr_fc3, n_compr_fc3), dtype=float) mat34 = np.zeros((n_compr_fc3, n_compr_fc4), dtype=float) mat44 = np.zeros((n_compr_fc4, n_compr_fc4), dtype=float) mat3y = np.zeros(n_compr_fc3, dtype=float) mat4y = np.zeros(n_compr_fc4, dtype=float) t_all1 = time.time() const_fc3 = -0.5 const_fc4 = -1.0 / 6.0 compact_compress_mat_fc3 *= const_fc3 compact_compress_mat_fc4 *= const_fc4 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc3[begin_i * NN : end_i * NN, None] * 27 + np.arange(27)[None, :] ).reshape(-1) compr_mat_fc3 = reshape_nNN333_nx_to_N3N3_n3nx( compact_compress_mat_fc3[decompr_idx], N, n_atom_batch, ) decompr_idx = ( atomic_decompr_idx_fc4[begin_i * NNN : end_i * NNN, None] * 81 + np.arange(81)[None, :] ).reshape(-1) compr_mat_fc4 = reshape_nNNN3333_nx_to_N3N3N3_n3nx( compact_compress_mat_fc4[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() disps_N3N3 = set_disps_N3N3(disps[begin:end], sparse=False) X3 = dot_product_sparse( disps_N3N3, compr_mat_fc3, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc3)) X4 = dot_product_sparse( set_disps_N3N3N3(disps[begin:end], sparse=False, disps_N3N3=disps_N3N3), compr_mat_fc4, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc4)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat33 += X3.T @ X3 mat34 += X3.T @ X4 mat44 += X4.T @ X4 mat3y += X3.T @ y mat4y += X4.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) mat33 = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc3, mat33) mat34 = block_matrix_sandwich(compress_eigvecs_fc3, compress_eigvecs_fc4, mat34) mat44 = block_matrix_sandwich(compress_eigvecs_fc4, compress_eigvecs_fc4, mat44) mat3y = compress_eigvecs_fc3.transpose_dot(mat3y) mat4y = compress_eigvecs_fc4.transpose_dot(mat4y) XTX = np.block([[mat33, mat34], [mat34.T, mat44]]) XTy = np.hstack([mat3y, mat4y]) compact_compress_mat_fc3 /= const_fc3 compact_compress_mat_fc4 /= const_fc4 t_all2 = time.time() if verbose: print( "Time (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O3O4( disps, forces, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): """Estimate coeffs. in X @ coeffs = y. X_fc3 = displacements_fc3 @ compress_mat_fc3 @ compress_eigvecs_fc3 X_fc4 = displacements_fc4 @ compress_mat_fc4 @ compress_eigvecs_fc4 X = np.hstack([X_fc3, X_fc4]) Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc3 + N_basis_fc4) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O3O4( disps, forces, compact_compress_mat_fc3, compact_compress_mat_fc4, compress_eigvecs_fc3, compress_eigvecs_fc4, atomic_decompr_idx_fc3, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) n_basis_fc3 = compress_eigvecs_fc3.shape[1] coefs_fc3, coefs_fc4 = coefs[:n_basis_fc3], coefs[n_basis_fc3:] return coefs_fc3, coefs_fc4 symfc-1.6.0/src/symfc/solvers/solver_O4.py000066400000000000000000000164641511276756400205230ustar00rootroot00000000000000"""Solver of 4th order force constants.""" from __future__ import annotations import time from typing import Literal, Optional import numpy as np from symfc.basis_sets import FCBasisSetO4 from symfc.solvers.solver_O2O3O4 import ( reshape_nNNN3333_nx_to_N3N3N3_n3nx, set_disps_N3N3N3, ) from symfc.utils.matrix import block_matrix_sandwich from symfc.utils.solver_funcs import get_batch_slice, solve_linear_equation try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass from .solver_base import FCSolverBase class FCSolverO4(FCSolverBase): """Fourth order force constants solver.""" def __init__( self, basis_set: FCBasisSetO4, use_mkl: bool = False, log_level: int = 0, ): """Init method.""" self._basis_set: FCBasisSetO4 super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 36, ) -> FCSolverO4: """Solve force constants. Note ---- self._coefs = coefs_fc4 Parameters ---------- displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- self : FCSolverO4 """ n_data = forces.shape[0] f = forces.reshape(n_data, -1) d = displacements.reshape(n_data, -1) fc4_basis = self._basis_set compress_mat_fc4 = fc4_basis.compact_compression_matrix basis_set_fc4 = fc4_basis.blocked_basis_set atomic_decompr_idx_fc4 = fc4_basis.atomic_decompr_idx self._coefs = run_solver_O4( d, f, compress_mat_fc4, basis_set_fc4, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(N, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(n_a, N, N, N, 3, 3, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[np.ndarray]: fc4_basis: FCBasisSetO4 = self._basis_set if self._coefs is None or fc4_basis.basis_set is None: return None if comp_mat_type == "full": comp_mat_fc4 = fc4_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc4 = fc4_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc4 = fc4_basis.blocked_basis_set.dot(self._coefs) fc4 = np.array( (comp_mat_fc4 @ fc4).reshape((-1, N, N, N, 3, 3, 3, 3)), dtype="double", order="C", ) return fc4 def prepare_normal_equation_O4( disps, forces, compact_compress_mat_fc4, compress_eigvecs_fc4, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): r"""Calculate X.T @ X and X.T @ y. X = displacements (fc4) @ compress_mat @ compress_eigvecs displacements (fc4): (n_samples, NNN333) compact_compress_mat_fc4: (n_aNNN3333, n_compr_fc4) compress_eigvecs_fc4: (n_compr_fc4, n_basis_fc4) Matrix reshapings are appropriately applied to compress_mat and its products. X.T @ X and X.T @ y are sequentially calculated using divided dataset. X.T @ X = \sum_i X_i.T @ X_i X.T @ y = \sum_i X_i.T @ y_i (i: batch index) """ N3 = disps.shape[1] N = N3 // 3 NNN = N**3 n_compr_fc4 = compact_compress_mat_fc4.shape[1] n_batch = (n_compr_fc4 // 5000 + 1) * (N // 50 + 1) n_batch = min(N, n_batch) begin_batch_atom, end_batch_atom = get_batch_slice(N, N // n_batch) begin_batch, end_batch = get_batch_slice(disps.shape[0], batch_size) mat44 = np.zeros((n_compr_fc4, n_compr_fc4), dtype=float) mat4y = np.zeros(n_compr_fc4, dtype=float) t_all1 = time.time() const_fc4 = -1.0 / 6.0 compact_compress_mat_fc4 *= const_fc4 for begin_i, end_i in zip(begin_batch_atom, end_batch_atom): if verbose: print("-----", flush=True) print("Solver_atoms:", begin_i + 1, "--", end_i, "/", N, flush=True) n_atom_batch = end_i - begin_i t1 = time.time() decompr_idx = ( atomic_decompr_idx_fc4[begin_i * NNN : end_i * NNN, None] * 81 + np.arange(81)[None, :] ).reshape(-1) compr_mat_fc4 = reshape_nNNN3333_nx_to_N3N3N3_n3nx( compact_compress_mat_fc4[decompr_idx], N, n_atom_batch, ) t2 = time.time() if verbose: print( "Time (Solver_compr_matrix_reshape):", "{:.3f}".format(t2 - t1), flush=True, ) for begin, end in zip(begin_batch, end_batch): t1 = time.time() X4 = dot_product_sparse( set_disps_N3N3N3(disps[begin:end], sparse=False), compr_mat_fc4, use_mkl=use_mkl, dense=True, ).reshape((-1, n_compr_fc4)) y = forces[begin:end, begin_i * 3 : end_i * 3].reshape(-1) mat44 += X4.T @ X4 mat4y += X4.T @ y t2 = time.time() if verbose: print("Solver_block:", end, "/", disps.shape[0], flush=True) print(" - Time:", "{:.3f}".format(t2 - t1), flush=True) if verbose: print("Solver:", "Calculate X.T @ X and X.T @ y", flush=True) XTX = block_matrix_sandwich(compress_eigvecs_fc4, compress_eigvecs_fc4, mat44) XTy = compress_eigvecs_fc4.transpose_dot(mat4y) compact_compress_mat_fc4 /= const_fc4 t_all2 = time.time() if verbose: print( "Time (disp @ compr @ eigvecs).T @ (disp @ compr @ eigvecs):", "{:.3f}".format(t_all2 - t_all1), flush=True, ) return XTX, XTy def run_solver_O4( disps, forces, compact_compress_mat_fc4, compress_eigvecs_fc4, atomic_decompr_idx_fc4, batch_size=36, use_mkl=False, verbose=False, ): """Estimate coeffs. in X @ coeffs = y. X = displacements_fc4 @ compress_mat_fc4 @ compress_eigvecs_fc4 Matrix reshapings are appropriately applied. X: features (n_samples * N3, N_basis_fc4) y: observations (forces), (n_samples * N3) """ XTX, XTy = prepare_normal_equation_O4( disps, forces, compact_compress_mat_fc4, compress_eigvecs_fc4, atomic_decompr_idx_fc4, batch_size=batch_size, use_mkl=use_mkl, verbose=verbose, ) coefs = solve_linear_equation(XTX, XTy) return coefs symfc-1.6.0/src/symfc/solvers/solver_base.py000066400000000000000000000030661511276756400211450ustar00rootroot00000000000000"""Base class of force constants solvers.""" from __future__ import annotations from abc import ABC, abstractmethod from collections.abc import Sequence from typing import Optional, Union import numpy as np from symfc.basis_sets import FCBasisSetBase class FCSolverBase(ABC): """Abstract base class of force constants solvers.""" def __init__( self, basis_set: Union[FCBasisSetBase, Sequence[FCBasisSetBase]], use_mkl: bool = False, log_level: int = 0, ): """Init method.""" self._basis_set = basis_set self._use_mkl = use_mkl self._log_level = log_level if isinstance(self._basis_set, Sequence): _basis_set = self._basis_set[0] else: _basis_set = basis_set assert isinstance(_basis_set, FCBasisSetBase) self._natom = _basis_set.translation_permutations.shape[1] self._coefs: Optional[Union[np.ndarray, Sequence[np.ndarray]]] = None @property def coefs(self) -> Optional[Union[np.ndarray, Sequence[np.ndarray]]]: """Return coefficients of force constants with respect to basis set.""" return self._coefs @property @abstractmethod def full_fc(self) -> Optional[np.ndarray]: """Return full force constants.""" pass @property @abstractmethod def compact_fc(self) -> Optional[np.ndarray]: """Return compact force constants.""" pass @abstractmethod def solve(self): """Solve coefficients of basis set from displacements and forces.""" pass symfc-1.6.0/src/symfc/solvers/sparse_solver_O2.py000066400000000000000000000065421511276756400220720ustar00rootroot00000000000000"""2nd order force constants solver using finite displacements.""" from __future__ import annotations from typing import Literal, Optional import numpy as np from symfc.basis_sets import FCBasisSetO2 from symfc.utils.solver_funcs import get_displacement_sparse_matrix from .solver_base import FCSolverBase from .solver_O2 import run_solver_O2 class FCSparseSolverO2(FCSolverBase): """Third order force constants solver.""" def __init__( self, basis_set: FCBasisSetO2, use_mkl: bool = False, log_level: int = 0, ): """Init method.""" self._basis_set: FCBasisSetO2 super().__init__(basis_set, use_mkl=use_mkl, log_level=log_level) def solve( self, atoms: np.ndarray, displacements: np.ndarray, forces: np.ndarray, batch_size: int = 10000, ) -> FCSparseSolverO2: """Solve coefficients of basis set from displacements and forces. Parameters ---------- atoms : ndarray Indices of atoms displaced. shape=(n_snapshot), dtype='int' displacements : ndarray Displacements of atoms in Cartesian coordinates. shape=(n_snapshot, 3), dtype='double' forces : ndarray Forces of atoms in Cartesian coordinates. shape=(n_snapshot, N, 3), dtype='double' Returns ------- self : FCSparseSolverO2 """ n_data, n_atom, _ = forces.shape f = forces.reshape(n_data, -1) d = get_displacement_sparse_matrix(atoms, displacements, n_atom) fc2_basis = self._basis_set compress_mat_fc2 = fc2_basis.compact_compression_matrix basis_set_fc2 = fc2_basis.blocked_basis_set atomic_decompr_idx_fc2 = fc2_basis.atomic_decompr_idx self._coefs = run_solver_O2( d, f, compress_mat_fc2, basis_set_fc2, atomic_decompr_idx_fc2, batch_size=batch_size, use_sparse_disps=True, use_mkl=self._use_mkl, verbose=self._log_level > 0, ) return self @property def full_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(N, N, 3, 3), dtype='double', order='C' """ return self._recover_fcs("full") @property def compact_fc(self) -> Optional[np.ndarray]: """Return full force constants. Returns ------- np.ndarray shape=(n_a, N, 3, 3), dtype='double', order='C' """ return self._recover_fcs("compact") def _recover_fcs( self, comp_mat_type: Literal["full", "compact"] ) -> Optional[np.ndarray]: fc2_basis = self._basis_set if self._coefs is None or fc2_basis.basis_set is None: return None if comp_mat_type == "full": comp_mat_fc2 = fc2_basis.compression_matrix elif comp_mat_type == "compact": comp_mat_fc2 = fc2_basis.compact_compression_matrix else: raise ValueError("Invalid comp_mat_type.") N = self._natom fc2 = fc2_basis.blocked_basis_set.dot(self._coefs) fc2 = np.array( (comp_mat_fc2 @ fc2).reshape((-1, N, 3, 3)), dtype="double", order="C" ) return fc2 symfc-1.6.0/src/symfc/spg_reps/000077500000000000000000000000001511276756400164075ustar00rootroot00000000000000symfc-1.6.0/src/symfc/spg_reps/__init__.py000066400000000000000000000005371511276756400205250ustar00rootroot00000000000000"""Representations of space group for N-th order force constants.""" from .spg_reps_base import SpgRepsBase from .spg_reps_O1 import SpgRepsO1 from .spg_reps_O2 import SpgRepsO2 from .spg_reps_O3 import SpgRepsO3 from .spg_reps_O4 import SpgRepsO4 __all__ = [ "SpgRepsBase", "SpgRepsO1", "SpgRepsO2", "SpgRepsO3", "SpgRepsO4", ] symfc-1.6.0/src/symfc/spg_reps/spg_reps_O1.py000066400000000000000000000052071511276756400211460ustar00rootroot00000000000000"""O1 reps of space group ops with respect to atomic coordinate basis.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.utils.utils import SymfcAtoms from .spg_reps_base import SpgRepsBase class SpgRepsO1(SpgRepsBase): """Class of reps of space group operations for fc1.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._r1_reps: list[csr_array] self._col: np.ndarray self._data: np.ndarray super().__init__(supercell, spacegroup_operations=spacegroup_operations) @property def r_reps(self) -> list[csr_array]: """Return 1st rank tensor rotation matricies.""" return self._r1_reps def get_sigma1_rep(self, i: int) -> csr_array: """Compute and return i-th atomic permutation matrix. Parameters ---------- i : int Index of coset presentations of space group operations. """ data, row, col, shape = self._get_sigma1_rep_data(i) return csr_array((data, (row, col)), shape=shape) def _prepare(self, spacegroup_operations): super()._prepare(spacegroup_operations) N = len(self._numbers) self._col = np.arange(N, dtype=int) self._data = np.ones(N, dtype=int) self._compute_r1_reps() def _compute_r1_reps(self, tol: float = 1e-10): """Compute and return 1st rank tensor rotation matricies. This is equivalent to rotation matrix of atomic position in Cartesian coordinates. """ r1_reps = [] for r in self._unique_rotations: r1_rep: np.ndarray = self._lattice.T @ r @ np.linalg.inv(self._lattice.T) row, col = np.nonzero(np.abs(r1_rep) > tol) data = r1_rep[(row, col)] r1_reps.append(csr_array((data, (row, col)), shape=r1_rep.shape)) self._r1_reps = r1_reps def _get_sigma1_rep_data(self, i: int) -> tuple: uri = self._unique_rotation_indices permutation = self._permutations[uri[i]] N = len(self._numbers) row = permutation return self._data, row, self._col, (N, N) symfc-1.6.0/src/symfc/spg_reps/spg_reps_O2.py000066400000000000000000000133011511276756400211410ustar00rootroot00000000000000"""O2 reps of space group ops with respect to atomic coordinate basis.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.utils.utils import SymfcAtoms from .spg_reps_base import SpgRepsBase class SpgRepsO2(SpgRepsBase): """Class of reps of space group operations for fc2.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._r2_reps: list[csr_array] super().__init__(supercell, spacegroup_operations=spacegroup_operations) @property def r_reps(self) -> list[csr_array]: """Return 2nd rank tensor rotation matricies.""" return self._r2_reps def get_sigma2_rep(self, i: int, nonzero: Optional[np.ndarray] = None) -> csr_array: """Compute vector representation of i-th atomic pair permutation matrix. Parameters ---------- i : int Index of coset presentations of space group operations. """ return self._get_sigma2_rep_data(i, nonzero=nonzero) def _prepare(self, spacegroup_operations): super()._prepare(spacegroup_operations) N = len(self._numbers) self._atom_pairs = (np.mgrid[0:N, 0:N].reshape((2, -1)).T).astype( "uint16", copy=False ) self._coeff = np.array([N, 1], dtype=int) self._compute_r2_reps() def _compute_r2_reps(self, tol: float = 1e-10): """Compute and return 2nd rank tensor rotation matricies.""" r2_reps = [] for r in self._unique_rotations: r_c = self._lattice.T @ r @ np.linalg.inv(self._lattice.T) r2_rep = np.kron(r_c, r_c) row, col = np.nonzero(np.abs(r2_rep) > tol) data = r2_rep[(row, col)] r2_reps.append(csr_array((data, (row, col)), shape=r2_rep.shape)) self._r2_reps = r2_reps def _get_sigma2_rep_data( self, i: int, nonzero: Optional[np.ndarray] = None ) -> csr_array: """Compute vector representation of i-th atomic pair permutation matrix. Operation permutation[self._atom_pairs @ self._coeff is divided to reduce memory allocation. permutation_pairs represents row indices of nonzero elements in permutation matrix. Column indices of nonzero elements in permutation matrix are array indices of permutation_pairs. """ uri = self._unique_rotation_indices permutation = self._permutations[uri[i]] if nonzero is not None: pairs = self._atom_pairs[nonzero] else: pairs = self._atom_pairs permutation_pairs = permutation[pairs[:, 0]] * self._coeff[0] permutation_pairs += permutation[pairs[:, 1]] return permutation_pairs class SpgRepsO2MatrixReps(SpgRepsBase): """Class of reps of space group operations for fc2.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._r2_reps: list[csr_array] self._col: np.ndarray self._data: np.ndarray super().__init__(supercell, spacegroup_operations=spacegroup_operations) @property def r_reps(self) -> list[csr_array]: """Return 2nd rank tensor rotation matricies.""" return self._r2_reps def get_sigma2_rep(self, i: int) -> csr_array: """Compute and return i-th atomic pair permutation matrix. Parameters ---------- i : int Index of coset presentations of space group operations. """ data, row, col, shape = self._get_sigma2_rep_data(i) return csr_array((data, (row, col)), shape=shape) def _prepare(self, spacegroup_operations): super()._prepare(spacegroup_operations) N = len(self._numbers) a = np.arange(N) self._atom_pairs = np.stack(np.meshgrid(a, a), axis=-1).reshape(-1, 2) self._coeff = np.array([1, N], dtype=int) self._col = self._atom_pairs @ self._coeff self._data = np.ones(N * N, dtype=int) self._compute_r2_reps() def _compute_r2_reps(self, tol: float = 1e-10): """Compute and return 2nd rank tensor rotation matricies.""" r2_reps = [] for r in self._unique_rotations: r_c = self._lattice.T @ r @ np.linalg.inv(self._lattice.T) r2_rep = np.kron(r_c, r_c) row, col = np.nonzero(np.abs(r2_rep) > tol) data = r2_rep[(row, col)] r2_reps.append(csr_array((data, (row, col)), shape=r2_rep.shape)) self._r2_reps = r2_reps def _get_sigma2_rep_data(self, i: int) -> tuple: uri = self._unique_rotation_indices permutation = self._permutations[uri[i]] NN = len(self._numbers) ** 2 row = permutation[self._atom_pairs] @ self._coeff return self._data, row, self._col, (NN, NN) symfc-1.6.0/src/symfc/spg_reps/spg_reps_O3.py000066400000000000000000000066301511276756400211510ustar00rootroot00000000000000"""O3 reps of space group ops with respect to atomic coordinate basis.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms class SpgRepsO3(SpgRepsBase): """Class of reps of space group operations for fc3.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._r3_reps: list[csr_array] super().__init__(supercell, spacegroup_operations=spacegroup_operations) @property def r_reps(self) -> list[csr_array]: """Return 3rd rank tensor rotation matricies.""" return self._r3_reps def get_sigma3_rep( self, i: int, nonzero: Optional[np.ndarray] = None ) -> np.ndarray: """Compute vector representation of i-th atomic pair permutation matrix. Parameters ---------- i : int Index of coset presentations of space group operations. """ return self._get_sigma3_rep_data(i, nonzero=nonzero) def _prepare(self, spacegroup_operations): super()._prepare(spacegroup_operations) N = len(self._numbers) """Bottleneck part in memory allocation""" self._atom_triplets = (np.mgrid[0:N, 0:N, 0:N].reshape((3, -1)).T).astype( "uint16", copy=False ) self._coeff = np.array([N**2, N, 1], dtype=int) self._compute_r3_reps() def _compute_r3_reps(self, tol: float = 1e-10): """Compute and return 3rd rank tensor rotation matricies.""" r3_reps = [] for r in self._unique_rotations: r_c = self._lattice.T @ r @ np.linalg.inv(self._lattice.T) r3_rep = np.kron(r_c, np.kron(r_c, r_c)) row, col = np.nonzero(np.abs(r3_rep) > tol) data = r3_rep[(row, col)] r3_reps.append(csr_array((data, (row, col)), shape=r3_rep.shape)) self._r3_reps = r3_reps def _get_sigma3_rep_data( self, i: int, nonzero: Optional[np.ndarray] = None ) -> np.ndarray: """Compute vector representation of i-th atomic pair permutation matrix. Operation permutation[self._atom_triplets] @ self._coeff is divided to reduce memory allocation. permutation_triplets represents row indices of nonzero elements in permutation matrix. Column indices of nonzero elements in permutation matrix are array indices of permutation_triplets. """ uri = self._unique_rotation_indices permutation = self._permutations[uri[i]] if nonzero is not None: triplets = self._atom_triplets[nonzero] else: triplets = self._atom_triplets permutation_triplets = permutation[triplets[:, 0]] * self._coeff[0] permutation_triplets += permutation[triplets[:, 1]] * self._coeff[1] permutation_triplets += permutation[triplets[:, 2]] return permutation_triplets symfc-1.6.0/src/symfc/spg_reps/spg_reps_O4.py000066400000000000000000000070611511276756400211510ustar00rootroot00000000000000"""O4 reps of space group ops with respect to atomic coordinate basis.""" from __future__ import annotations from typing import Optional import numpy as np from scipy.sparse import csr_array from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms class SpgRepsO4(SpgRepsBase): """Class of reps of space group operations for fc4.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._r4_reps: list[csr_array] super().__init__(supercell, spacegroup_operations=spacegroup_operations) @property def r_reps(self) -> list[csr_array]: """Return 4th rank tensor rotation matricies.""" return self._r4_reps def get_sigma4_rep( self, i: int, nonzero: Optional[np.ndarray] = None ) -> np.ndarray: """Compute vector representation of i-th atomic pair permutation matrix. Parameters ---------- i : int Index of coset presentations of space group operations. """ return self._get_sigma4_rep_data(i, nonzero=nonzero) def _prepare(self, spacegroup_operations): super()._prepare(spacegroup_operations) N = len(self._numbers) """Bottleneck part in memory allocation""" self._atom_quadruplets = ( np.mgrid[0:N, 0:N, 0:N, 0:N].reshape((4, -1)).T ).astype("uint16", copy=False) self._coeff = np.array([N**3, N**2, N, 1], dtype=int) self._compute_r4_reps() def _compute_r4_reps(self, tol: float = 1e-10): """Compute and return 4th rank tensor rotation matricies.""" r4_reps = [] for r in self._unique_rotations: r_c = self._lattice.T @ r @ np.linalg.inv(self._lattice.T) r4_rep = np.kron(r_c, np.kron(r_c, np.kron(r_c, r_c))) row, col = np.nonzero(np.abs(r4_rep) > tol) data = r4_rep[(row, col)] r4_reps.append(csr_array((data, (row, col)), shape=r4_rep.shape)) self._r4_reps = r4_reps def _get_sigma4_rep_data( self, i: int, nonzero: Optional[np.ndarray] = None ) -> np.ndarray: """Compute vector representation of i-th atomic pair permutation matrix. Operation permutation[self._atom_quadruplets] @ self._coeff is divided to reduce memory allocation. permutation_quadruplets represents row indices of nonzero elements in permutation matrix. Column indices of nonzero elements in permutation matrix are array indices of permutation_quadruplets. """ uri = self._unique_rotation_indices permutation = self._permutations[uri[i]] if nonzero is not None: quadruplets = self._atom_quadruplets[nonzero] else: quadruplets = self._atom_quadruplets permutation_quadruplets = permutation[quadruplets[:, 0]] * self._coeff[0] permutation_quadruplets += permutation[quadruplets[:, 1]] * self._coeff[1] permutation_quadruplets += permutation[quadruplets[:, 2]] * self._coeff[2] permutation_quadruplets += permutation[quadruplets[:, 3]] return permutation_quadruplets symfc-1.6.0/src/symfc/spg_reps/spg_reps_base.py000066400000000000000000000114571511276756400216050ustar00rootroot00000000000000"""Reps base of space group ops with respect to atomic coordinate basis.""" from __future__ import annotations from typing import Optional import numpy as np from symfc.utils.utils import ( SymfcAtoms, compute_sg_permutations, get_indep_atoms_by_lat_trans, ) class SpgRepsBase: """Base class of reps of space group operations.""" def __init__( self, supercell: SymfcAtoms, spacegroup_operations: Optional[dict] = None ): """Init method. Parameters ---------- supercell : SymfcAtoms Supercell. spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used. The following keys and values correspond to spglib symmetry dataset: rotations : array_like translations : array_like """ self._lattice = np.array(supercell.cell, dtype="double", order="C") self._positions = np.array( supercell.scaled_positions, dtype="double", order="C" ) self._numbers = supercell.numbers self._unique_rotations: np.ndarray self._unique_rotation_indices: np.ndarray self._translation_permutations: np.ndarray self._p2s_map: np.ndarray self._prepare(spacegroup_operations) @property def translation_permutations(self) -> np.ndarray: """Return permutations by lattice translation. Returns ------- Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms), dtype=int """ return self._translation_permutations @property def unique_rotation_indices(self) -> np.ndarray: """Return indices of coset representatives of space group operations.""" return self._unique_rotation_indices @property def p2s_map(self) -> np.ndarray: """Return indices of translationally independent atoms.""" return self._p2s_map def _prepare(self, spacegroup_operations): rotations, translations = self._get_symops(spacegroup_operations) self._unique_rotation_indices, self._unique_rotations = ( self._get_unique_rotation_indices(rotations) ) self._permutations = compute_sg_permutations( self._positions, rotations, translations, self._lattice.T, 1e-5 ) self._translation_permutations = self._get_translation_permutations(rotations) self._p2s_map = get_indep_atoms_by_lat_trans(self._translation_permutations) def _get_translation_permutations(self, rotations) -> np.ndarray: eye3 = np.eye(3, dtype=int) trans_perms = [] for r, perm in zip(rotations, self._permutations): if np.array_equal(r, eye3): trans_perms.append(perm) return np.array(trans_perms, dtype="intc", order="C") def _get_unique_rotation_indices( self, rotations: np.ndarray ) -> tuple[np.ndarray, np.ndarray]: unique_rotations: list[np.ndarray] = [] indices = [] for i, r in enumerate(rotations): is_found = False for ur in unique_rotations: if np.array_equal(r, ur): is_found = True break if not is_found: unique_rotations.append(r) indices.append(i) return np.array(indices, dtype=int), np.array(unique_rotations, dtype=int) def _get_symops( self, spacegroup_operations: Optional[dict] = None ) -> tuple[np.ndarray, np.ndarray]: """Return symmetry operations. The set of inverse operations is the same as the set of the operations. Parameters ---------- spacegroup_operations : dict, optional Space group operations in supercell, by default None. When None, spglib is used to get the operations. Returns ------- rotations : array_like A set of rotation matrices of inverse space group operations. (n_symops, 3, 3), dtype='intc', order='C' translations : array_like A set of translation vectors. It is assumed that inverse matrices are included in this set. (n_symops, 3), dtype='double'. """ if spacegroup_operations is None: try: import spglib except ImportError as exc: raise ModuleNotFoundError( "Spglib python module was not found." ) from exc symops = spglib.get_symmetry( # type: ignore (self._lattice, self._positions, self._numbers) # type: ignore ) else: symops = spacegroup_operations return symops["rotations"], symops["translations"] # type: ignore symfc-1.6.0/src/symfc/utils/000077500000000000000000000000001511276756400157255ustar00rootroot00000000000000symfc-1.6.0/src/symfc/utils/cutoff_tools.py000066400000000000000000000215211511276756400210060ustar00rootroot00000000000000"""Utility functions for introducing zero force constants.""" import itertools from typing import Union import numpy as np from symfc.utils.utils import SymfcAtoms def apply_zeros(C, zero_ids): """Assign zero to matrix elements. Use this function, sparse C can become larger by assigning zeros. Zero elements should be applied to c_trans and c_perm in constructing them. Warning: Slow when zero_ids is large. Method 1 -------- C[zero_ids,:] = 0 Method 2 -------- C = C.tolil() C[zero_ids, :] = 0 C = C.tocsr() """ for i in zero_ids: nonzero_cols = C.getrow(i).nonzero()[1] for j in nonzero_cols: C[i, j] = 0 return C class FCCutoff: """Class for introducing cutoff radius.""" def __init__(self, supercell: SymfcAtoms, cutoff: float = 7.0): """Find nonzero FC elements given by cutoff distance. For FC3, zero elements are determined by the distance between three pairs of atoms. Parameters ---------- supercell: SymfcAtoms Supercell structure. cutoff: float Cutoff distance between two atoms. """ self._supercell = supercell self._cutoff = cutoff self._n_atom = supercell.scaled_positions.shape[0] self._distances = self._calc_distances() self._neighbors = None self._nonzero_fc2 = None self._nonzero_fc3 = None self._nonzero_fc4 = None @property def neighbors(self) -> list[np.ndarray]: """Neighbor atoms: shape=(n_atom, n_neighbors).""" if self._neighbors is None: self._neighbors = [ np.where(self._distances[i] < self._cutoff)[0] for i in range(self._n_atom) ] return self._neighbors @property def outsides(self) -> list[np.ndarray]: """Atoms outside cutoff radius: shape=(n_atom, n_neighbors).""" return [ np.where(self._distances[i] >= self._cutoff)[0] for i in range(self._n_atom) ] @property def distances(self) -> np.ndarray: """Minimum distances between atoms: shape=(n_atom, n_atom).""" return self._distances def combinations1(self) -> np.ndarray: """Return combinations with single index ia.""" return np.array([[i] for i in range(3 * self._n_atom)], dtype=int) def combinations2(self) -> np.ndarray: """Return combinations with two distinguished indices (ia,jb).""" combinations = [] for jb in range(3 * self._n_atom): j = jb // 3 combs = [ [3 * i + a, jb] for i in self.neighbors[j] for a in range(3) if 3 * i + a < jb ] combinations.extend(combs) combinations_fc2 = np.array(combinations) return combinations_fc2 def combinations3_all(self) -> np.ndarray: """Return combinations with three distinguished indices (ia,jb,kc).""" combinations = [] for kc in range(3 * self._n_atom): combs = self.combinations3(kc) combinations.extend(combs) combinations_fc3 = np.array(combinations) return combinations_fc3 def combinations3(self, kc) -> Union[list, np.ndarray]: """Return combinations with three distinguished indices (ia,jb,kc). Return only combinations with kc. """ k = kc // 3 neighbors_N3 = [ 3 * j + b for j in self.neighbors[k] for b in range(3) if 3 * j + b < kc ] combs = np.array(list(itertools.combinations(neighbors_N3, 2))) if len(combs) > 0: indices = np.where( self.distances[(combs[:, 0] // 3, combs[:, 1] // 3)] < self._cutoff )[0] combs = combs[indices] return np.hstack([combs, np.full((combs.shape[0], 1), kc)]) return [] def combinations4_all(self) -> np.ndarray: """Return combinations with three distinguished indices (ia,jb,kc,ld).""" combinations = [] for ld in range(3 * self._n_atom): combs = self.combinations4(ld) combinations.extend(combs) combinations_fc4 = np.array(combinations) return combinations_fc4 def combinations4(self, ld) -> Union[list, np.ndarray]: """Return combinations with three distinguished indices (ia,jb,kc,ld). Return only combinations with kc. """ ll = ld // 3 neighbors_N3 = [ 3 * j + b for j in self.neighbors[ll] for b in range(3) if 3 * j + b < ld ] combs = np.array(list(itertools.combinations(neighbors_N3, 3))) if len(combs) > 0: indices = np.where( (self.distances[(combs[:, 0] // 3, combs[:, 1] // 3)] < self._cutoff) & (self.distances[(combs[:, 0] // 3, combs[:, 2] // 3)] < self._cutoff) & (self.distances[(combs[:, 1] // 3, combs[:, 2] // 3)] < self._cutoff) )[0] combs = combs[indices] return np.hstack([combs, np.full((combs.shape[0], 1), ld)]) return [] def nonzero_atomic_indices_fc2(self) -> np.ndarray: """Return atomic indices of nonzero FC2. Returns ------- nonzero : np.ndarray FC2 element is nonzero (True) or zero (False), shape=(NN). """ if self._nonzero_fc2 is not None: return self._nonzero_fc2 self._nonzero_fc2 = nonzero = np.zeros(self._n_atom**2, dtype=bool) for i in range(self._n_atom): ids = np.array(self.neighbors[i]) + i * self._n_atom nonzero[ids] = True return self._nonzero_fc2 def nonzero_atomic_indices_fc3(self) -> np.ndarray: """Return atomic indices of nonzero FC3. Returns ------- nonzero : np.ndarray FC3 element is nonzero (True) or zero (False), shape=(NNN). """ if self._nonzero_fc3 is not None: return self._nonzero_fc3 self._nonzero_fc3 = nonzero = np.zeros(self._n_atom**3, dtype=bool) for i in range(self._n_atom): jlist = self.neighbors[i] combs = np.array(list(itertools.product(jlist, jlist))) if len(combs) > 0: combs = combs[self.distances[(combs[:, 0], combs[:, 1])] < self._cutoff] ids = combs @ np.array([self._n_atom, 1]) + i * self._n_atom**2 nonzero[ids] = True return self._nonzero_fc3 def nonzero_atomic_indices_fc4(self) -> np.ndarray: """Return atomic indices of nonzero FC4. Returns ------- nonzero : np.ndarray FC4 element is nonzero (True) or zero (False), shape=(NNNN). """ if self._nonzero_fc4 is not None: return self._nonzero_fc4 self._nonzero_fc4 = nonzero = np.zeros(self._n_atom**4, dtype=bool) for i in range(self._n_atom): jlist = self.neighbors[i] combs = np.array(list(itertools.product(*[jlist, jlist, jlist]))) if len(combs) > 0: combs = combs[ (self.distances[(combs[:, 0], combs[:, 1])] < self._cutoff) & (self.distances[(combs[:, 0], combs[:, 2])] < self._cutoff) & (self.distances[(combs[:, 1], combs[:, 2])] < self._cutoff) ] ids = combs @ np.array([self._n_atom**2, self._n_atom, 1]) ids += i * self._n_atom**3 nonzero[ids] = True return self._nonzero_fc4 def _calc_distances(self) -> np.ndarray: """Calculate minimum distances between atoms. This algorithm must be reconsidered. (Not applicable to structures with strange lattice shape) """ try: import spglib except ImportError as exc: raise ModuleNotFoundError("Spglib python module was not found.") from exc reduced_bases = spglib.niggli_reduce(self._supercell.cell) # type: ignore trans_mat_float = self._supercell.cell @ np.linalg.inv(reduced_bases) # type: ignore trans_mat = np.rint(trans_mat_float).astype(int) assert (np.abs(trans_mat_float - trans_mat) < 1e-8).all() scaled_positions = self._supercell.scaled_positions @ trans_mat scaled_positions -= np.rint(scaled_positions) diff = scaled_positions[:, None, :] - scaled_positions[None, :, :] trans = np.array(list(itertools.product(*[[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]))) norms = np.ones((self._n_atom, self._n_atom)) * 1e10 for t1 in trans: t1_tile = np.tile(t1, (self._n_atom, self._n_atom, 1)) norms_trial = np.linalg.norm((diff - t1_tile) @ reduced_bases, axis=2) match = norms_trial < norms norms[match] = norms_trial[match] self._distances = norms return self._distances symfc-1.6.0/src/symfc/utils/eig_tools.py000066400000000000000000000501461511276756400202710ustar00rootroot00000000000000"""Utility functions for eigenvalue solutions.""" from collections import defaultdict from dataclasses import dataclass from typing import Optional, Union import numpy as np import scipy from scipy.sparse import csr_array from symfc.utils.graph import connected_components from symfc.utils.matrix import ( BlockMatrixNode, append_node, matrix_rank, return_numpy_array, root_block_matrix, ) from symfc.utils.solver_funcs import get_batch_slice def eigh_projector( p: Union[np.ndarray, csr_array], atol: float = 1e-8, rtol: float = 0.0, return_cmplt: bool = False, return_block: bool = False, verbose: bool = True, ) -> Union[ np.ndarray, tuple[np.ndarray, tuple[np.ndarray, np.ndarray]], None, tuple[None, None], ]: """Solve eigenvalue problem using numpy and eliminate eigenvectors with e < 1.0.""" p = return_numpy_array(p) rank = matrix_rank(p) if rank == 0: if return_cmplt: return None, None return None if rank > 32767: raise RuntimeError("Projector rank is too large in eigh.") try: eigvals, eigvecs = np.linalg.eigh(p) except np.linalg.LinAlgError as e: if verbose: print(f"np.linalg.eigh failed: {str(e)}") print("Try scipy.linalg.lapack.dsyev") eigvals, eigvecs, info = scipy.linalg.lapack.dsyev(p.T) if info != 0: raise scipy.linalg.LinAlgError( "scipy.linalg.lapack.dsyev failed: Eigenvalues did not converge" ) from e tol = 1e-8 if np.count_nonzero((eigvals > 1.0 + tol) | (eigvals < -tol)): diff = np.abs(p - p.T) if np.any(diff > 1e-3): raise RuntimeError("Transpose equality not satisfied") elif np.any(diff > 1e-15): eigvals, eigvecs = np.linalg.eigh(0.5 * (p + p.T)) if np.count_nonzero((eigvals > 1.0 + tol) | (eigvals < -tol)): raise ValueError("Eigenvalue error: e > 1 or e < 0.") nonzero = np.isclose(eigvals, 1.0, atol=atol, rtol=rtol) if return_cmplt: cmplt_bool = np.logical_and(np.logical_not(nonzero), eigvals > 1e-12) cmplt_eigvals, cmplt_eigvecs = eigvals[cmplt_bool], eigvecs[:, cmplt_bool] eigvecs = eigvecs[:, nonzero] if return_block: block = root_block_matrix(data=eigvecs) if return_cmplt: return block, (cmplt_eigvals, cmplt_eigvecs) return block if return_cmplt: return eigvecs, (cmplt_eigvals, cmplt_eigvecs) return eigvecs def _compr_projector(p: csr_array) -> tuple[csr_array, Optional[csr_array]]: """Compress projection matrix p with many zero rows and columns.""" _, col_p = p.nonzero() # type: ignore col_p = np.unique(col_p) size = len(col_p) if p.shape[1] > size: compr = csr_array( (np.ones(size), (col_p, np.arange(size))), shape=(p.shape[1], size), dtype="int", ) """p = compr.T @ p @ compr""" p = p[col_p].T p = p[col_p].T return p, compr return p, None def _find_projector_blocks(p: csr_array, verbose: bool = False): """Find block structures in projection matrix.""" if verbose: print("Finding block diagonal structure in projector.", flush=True) if len(p.data) < 2147483647: if verbose: print("Using scipy connected_components.", flush=True) n_components, labels = scipy.sparse.csgraph.connected_components(p) group = defaultdict(list) for i, ll in enumerate(labels): group[ll].append(i) else: if verbose: print("Using symfc connected_components with DFS.", flush=True) group = connected_components(p) return group def _recover_eigvecs_from_uniq_eigvecs( uniq_eigvecs: dict, group: dict, size_projector: int, ) -> csr_array: """Recover all eigenvectors from unique eigenvectors. Parameters ---------- uniq_eigvecs: Unique eigenvectors and submatrixblock indices are included in its values. group: Row indices comprising submatrix blocks. """ total_length = sum( len(labels) * v.shape[0] * v.shape[1] for v, labels in uniq_eigvecs.values() if v is not None ) row = np.zeros(total_length, dtype=int) col = np.zeros(total_length, dtype=int) data = np.zeros(total_length, dtype="double") current_id, col_id = 0, 0 for eigvecs, labels in uniq_eigvecs.values(): if eigvecs is not None: n_row, n_col = eigvecs.shape num_labels = len(labels) end_id = current_id + n_row * n_col * num_labels row[current_id:end_id] = np.repeat( [i for ll in labels for i in group[ll]], n_col ) col[current_id:end_id] = [ j for seq, _ in enumerate(labels) for i in range(n_row) for j in range(col_id + seq * n_col, col_id + (seq + 1) * n_col) ] data[current_id:end_id] = np.tile(eigvecs.flatten(), num_labels) col_id += n_col * num_labels current_id = end_id n_col = col_id eigvecs = csr_array((data, (row, col)), shape=(size_projector, n_col)) return eigvecs @dataclass class DataCSR: """Dataclass for extracting data in projector.""" data: np.ndarray block_labels: np.ndarray block_sizes: np.ndarray slice_begin: Optional[np.ndarray] = None slice_end: Optional[np.ndarray] = None def __post_init__(self): """Init method.""" self.slice_end = np.cumsum(self.block_sizes**2) self.slice_begin = np.zeros_like(self.slice_end) self.slice_begin[1:] = self.slice_end[:-1] def get_data(self, idx: int): """Get data in projector for i-th block.""" if self.slice_begin is None or self.slice_end is None: raise ValueError("Slice begin and end are not initialized.") s1 = self.slice_begin[idx] s2 = self.slice_end[idx] return self.data[s1:s2] def get_block_label(self, idx: int): """Get block label for i-th block.""" return self.block_labels[idx] def get_block_size(self, idx: int): """Get block size for i-th block.""" return self.block_sizes[idx] @property def n_blocks(self): """Return number of blocks in projector.""" return len(self.block_labels) def _extract_sparse_projector_data(p: csr_array, group: dict) -> DataCSR: """Extract data in projector in csr_format efficiently. Parameters ---------- p: Projection matrix in CSR format. group: Row indices comprising submatrix blocks. """ # r = np.array([i for ids in group.values() for i in ids for j in ids]) group_ravel = [i for ids in group.values() for i in ids] lengths = [len(ids) for ids in group.values() for i in ids] r = np.repeat(group_ravel, lengths) c = np.array([j for ids in group.values() for _ in ids for j in ids]) sizes = np.array([len(ids) for ids in group.values()], dtype=int) p_data = DataCSR( data=np.ravel(p[r, c]), block_labels=np.array(list(group.keys())), block_sizes=sizes, ) return p_data def eigsh_projector( p: csr_array, atol: float = 1e-8, rtol: float = 0.0, verbose: bool = True, ) -> csr_array: """Solve eigenvalue problem for matrix p. Return sparse matrix for eigenvectors of matrix p. This algorithm begins with finding block diagonal structures in matrix p. For each block submatrix, eigenvectors are solved. When p = diag(A,B), Av = v, and Bw = w, p[v,0] = [v,0] and p[0,w] = [0,w] are solutions. This function avoids solving numpy.eigh for duplicate block matrices. This function is efficient for matrix p composed of many duplicate block matrices. """ p, compr_p = _compr_projector(p) group = _find_projector_blocks(p, verbose=verbose) if verbose: rank = matrix_rank(p) print("Rank of projector:", rank, flush=True) print("Number of blocks in projector:", len(group), flush=True) p_data = _extract_sparse_projector_data(p, group) uniq_eigvecs = dict() for i in range(p_data.n_blocks): p_block = p_data.get_data(i) block_label = p_data.get_block_label(i) block_size = p_data.get_block_size(i) if block_size > 1: key = tuple(p_block) try: uniq_eigvecs[key][1].append(block_label) except KeyError: p_np = p_block.reshape((block_size, block_size)) eigvecs = eigh_projector(p_np, atol=atol, rtol=rtol, verbose=verbose) uniq_eigvecs[key] = [eigvecs, [block_label]] else: if not np.isclose(p_block[0], 0.0): if "one" in uniq_eigvecs: uniq_eigvecs["one"][1].append(block_label) else: uniq_eigvecs["one"] = [np.array([[1.0]]), [block_label]] c_p = _recover_eigvecs_from_uniq_eigvecs(uniq_eigvecs, group, p.shape[0]) # type: ignore if compr_p is not None: return compr_p @ c_p return c_p def _find_submatrix_eigenvectors( p: Union[np.ndarray, csr_array], batch_size: Optional[int] = None, depth: int = 0, use_mkl: bool = False, verbose: bool = False, ): """Find eigenvectors in division part of submatrix division algorithm.""" p_size = p.shape[0] if p_size < 500: repeat = False elif p_size > 30000: repeat = True if depth < 8 else False else: repeat = True if depth < 3 else False if batch_size is None: if depth == 1: batch_size = max(p_size // 10, 500) elif depth == 2: batch_size = max(p_size // 5, 250) elif depth == 3: batch_size = p_size // 2 else: batch_size = min(int(round(p_size / 1.3)), 20000) sibling, sibling_c = None, None col_id, col_id_c = 0, 0 header = " " * (depth - 1) + "(" + str(depth) + ")" for begin, end in zip(*get_batch_slice(p_size, batch_size)): if verbose: print(header, "Block:", end, "/", p_size, flush=True) p_small = p[begin:end, begin:end] rank = matrix_rank(p_small) if rank > 0: if repeat: block, (cmplt_eigvals, cmplt_vecs) = eigh_projector_division( p_small, atol=1e-12, rtol=0.0, depth=depth, return_cmplt=True, return_block=True, use_mkl=use_mkl, verbose=verbose, ) else: block, (cmplt_eigvals, cmplt_vecs) = eigh_projector( p_small, atol=1e-12, rtol=0.0, return_cmplt=True, return_block=True, verbose=verbose, ) rows = np.arange(begin, end) if block is not None: if verbose: print( header, " ", block.shape[1], "eigenvectors found.", flush=True ) sibling = append_node(block, sibling, rows=rows, col_begin=col_id) col_id += block.shape[1] if cmplt_vecs is not None: sibling_c = append_node( cmplt_vecs, sibling_c, rows=rows, col_begin=col_id_c, eigvals=cmplt_eigvals, ) col_id_c += cmplt_vecs.shape[1] if col_id_c > 0: cmplt = root_block_matrix((p_size, col_id_c), first_child=sibling_c) else: cmplt = None return sibling, col_id, cmplt def _find_complement_eigenvectors( p: Union[np.ndarray, csr_array], sibling: BlockMatrixNode, col_id: int, cmplt: BlockMatrixNode, atol: float = 1e-8, rtol: float = 0.0, depth: int = 0, return_cmplt: bool = False, use_mkl: bool = False, verbose: bool = False, ): """Find eigenvectors in complementary part of submatrix division algorithm.""" p_size = p.shape[0] if p_size < 5000: repeat = False elif p_size > 30000: repeat = True if depth < 8 else False else: repeat = True if depth < 4 else False if depth == 1: batch_size_cmplt = max(cmplt.shape[1] // 3, p_size // 15) elif depth == 2: batch_size_cmplt = int(round(cmplt.shape[1] / 1.5)) elif depth == 3: batch_size_cmplt = int(round(cmplt.shape[1] / 1.3)) else: batch_size_cmplt = min(int(round(cmplt.shape[1] / 1.2)), 20000) header = " " * (depth - 1) + "(" + str(depth) + ")" if verbose: print(header, "Complementary block size:", cmplt.shape[1], flush=True) print(header, "Compute compressed projector.", flush=True) p_cmr = cmplt.compress_matrix(p, use_mkl=use_mkl) if not repeat: if verbose: print(header, "Use standard solver.", flush=True) result = eigh_projector( p_cmr, atol=atol, rtol=rtol, return_cmplt=return_cmplt, verbose=verbose, ) else: if verbose: print(header, "Use submatrix size of", batch_size_cmplt, flush=True) result = eigh_projector_division( p_cmr, atol=atol, rtol=rtol, depth=depth, batch_size=batch_size_cmplt, return_cmplt=return_cmplt, use_mkl=use_mkl, verbose=verbose, ) eigvecs = result[0] if return_cmplt else result if eigvecs is not None: if verbose: print(header, " ", eigvecs.shape[1], "eigenvectors found.", flush=True) sibling = append_node( eigvecs, sibling, rows=np.arange(p_size), col_begin=col_id, compress=cmplt, ) col_id += eigvecs.shape[1] if return_cmplt: cmplt_eigvals, cmplt_small = result[1] if cmplt_small is not None and cmplt_small.shape[1] > 0: cmplt_eigvecs = cmplt.dot(cmplt_small) else: cmplt_eigvals, cmplt_eigvecs = None, None return sibling, col_id, (cmplt_eigvals, cmplt_eigvecs) return sibling, col_id def _run_division( p: Union[np.ndarray, csr_array], batch_size: Optional[int] = None, atol: float = 1e-8, rtol: float = 0.0, depth: int = 0, return_cmplt: bool = False, use_mkl: bool = False, verbose: bool = False, ): """Find eigenvectors in division and complementary parts.""" depth += 1 sibling, col_id, cmplt = _find_submatrix_eigenvectors( p, batch_size=batch_size, depth=depth, use_mkl=use_mkl, verbose=verbose, ) if cmplt is not None: result = _find_complement_eigenvectors( p, sibling, col_id, cmplt, atol=atol, rtol=rtol, depth=depth, return_cmplt=return_cmplt, use_mkl=use_mkl, verbose=verbose, ) if return_cmplt: sibling, col_id, (cmplt_eigvals, cmplt_eigvecs) = result else: sibling, col_id = result else: cmplt_eigvals, cmplt_eigvecs = None, None block = root_block_matrix((p.shape[0], col_id), first_child=sibling) if return_cmplt: return block, (cmplt_eigvals, cmplt_eigvecs) return block def eigh_projector_division( p: Union[np.ndarray, csr_array], atol: float = 1e-8, rtol: float = 0.0, depth: int = 0, batch_size: Optional[int] = None, return_cmplt: bool = False, return_block: bool = False, use_mkl: bool = False, verbose: bool = False, ): r"""Solve eigenvalue problem using submatrix division algorithm. This algorithm is optimized to solve an eigenvalue problem for projection matrix p with large block submatrices. The algorithm for solving each block submatrix is as follows. 1. Divide each block submatrix into reasonable sizes of submatrices, not projectors. 2. Solve eigenvalue problems for these submatrices and eigenvectors with eigenvalues of one are extracted. The eigenvectors with eigenvalues e < 1 are used for compressing the complementary matrix in the next step. 3. Calculate the complementary matrix, which corresponds to the complement of the vector space spanned by the eigenvectors. The complementary matrix is compressed using the eigenvectors with e < 1. 4. Solve eigenvalue problem for the complementary matrix and eigenvectors with e = 1 are calculated. The eigenvalue problems are efficiently solved using the compressed complementary matrix and the eigenvectors are recovered by the compression matrix. 5. Collect all eigenvectors with e = 1. """ p_size = p.shape[0] if p_size < 500: return eigh_projector( p, atol=atol, rtol=rtol, return_cmplt=return_cmplt, return_block=return_block, verbose=verbose, ) result = _run_division( p, batch_size=batch_size, atol=atol, rtol=rtol, depth=depth, return_cmplt=return_cmplt, use_mkl=use_mkl, verbose=verbose, ) if return_block: return result if return_cmplt: block, (cmplt_eigvals, cmplt_eigvecs) = result else: block = result eigvecs = block.recover() if block is not None else None if return_cmplt: return eigvecs, (cmplt_eigvals, cmplt_eigvecs) return eigvecs def eigsh_projector_sumrule( p: csr_array, atol: float = 1e-8, rtol: float = 0.0, size_threshold: int = 500, use_mkl: bool = False, verbose: bool = True, ) -> BlockMatrixNode: """Solve eigenvalue problem for matrix p. Return dense matrix for eigenvectors of matrix p. This algorithm begins with finding block diagonal structures in matrix p. For each block submatrix, eigenvectors are solved. When p = diag(A,B), Av = v, and Bw = w, p[v,0] = [v,0] and p[0,w] = [0,w] are solutions. If p.shape[0] < size_threshold, this function solves numpy.eigh for all block matrices. Otherwise, this function use a submatrix division algorithm to solve the eigenvalue problem of each block matrix. """ group = _find_projector_blocks(p, verbose=verbose) lengths = [-len(ids) for ids in group.values()] order = np.array(list(group.keys()))[np.argsort(lengths)] if verbose: print("Number of blocks in projector (Sum rule):", len(group), flush=True) sibling, col_id = None, 0 for i, key in enumerate(order): ids = np.array(group[key]) if verbose and len(ids) > 0: print("--- Eigsh_solver_block:", i + 1, "/", len(group), "---", flush=True) print("Block_size:", len(ids), flush=True) p_block = p[np.ix_(ids, ids)] rank = matrix_rank(p_block) if rank > 0 and p_block.shape[0] < size_threshold: if verbose: print("Use standard eigh solver.", flush=True) p_block = p_block.toarray() eigvecs = eigh_projector(p_block, atol=atol, rtol=rtol, verbose=verbose) if eigvecs is not None: sibling = append_node(eigvecs, sibling, rows=ids, col_begin=col_id) col_id += eigvecs.shape[1] elif rank > 0 and p_block.shape[0] >= size_threshold: if verbose: print("Use submatrix version of eigh solver.", flush=True) block = eigh_projector_division( p_block, atol=atol, rtol=rtol, return_block=True, use_mkl=use_mkl, verbose=verbose, ) if block is not None: sibling = append_node(block, sibling, rows=ids, col_begin=col_id) col_id += block.shape[1] del p_block block = root_block_matrix((p.shape[0], col_id), first_child=sibling) if verbose: print("Tree of FC basis block matrices:", flush=True) block.print_nodes() return block symfc-1.6.0/src/symfc/utils/graph.py000066400000000000000000000025171511276756400174050ustar00rootroot00000000000000"""Functions for graph.""" import numpy as np from scipy.sparse import csr_array, issparse def get_cols(p: csr_array, node: int): """Return column indices of csr array.""" start = p.indptr[node] end = p.indptr[node + 1] if start == 0: return p.indices[end - 1 :: -1] return p.indices[end - 1 : start - 1 : -1] def dfs_tree_stack(p: csr_array, visited: np.ndarray, initial_node: int = 0): """Run depth first search using stack for sparse matrix p.""" visited_nodes = [] stack = [initial_node] while stack: node = stack.pop() if not visited[node]: visited[node] = True visited_nodes.append(node) cols = get_cols(p, node) unvisited = cols[~visited[cols]] stack.extend(unvisited.tolist()) return visited, sorted(visited_nodes) def connected_components(p: csr_array): """Calculate connected components of graph p.""" if not issparse(p): raise RuntimeError("Not sparse matrix.") p.eliminate_zeros() visited = np.zeros(p.shape[0], dtype=bool) group, label = dict(), 0 for node in range(p.shape[0]): if visited[node]: continue visited, visited_nodes = dfs_tree_stack(p, visited, initial_node=node) group[label] = visited_nodes label += 1 return group symfc-1.6.0/src/symfc/utils/matrix.py000066400000000000000000000305201511276756400176030ustar00rootroot00000000000000"""Utility functions for matrices.""" from dataclasses import dataclass from typing import Any, Optional, Union import numpy as np from scipy.sparse import csr_array try: from sparse_dot_mkl import dot_product_mkl # type: ignore except ImportError: pass import sys sys.setrecursionlimit(100000) def dot_product_sparse( A: Union[np.ndarray, csr_array], B: Union[np.ndarray, csr_array], use_mkl: bool = False, dense: bool = False, ) -> csr_array: """Compute dot-product of sparse matrices.""" if use_mkl: return dot_product_mkl(A, B, dense=dense) return A @ B def is_sparse(p: Union[np.ndarray, csr_array]) -> bool: """Check whether matrix is sparse matrix or not.""" if isinstance(p, np.ndarray): return False elif isinstance(p, list): return False return True def return_numpy_array(p: Union[np.ndarray, csr_array]) -> np.ndarray: """Return numpy array.""" if isinstance(p, np.ndarray): return p return p.toarray() def matrix_rank(p: Union[np.ndarray, csr_array]) -> int: """Calculate projector rank.""" if is_sparse(p): return int(round(p.trace())) return int(round(np.trace(p))) @dataclass class BlockMatrixNode: """Dataclass for node in block matrix tree.""" rows: np.ndarray col_begin: int col_end: int root: bool = False shape: Optional[tuple] = None data: Optional[np.ndarray] = None first_child: Optional[Any] = None next_sibling: Optional[Any] = None compress: Optional[Any] = None rows_root: Optional[np.ndarray] = None col_begin_root: Optional[int] = None col_end_root: Optional[int] = None index: Optional[int] = None eigvals: Optional[np.ndarray] = None def __post_init__(self): """Post init method.""" self.shape = (len(self.rows), self.col_end - self.col_begin) self.rows = np.array(self.rows) self._check_errors() if self.root: self.set_root_indices() def _check_errors(self): """Check errors in node.""" if self.data is None: if self.first_child is None: raise RuntimeError("No data in this node and its children.") if self.compress is not None: raise RuntimeError("Data is required with compress matrix.") else: if self.compress is None: if self.data.shape != self.shape: raise RuntimeError( "Data shape is inconsistent with rows and columns." ) else: if self.compress.shape[0] != self.shape[0]: raise RuntimeError("Data shape is inconsistent with rows.") if self.data.shape[1] != self.shape[1]: raise RuntimeError("Data shape is inconsistent with columns.") def traverse_data_nodes(self): """Traverse all nodes with data.""" if self.first_child is not None: yield from self.first_child.traverse_data_nodes() if self.next_sibling is not None: yield from self.next_sibling.traverse_data_nodes() if self.data is not None: yield self def print_nodes(self, depth: int = 0): """Print all nodes.""" if depth == 0: header = "-" else: header = " " * (depth - 1) + "|--" print(header, self.shape, end=", ", flush=True) if self.data is not None: if self.compress is not None: print("data:", self.compress.shape, "@", self.data.shape, flush=True) else: print("data: True", flush=True) else: print("data: False", flush=True) if self.first_child is not None: self.first_child.print_nodes(depth=depth + 1) if self.next_sibling is not None: self.next_sibling.print_nodes(depth=depth) return self def set_root_indices( self, parent_rows: Optional[np.ndarray] = None, parent_col_begin: Optional[int] = None, ): """Set row and columns indices compatible with root node and full matrix.""" if parent_rows is not None: self.rows_root = parent_rows[self.rows] self.col_begin_root = self.col_begin + parent_col_begin self.col_end_root = self.col_end + parent_col_begin if self.root: self.root = False if self.first_child is not None: if parent_rows is None: self.first_child.set_root_indices(self.rows, self.col_begin) else: self.first_child.set_root_indices( parent_rows[self.rows], parent_col_begin + self.col_begin, ) if self.next_sibling is not None: self.next_sibling.set_root_indices(parent_rows, parent_col_begin) return self def decompress(self): """Decompress compressed data matrix.""" if self.compress is not None: return self.compress.dot(self.data) return self.data def recover(self): """Recover full block matrix.""" if not self.root: raise RuntimeError("Node must be root of tree.") full = np.zeros(self.shape, dtype="double") # type: ignore for b in self.traverse_data_nodes(): full[b.rows_root, b.col_begin_root : b.col_end_root] = b.decompress() return full def dot(self, mat: np.ndarray): """Calculate dot product block_mat @ mat.""" if not self.root: raise RuntimeError("Node must be root of tree.") if len(mat.shape) == 1: prod = np.zeros(self.shape[0]) elif len(mat.shape) == 2: prod = np.zeros((self.shape[0], mat.shape[1])) else: raise RuntimeError("Dimension of input numpy array must be one or two.") for b in self.traverse_data_nodes(): res = b.data @ mat[b.col_begin_root : b.col_end_root] if b.compress is not None: res = b.compress.dot(res) prod[b.rows_root] += res return prod def transpose_dot(self, mat: np.ndarray): """Calculate dot product block_mat.T @ mat.""" if not self.root: raise RuntimeError("Node must be root of tree.") if len(mat.shape) == 1: prod = np.zeros(self.shape[1]) elif len(mat.shape) == 2: prod = np.zeros((self.shape[1], mat.shape[1])) else: raise RuntimeError("Dimension of input numpy array must be one or two.") for b in self.traverse_data_nodes(): if b.compress is not None: res = b.data.T @ b.compress.transpose_dot(mat[b.rows_root]) else: res = b.data.T @ mat[b.rows_root] prod[b.col_begin_root : b.col_end_root] += res return prod def compress_matrix(self, mat: Union[csr_array, np.ndarray], use_mkl: bool = False): """Calculate block_mat.T @ mat @ block_mat. Block matrix must be eigenvectors and include their eigenvalues. """ if not self.root: raise RuntimeError("Node must be root of tree.") if is_sparse(mat): return self.compress_sparse_matrix(mat, use_mkl=use_mkl) return self.compress_dense_matrix(mat) def compress_dense_matrix(self, mat: np.ndarray): """Calculate block_mat.T @ mat @ block_mat for numpy array. Block matrix must be eigenvectors and include their eigenvalues. """ if not self.root: raise RuntimeError("Node must be root of tree.") if self.shape[1] < 10000: return self.recover().T @ mat @ self.recover() res = np.zeros((self.shape[1], self.shape[1])) for i, b1 in enumerate(self.traverse_data_nodes()): col_begin1, col_end1 = b1.col_begin_root, b1.col_end_root data1 = b1.decompress() for c, val in zip(range(col_begin1, col_end1), b1.eigvals): res[c, c] = val for j, b2 in enumerate(self.traverse_data_nodes()): if i != j: col_begin2, col_end2 = b2.col_begin_root, b2.col_end_root data2 = b2.decompress() prod = data1.T @ mat[np.ix_(b1.rows_root, b2.rows_root)] @ data2 res[col_begin1:col_end1, col_begin2:col_end2] = prod return res def compress_sparse_matrix(self, mat: csr_array, use_mkl: bool = False): """Calculate block_mat.T @ mat(csr) @ block_mat for csr_array. Block matrix must be eigenvectors and include their eigenvalues. """ if not self.root: raise RuntimeError("Node must be root of tree.") if mat.shape[0] < 30000: use_mkl = False res = np.zeros((self.shape[1], self.shape[1])) for i, b1 in enumerate(self.traverse_data_nodes()): col_begin1, col_end1 = b1.col_begin_root, b1.col_end_root data1 = b1.decompress() mat1 = mat[b1.rows_root] for c, val in zip(range(col_begin1, col_end1), b1.eigvals): res[c, c] = val for j, b2 in enumerate(self.traverse_data_nodes()): if i > j: col_begin2, col_end2 = b2.col_begin_root, b2.col_end_root data2 = b2.decompress() mat_slice = mat1[:, b2.rows_root] mat_slice_t = mat[np.ix_(b2.rows_root, b1.rows_root)].T mat_slice = 0.5 * (mat_slice + mat_slice_t) prod = dot_product_sparse(mat_slice, data2, use_mkl=use_mkl) prod = dot_product_sparse( data1.T, prod, use_mkl=use_mkl, dense=True ) res[col_begin1:col_end1, col_begin2:col_end2] = prod res[col_begin2:col_end2, col_begin1:col_end1] = prod.T return res def append_node( eigvecs: np.ndarray, next_sibling: BlockMatrixNode, rows: np.ndarray, col_begin, compress: Optional[BlockMatrixNode] = None, eigvals: Optional[np.ndarray] = None, ): """Add eigenvectors to block matrix node.""" if isinstance(eigvecs, BlockMatrixNode): block = eigvecs if block.shape[1] > 0: block.rows = rows block.col_begin = col_begin block.col_end = col_begin + block.shape[1] # type: ignore block.next_sibling = next_sibling block.eigvals = eigvals block.root = False next_sibling = block else: if eigvecs is not None and eigvecs.shape[1] > 0: col_end = col_begin + eigvecs.shape[1] # type: ignore block = BlockMatrixNode( rows=rows, col_begin=col_begin, col_end=col_end, data=eigvecs, next_sibling=next_sibling, compress=compress, eigvals=eigvals, ) next_sibling = block return next_sibling def root_block_matrix( shape: Optional[tuple] = None, data: Optional[np.ndarray] = None, first_child: Optional[BlockMatrixNode] = None, ): """Return root block matrix.""" if shape is None and data is None: raise RuntimeError("Shape or data is required.") if data is not None: shape = data.shape if shape[1] == 0: return None return BlockMatrixNode( rows=np.arange(shape[0]), col_begin=0, col_end=shape[1], first_child=first_child, data=data, root=True, ) def block_matrix_sandwich(bm1: BlockMatrixNode, bm2: BlockMatrixNode, mat: np.ndarray): """Calculate block1.T @ mat @ block2.""" if not bm1.root or not bm2.root: raise RuntimeError("Nodes must be root of tree.") if bm1.shape[1] < 20000 and bm2.shape[1] < 20000: return bm1.recover().T @ mat @ bm2.recover() res = np.zeros((bm1.shape[1], bm2.shape[1])) for b1 in bm1.traverse_data_nodes(): col_begin1, col_end1 = b1.col_begin_root, b1.col_end_root data1 = b1.decompress() for b2 in bm2.traverse_data_nodes(): col_begin2, col_end2 = b2.col_begin_root, b2.col_end_root data2 = b2.decompress() prod = data1.T @ mat[np.ix_(b1.rows_root, b2.rows_root)] @ data2 res[col_begin1:col_end1, col_begin2:col_end2] += prod return res symfc-1.6.0/src/symfc/utils/permutation_tools.py000066400000000000000000000067261511276756400221010ustar00rootroot00000000000000"""Permutation utility functions.""" from typing import Optional import numpy as np import scipy from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff def get_entire_combinations(n: int, r: int): """Return numpy array of combinations. combinations = np.array( list(itertools.combinations(range(n), r)), dtype=int ) """ combs = np.ones((r, n - r + 1), dtype=int) combs[0] = np.arange(n - r + 1) for j in range(1, r): reps = (n - r + j) - combs[j - 1] combs = np.repeat(combs, reps, axis=1) ind = np.add.accumulate(reps) combs[j, ind[:-1]] = 1 - reps[1:] combs[j, 0] = j combs[j] = np.add.accumulate(combs[j]) return combs.T def get_combinations( natom: int, order: int, fc_cutoff: Optional[FCCutoff] = None, indep_atoms: Optional[np.ndarray] = None, ): """Return numpy array of FC index combinations.""" if fc_cutoff is None: combinations = get_entire_combinations(3 * natom, order) else: if order == 2: combinations = fc_cutoff.combinations2() elif order == 3: """Combinations can be divided using fc_cut.combiations3(i).""" combinations = fc_cutoff.combinations3_all() elif order == 4: combinations = fc_cutoff.combinations4_all() else: raise NotImplementedError( "Combinations are implemented only for 2 <= order <= 4." ) if indep_atoms is not None: nonzero = np.zeros(combinations.shape[0], dtype=bool) atom_indices = combinations[:, 0] // 3 for i in indep_atoms: nonzero[atom_indices == i] = True combinations = combinations[nonzero] return combinations def _eliminate_zero_elements( perm_decompr_idx: np.ndarray, nonzero: np.ndarray ) -> np.ndarray: """Eliminate zero elements and reindex orbit indexes.""" size_full = len(perm_decompr_idx) if not np.all(nonzero): perm_decompr_idx = perm_decompr_idx[nonzero] nonzero_map = np.ones(size_full, dtype="int") * -1 nonzero_map[nonzero] = np.arange(len(perm_decompr_idx)) perm_decompr_idx = nonzero_map[perm_decompr_idx] return perm_decompr_idx def construct_basis_from_perm_decompr_indices( perm_decompr_idx: np.ndarray, verbose: bool = False ): """Transform perm_decompr_idx into basis matrix. Parameters ---------- perm_decompr_idx: Decompression indices of lattice translation basis using permutations. Return ------ c_pt: Compressed basis matrix for permutations and lattice translations. c_pt = eigh(C_trans.T @ C_perm @ C_perm.T @ C_trans) """ if verbose: print("Construct permutation basis matrix.", flush=True) size_full = len(perm_decompr_idx) nonzero = perm_decompr_idx != -1 perm_decompr_idx = _eliminate_zero_elements(perm_decompr_idx, nonzero) size1 = len(perm_decompr_idx) perm_lat_trans_graph = csr_array( (np.ones(size1, dtype=bool), (np.arange(size1), perm_decompr_idx)), shape=(size1, size1), dtype=bool, ) n_col, cols = scipy.sparse.csgraph.connected_components(perm_lat_trans_graph) key, cnt = np.unique(cols, return_counts=True) values = np.reciprocal(np.sqrt(cnt)) rows = np.where(nonzero)[0] c_pt = csr_array( (values[cols], (rows, cols)), shape=(size_full, n_col), dtype="double", ) return c_pt symfc-1.6.0/src/symfc/utils/permutation_tools_O2.py000066400000000000000000000112021511276756400224220ustar00rootroot00000000000000"""Permutation utility functions for 2nd order force constants.""" from typing import Optional, Union import numpy as np from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.permutation_tools import ( construct_basis_from_perm_decompr_indices, get_combinations, ) from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O2 import _get_atomic_lat_trans_decompr_indices def _N3N3_to_NNand33(combs: np.ndarray, N: int) -> tuple[np.ndarray, np.ndarray]: """Transform index order.""" vecNN, vec33 = np.divmod(combs[:, 0], 3) vecNN *= N vec33 *= 3 div, mod = np.divmod(combs[:, 1], 3) vecNN += div vec33 += mod return vecNN, vec33 def compr_permutation_lat_trans_O2( trans_perms: np.ndarray, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, verbose: bool = False, ) -> csr_array: r"""Build a compression matrix for permutation rules compressed by C_trans. This calculates C_(trans,perm) without allocating C_trans and C_perm. Batch calculations are used to reduce memory allocation. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. fc_cutoff : FCCutoff class object. Default is None. Return ------ c_pt: Compressed basis matrix for permutations and lattice translations. c_pt = eigh(C_trans.T @ C_perm @ C_perm.T @ C_trans) shape: (NN33//n_lp, n_basis_pt). n_basis_pt denotes the size of basis for permutations and lattice translations. Algorithm --------- 1. Calculate combinations {(i, a), (j, b)}. 2. Apply permutations to these combinations and calculate the orbits of {(i, a), (j, b)} related to each other by permutations. 3. Calculate the lattice translation basis indices of the orbits, and represent the lattice translation indices related to each other by a representative in perm_decompr_idx. """ n_lp, natom = trans_perms.shape NN9 = natom**2 * 9 if atomic_decompr_idx is None: atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) perm_decompr_idx = np.ones(NN9 // n_lp, dtype="int") * -1 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) # order = 1 combinations = np.array([[i, i] for i in range(3 * natom)], dtype=int) perms = [[0, 0]] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=1, verbose=verbose, ) # order = 2 combinations = get_combinations( natom, order=2, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = [[0, 1], [1, 0]] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=1, verbose=verbose, ) c_pt = construct_basis_from_perm_decompr_indices(perm_decompr_idx, verbose=verbose) return c_pt def _update_perm_decompr_indices( combinations: np.ndarray, permutations: Union[np.ndarray, list], atomic_decompr_idx: np.ndarray, trans_perms: np.ndarray, perm_decompr_idx: np.ndarray, n_perms_group: int = 1, n_batch: int = 1, verbose: bool = False, ) -> np.ndarray: """Apply permutations to lattice translation basis. Return ------ perm_decompr_idx: Updated decompression indices of lattice translation basis using permutations. """ n_lp, natom = trans_perms.shape n_comb = combinations.shape[0] n_perms = len(permutations) n_perms_sym = n_perms // n_perms_group for begin, end in zip(*get_batch_slice(n_comb, n_comb // n_batch)): if verbose: print("Permutation basis:", str(end) + "/" + str(n_comb), flush=True) combs_perm = combinations[begin:end][:, permutations].reshape((-1, 2)) combs_perm, combs33 = _N3N3_to_NNand33(combs_perm, natom) decompr_idx_combs_perm = atomic_decompr_idx[combs_perm] * 9 + combs33 decompr_idx_combs_perm = decompr_idx_combs_perm.reshape(-1, n_perms_sym) for orbit_components in decompr_idx_combs_perm.T: perm_decompr_idx[orbit_components] = decompr_idx_combs_perm[:, 0] return perm_decompr_idx symfc-1.6.0/src/symfc/utils/permutation_tools_O3.py000066400000000000000000000127251511276756400224360ustar00rootroot00000000000000"""Permutation utility functions for 3rd order force constants.""" from typing import Optional, Union import numpy as np from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.permutation_tools import ( construct_basis_from_perm_decompr_indices, get_combinations, ) from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O3 import get_atomic_lat_trans_decompr_indices_O3 def _N3N3N3_to_NNNand333(combs: np.ndarray, N: int) -> tuple[np.ndarray, np.ndarray]: """Transform index order.""" vecNNN, vec333 = np.divmod(combs[:, 0], 3) vecNNN *= N**2 vec333 *= 9 div, mod = np.divmod(combs[:, 1], 3) vecNNN += div * N vec333 += mod * 3 div, mod = np.divmod(combs[:, 2], 3) vecNNN += div vec333 += mod return vecNNN, vec333 def compr_permutation_lat_trans_O3( trans_perms: np.ndarray, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, verbose: bool = False, ) -> csr_array: r"""Build a compression matrix for permutation rules compressed by C_trans. This calculates C_(trans,perm) without allocating C_trans and C_perm. Batch calculations are used to reduce memory allocation. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. fc_cutoff : FCCutoff class object. Default is None. Return ------ c_pt: Compressed basis matrix for permutations and lattice translations. c_pt = eigh(C_trans.T @ C_perm @ C_perm.T @ C_trans) shape: (NNN333//n_lp, n_basis_pt). n_basis_pt denotes the size of basis for permutations and lattice translations. Algorithm --------- 1. Calculate combinations {(i, a), (j, b), (k, c)}. 2. Apply permutations to these combinations and calculate the orbits of {(i, a), (j, b), (k, c)} related to each other by permutations. 3. Calculate the lattice translation basis indices of the orbits, and represent the lattice translation indices related to each other by a representative in perm_decompr_idx. """ n_lp, natom = trans_perms.shape NNN27 = natom**3 * 27 if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) if n_batch is None: n_batch = 1 if natom <= 128 else int(round((natom / 128) ** 2)) perm_decompr_idx = np.ones(NNN27 // n_lp, dtype="int") * -1 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) # order = 1 combinations = np.array([[i, i, i] for i in range(3 * natom)], dtype=int) perms = [[0, 0, 0]] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=1, verbose=verbose, ) # order = 2 combinations = get_combinations( natom, order=2, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = [ [0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], ] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=2, n_batch=1, verbose=verbose, ) # order = 3 combinations = get_combinations( natom, order=3, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = [ [0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0], ] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=n_batch, verbose=verbose, ) c_pt = construct_basis_from_perm_decompr_indices(perm_decompr_idx, verbose=verbose) return c_pt def _update_perm_decompr_indices( combinations: np.ndarray, permutations: Union[np.ndarray, list], atomic_decompr_idx: np.ndarray, trans_perms: np.ndarray, perm_decompr_idx: np.ndarray, n_perms_group: int = 1, n_batch: int = 1, verbose: bool = False, ) -> np.ndarray: """Apply permutations to lattice translation basis. Return ------ perm_decompr_idx: Updated decompression indices of lattice translation basis using permutations. """ n_lp, natom = trans_perms.shape n_comb = combinations.shape[0] n_perms = len(permutations) n_perms_sym = n_perms // n_perms_group for begin, end in zip(*get_batch_slice(n_comb, n_comb // n_batch)): if verbose: print("Permutation basis:", str(end) + "/" + str(n_comb), flush=True) combs_perm = combinations[begin:end][:, permutations].reshape((-1, 3)) combs_perm, combs333 = _N3N3N3_to_NNNand333(combs_perm, natom) decompr_idx_combs_perm = atomic_decompr_idx[combs_perm] * 27 + combs333 decompr_idx_combs_perm = decompr_idx_combs_perm.reshape(-1, n_perms_sym) for orbit_components in decompr_idx_combs_perm.T: perm_decompr_idx[orbit_components] = decompr_idx_combs_perm[:, 0] return perm_decompr_idx symfc-1.6.0/src/symfc/utils/permutation_tools_O4.py000066400000000000000000000155351511276756400224410ustar00rootroot00000000000000"""Permutation utility functions for 4th order force constants.""" import itertools from typing import Optional, Union import numpy as np from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.permutation_tools import ( construct_basis_from_perm_decompr_indices, get_combinations, ) from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O4 import get_atomic_lat_trans_decompr_indices_O4 def _N3N3N3N3_to_NNNNand3333( combs: np.ndarray, N: int ) -> tuple[np.ndarray, np.ndarray]: """Transform index order.""" vecNNNN, vec3333 = np.divmod(combs[:, 0], 3) vecNNNN *= N**3 vec3333 *= 27 div, mod = np.divmod(combs[:, 1], 3) vecNNNN += div * N**2 vec3333 += mod * 9 div, mod = np.divmod(combs[:, 2], 3) vecNNNN += div * N vec3333 += mod * 3 div, mod = np.divmod(combs[:, 3], 3) vecNNNN += div vec3333 += mod return vecNNNN, vec3333 def compr_permutation_lat_trans_O4( trans_perms: np.ndarray, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, verbose: bool = False, ) -> csr_array: r"""Build a compression matrix for permutation rules compressed by C_trans. This calculates C_(trans,perm) without allocating C_trans and C_perm. Batch calculations are used to reduce memory allocation. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. fc_cutoff : FCCutoff class object. Default is None. Return ------ c_pt: Compressed basis matrix for permutations and lattice translations. c_pt = eigh(C_trans.T @ C_perm @ C_perm.T @ C_trans) shape: (NNNN3333//n_lp, n_basis_pt). n_basis_pt denotes the size of basis for permutations and lattice translations. Algorithm --------- 1. Calculate combinations {(i, a), (j, b), (k, c)}. 2. Apply permutations to these combinations and calculate the orbits of {(i, a), (j, b), (k, c)} related to each other by permutations. 3. Calculate the lattice translation basis indices of the orbits, and represent the lattice translation indices related to each other by a representative in perm_decompr_idx. """ n_lp, natom = trans_perms.shape NNNN81 = natom**4 * 81 if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) perm_decompr_idx = np.ones(NNNN81 // n_lp, dtype="int") * -1 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) # order = 1 combinations = np.array([[i, i, i] for i in range(3 * natom)], dtype=int) perms = [[0, 0, 0, 0]] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=1, verbose=verbose, ) # order = 2 combinations = get_combinations( natom, order=2, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = [ [0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1, 1, 0, 1], [1, 0, 1, 1], [0, 1, 1, 1], ] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=2, n_batch=1, verbose=verbose, ) # order = 3 if n_batch is None: n_batch3 = 1 if natom <= 128 else int(round((natom / 128) ** 2)) combinations = get_combinations( natom, order=3, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = [ [0, 0, 1, 2], [0, 0, 2, 1], [0, 1, 0, 2], [0, 2, 0, 1], [0, 1, 2, 0], [0, 2, 1, 0], [1, 0, 0, 2], [2, 0, 0, 1], [1, 0, 2, 0], [2, 0, 1, 0], [1, 2, 0, 0], [2, 1, 0, 0], [1, 1, 0, 2], [1, 1, 2, 0], [1, 0, 1, 2], [1, 2, 1, 0], [1, 0, 2, 1], [1, 2, 0, 1], [0, 1, 1, 2], [2, 1, 1, 0], [0, 1, 2, 1], [2, 1, 0, 1], [0, 2, 1, 1], [2, 0, 1, 1], [2, 2, 1, 0], [2, 2, 0, 1], [2, 1, 2, 0], [2, 0, 2, 1], [2, 1, 0, 2], [2, 0, 1, 2], [1, 2, 2, 0], [0, 2, 2, 1], [1, 2, 0, 2], [0, 2, 1, 2], [1, 0, 2, 2], [0, 1, 2, 2], ] perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=3, n_batch=n_batch3, verbose=verbose, ) # order = 4 if n_batch is None: n_batch4 = 1 if natom <= 16 else int(round((natom / 16) ** 2)) combinations = get_combinations( natom, order=4, fc_cutoff=fc_cutoff, indep_atoms=indep_atoms ) perms = np.array(list(itertools.permutations(range(4)))) perm_decompr_idx = _update_perm_decompr_indices( combinations, perms, atomic_decompr_idx, trans_perms, perm_decompr_idx, n_perms_group=1, n_batch=n_batch4, verbose=verbose, ) c_pt = construct_basis_from_perm_decompr_indices(perm_decompr_idx, verbose=verbose) return c_pt def _update_perm_decompr_indices( combinations: np.ndarray, permutations: Union[np.ndarray, list], atomic_decompr_idx: np.ndarray, trans_perms: np.ndarray, perm_decompr_idx: np.ndarray, n_perms_group: int = 1, n_batch: int = 1, verbose: bool = False, ) -> np.ndarray: """Apply permutations to lattice translation basis. Return ------ perm_decompr_idx: Updated decompression indices of lattice translation basis using permutations. """ n_lp, natom = trans_perms.shape n_comb = combinations.shape[0] n_perms = len(permutations) n_perms_sym = n_perms // n_perms_group for begin, end in zip(*get_batch_slice(n_comb, n_comb // n_batch)): if verbose: print("Permutation basis:", str(end) + "/" + str(n_comb), flush=True) combs_perm = combinations[begin:end][:, permutations].reshape((-1, 4)) combs_perm, combs3333 = _N3N3N3N3_to_NNNNand3333(combs_perm, natom) decompr_idx_combs_perm = atomic_decompr_idx[combs_perm] * 81 + combs3333 decompr_idx_combs_perm = decompr_idx_combs_perm.reshape(-1, n_perms_sym) for orbit_components in decompr_idx_combs_perm.T: perm_decompr_idx[orbit_components] = decompr_idx_combs_perm[:, 0] return perm_decompr_idx symfc-1.6.0/src/symfc/utils/rotation_dev/000077500000000000000000000000001511276756400204225ustar00rootroot00000000000000symfc-1.6.0/src/symfc/utils/rotation_dev/run_rotation_tools_O2.py000066400000000000000000000260611511276756400253040ustar00rootroot00000000000000"""Matrix utility functions for setting rotational invariants.""" # from typing import Optional # import itertools import numpy as np import scipy from pypolymlp.core.interface_vasp import Poscar from pypolymlp.utils.structure_utils import supercell_diagonal from scipy.sparse import csr_array from symfc import Symfc from symfc.basis_sets.basis_sets_O2 import FCBasisSetO2 from symfc.spg_reps import SpgRepsBase from symfc.utils.eig_tools import eigsh_projector, eigsh_projector_sumrule from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( get_lat_trans_compr_matrix, get_lat_trans_decompr_indices, ) try: from symfc.utils.eig_tools import dot_product_sparse except ImportError: pass import copy def _orthogonalize_constraints(positions_cartesian: np.ndarray): """Orthogonalize constraints derived from rotational invariance.""" natom = positions_cartesian.shape[0] N3 = 3 * natom C = np.zeros((N3, 3), dtype="double") C[1::3, 0] = -positions_cartesian[:, 0] C[2::3, 2] = positions_cartesian[:, 0] C[2::3, 1] = -positions_cartesian[:, 1] C[0::3, 0] = positions_cartesian[:, 1] C[0::3, 2] = -positions_cartesian[:, 2] C[1::3, 1] = positions_cartesian[:, 2] # # U, S, V = np.linalg.svd(C, full_matrices=False) # # nonzero = np.abs(S) > 1e-15 # # C = U[:,nonzero] # # C[np.abs(C) < 1e-15] = 0.0 # # print(S) proj_C = C @ np.linalg.inv(C.T @ C) @ C.T eigvals, eigvecs = np.linalg.eigh(proj_C) nonzero = np.isclose(eigvals, 1.0) C = eigvecs[:, nonzero] return C def complement_sum_rule(natom, decompr_idx=None, n_lp=None): """Construct sum rule.""" N33 = natom * 9 NN33 = natom**2 * 9 row = [] for j, alpha, beta in itertools.product(range(natom), range(3), range(3)): i = np.arange(natom) * N33 const_add = j * 9 + alpha * 3 + beta row.extend(i + const_add) col = np.repeat(np.arange(N33), natom) data = np.ones(len(col)) / np.sqrt(natom) if decompr_idx is not None: data /= np.sqrt(n_lp) c_sum = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, N33), dtype="double", ) else: c_sum = csr_array( (data, (row, col)), shape=(NN33, N33), dtype="double", ) return c_sum def complementary_compr_projector_rot_sum_rules_O2( supercell: SymfcAtoms, trans_perms: np.ndarray, n_a_compress_mat: np.ndarray, use_mkl: bool = False, ) -> csr_array: """Test function for setting rotational invariants.""" n_lp, natom = trans_perms.shape # TODO: decompr_idx -> atomic_decompr_idx decompr_idx = get_lat_trans_decompr_indices(trans_perms) indep_atoms = list(range(natom)) positions_cartesian = (supercell.scaled_positions) @ supercell.cell positions_cartesian[:, 0] -= np.average(positions_cartesian[:, 0]) positions_cartesian[:, 1] -= np.average(positions_cartesian[:, 1]) positions_cartesian[:, 2] -= np.average(positions_cartesian[:, 2]) C2 = _orthogonalize_constraints(positions_cartesian) N3 = natom * 3 N33 = N3 * 3 NN33 = N3 * N3 n_expand = len(indep_atoms) * 3 data = np.tile(C2.T.reshape(-1), n_expand) col = np.repeat(np.arange(n_expand * 3), N3) row = [] ids_ialpha = (np.arange(natom) * N33)[:, None] + (np.arange(3) * 3)[None, :] ids_ialpha = ids_ialpha.reshape(-1) for j, beta in itertools.product(indep_atoms, range(3)): ids = ids_ialpha + (j * 9 + beta) row.extend(np.tile(ids, 3)) data /= np.sqrt(n_lp) c_rot_cmplt = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, n_expand * 3), dtype="double", ) proj = scipy.sparse.identity(NN33 // n_lp) - c_rot_cmplt @ c_rot_cmplt.T c_rot = eigsh_projector(proj, verbose=True) c_cmplt = n_a_compress_mat.T @ c_rot proj = c_cmplt @ c_cmplt.T return proj def complement_rotational_sum_rule(supercell, indep_atoms, decompr_idx=None, n_lp=None): """Get complement_rotational_sum_rule.""" positions_cartesian = (supercell.scaled_positions) @ supercell.cell positions_cartesian[:, 0] -= np.average(positions_cartesian[:, 0]) positions_cartesian[:, 1] -= np.average(positions_cartesian[:, 1]) positions_cartesian[:, 2] -= np.average(positions_cartesian[:, 2]) C2 = _orthogonalize_constraints(positions_cartesian) natom = positions_cartesian.shape[0] N3 = natom * 3 N33 = N3 * 3 NN33 = N3 * N3 n_expand = len(indep_atoms) * 3 data = np.tile(C2.T.reshape(-1), n_expand) col = np.repeat(np.arange(n_expand * 3), N3) row = [] ids_ialpha = (np.arange(natom) * N33)[:, None] + (np.arange(3) * 3)[None, :] ids_ialpha = ids_ialpha.reshape(-1) for j, beta in itertools.product(indep_atoms, range(3)): ids = ids_ialpha + (j * 9 + beta) row.extend(np.tile(ids, 3)) if decompr_idx is None: c_rot = csr_array( (data, (row, col)), shape=(NN33, n_expand * 3), dtype="double", ) else: data /= np.sqrt(n_lp) c_rot = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, n_expand * 3), dtype="double", ) return c_rot def complementary_compr_projector_rot_O2_test( supercell: SymfcAtoms, trans_perms: np.ndarray, basis_set_fc2: FCBasisSetO2, indep_atoms=None, use_mkl: bool = False, ) -> csr_array: """Test function for setting rotational invariants.""" n_lp, natom = trans_perms.shape N3 = natom * 3 NN33 = N3 * N3 decompr_idx = get_lat_trans_decompr_indices(trans_perms) c_trans = get_lat_trans_compr_matrix(decompr_idx, natom, n_lp) # indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) # indep_atoms = [indep_atoms[0]] if indep_atoms is None: indep_atoms = list(range(natom)) # indep_atoms = [2] # indep_atoms = [0,1,2,3,4,5,6,7] c_rot = complement_rotational_sum_rule(supercell, indep_atoms) proj_rot = c_rot @ c_rot.T c_sum = complement_sum_rule(natom) proj_sum = c_sum @ c_sum.T print("--------------") print("Only lattice translation") proj = dot_product_sparse(c_trans, c_trans.T, use_mkl=use_mkl) print(proj.shape) c = eigsh_projector(proj, verbose=True) print(c.shape) print("--------------") print("Only translational sum rule") proj = scipy.sparse.identity(NN33) - proj_sum c = eigsh_projector(proj, verbose=True) print(c.shape) c = c_trans.T @ c print(c.shape) proj = dot_product_sparse(c, c.T, use_mkl=use_mkl) print(proj.shape) c = eigsh_projector(proj, verbose=True) print(c.shape) c_sum = complement_sum_rule(natom, decompr_idx=decompr_idx, n_lp=n_lp) proj = scipy.sparse.identity(NN33 // n_lp) - c_sum @ c_sum.T c = eigsh_projector(proj, verbose=True) print(c.shape) # print("--------------") # print("Only rotational sum rules (indep_atoms)") # # c_rot1 = complement_rotational_sum_rule( # supercell, [0], decompr_idx=decompr_idx, n_lp=n_lp # ) # # c_rot2 = complement_rotational_sum_rule( # supercell, [2], decompr_idx=decompr_idx, n_lp=n_lp # ) # #print(c_rot1 - c_rot2) print("--------------") print("Only rotational sum rules") c_rot = complement_rotational_sum_rule( supercell, indep_atoms, decompr_idx=decompr_idx, n_lp=n_lp ) proj = scipy.sparse.identity(NN33) - proj_rot c = eigsh_projector(proj, verbose=True) print(c.shape) c = c_trans.T @ c proj = dot_product_sparse(c, c.T, use_mkl=use_mkl) print(proj.shape) c = eigsh_projector(proj, verbose=True) print(c.shape) proj = scipy.sparse.identity(NN33 // n_lp) - c_rot @ c_rot.T proj_return = copy.copy(proj) c = eigsh_projector(proj, verbose=True) print(c.shape) vals, vecs = np.linalg.eigh(proj.toarray()) print(vals) print("--------------") print("Only rotational sum rules (cmplt)") c = eigsh_projector(proj_rot, verbose=True) print(c.shape) print("--------------") print("Only translational and rotational sum rules") proj = scipy.sparse.identity(NN33) - proj_sum - proj_rot c = eigsh_projector(proj, verbose=True) print(c.shape) c = c_trans.T @ c proj = dot_product_sparse(c, c.T, use_mkl=use_mkl) print(proj.shape) c = eigsh_projector(proj, verbose=True) print(c.shape) proj = scipy.sparse.identity(NN33 // n_lp) - c_rot @ c_rot.T - c_sum @ c_sum.T c = eigsh_projector(proj, verbose=True) print(c.shape) print("--------------") """ Compressed projector I - P^(c) P^(c) = n_a_compress_mat.T @ C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans @ n_a_compress_mat """ proj = scipy.sparse.identity(NN33 // n_lp) - c_rot @ c_rot.T c = eigsh_projector(proj, verbose=True) print(c.shape) n_a_compress_mat = np.sqrt(n_lp) * basis_set_fc2.compact_compression_matrix print(n_a_compress_mat.shape) # compress_mat = n_a_compress_mat @ basis_set_fc2.basis_set compress_mat = n_a_compress_mat print(compress_mat.shape) c = compress_mat.T @ c print(c.shape) proj = csr_array(c @ c.T) eigvecs = eigsh_projector(proj) print(eigvecs.shape) vals, vecs = np.linalg.eigh(proj.toarray()) print(vals) return proj_return if __name__ == "__main__": import argparse import signal import time signal.signal(signal.SIGINT, signal.SIG_DFL) parser = argparse.ArgumentParser() parser.add_argument("--poscar", type=str, default=None, help="poscar") parser.add_argument( "--supercell", nargs=3, type=int, default=None, help="Supercell size (diagonal components)", ) args = parser.parse_args() unitcell = Poscar(args.poscar).structure supercell = supercell_diagonal(unitcell, args.supercell) supercell = SymfcAtoms( numbers=supercell.types, cell=supercell.axis.T, scaled_positions=supercell.positions.T, ) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations n_lp, natom = trans_perms.shape symfc = Symfc(supercell, use_mkl=True, log_level=1).compute_basis_set(2) basis_set_fc2 = symfc.basis_set[2] n_a_compress_mat = np.sqrt(n_lp) * basis_set_fc2.compact_compression_matrix t1 = time.time() proj = complementary_compr_projector_rot_sum_rules_O2( supercell, trans_perms, n_a_compress_mat, use_mkl=True, ) # proj = scipy.sparse.identity(proj.shape[0]) - proj eigvecs = eigsh_projector_sumrule(proj, verbose=True) t2 = time.time() print("Elapsed time:", t2 - t1) print("Number of eigenvectors:", eigvecs.shape[1]) proj1 = complementary_compr_projector_rot_O2_test( supercell, trans_perms, basis_set_fc2, indep_atoms=[2], ) proj2 = complementary_compr_projector_rot_O2_test( supercell, trans_perms, basis_set_fc2, indep_atoms=[1, 6], ) # print(proj2 - proj1) symfc-1.6.0/src/symfc/utils/rotation_dev/run_rotation_tools_O3.py000066400000000000000000000233331511276756400253040ustar00rootroot00000000000000"""Matrix utility functions for setting rotational invariants.""" # from typing import Optional # import itertools import numpy as np import scipy from pypolymlp.core.interface_vasp import Poscar from pypolymlp.utils.structure_utils import supercell_diagonal from scipy.sparse import csr_array, vstack from symfc import Symfc from symfc.basis_sets import FCBasisSetO2, FCBasisSetO3 from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( get_lat_trans_compr_matrix_O2, get_lat_trans_decompr_indices, ) from symfc.utils.utils_O3 import ( get_lat_trans_compr_matrix_O3, get_lat_trans_decompr_indices_O3, ) # from symfc.utils.solver_funcs import get_batch_slice # from symfc.utils.cutoff_tools import FCCutoff # from symfc.utils.utils_O3 import get_atomic_lat_trans_decompr_indices_O3 try: from symfc.utils.eig_tools import dot_product_sparse except ImportError: pass def delta(a: int, b: int): """Delta function.""" if a == b: return 1 return 0 def _orthogonalize_constraints(positions_cartesian: np.ndarray): """Orthogonalize constraints derived from rotational invariance.""" natom = positions_cartesian.shape[0] N3 = 3 * natom C = np.zeros((N3, 3), dtype="double") C[1::3, 0] = -positions_cartesian[:, 0] C[2::3, 2] = positions_cartesian[:, 0] C[2::3, 1] = -positions_cartesian[:, 1] C[0::3, 0] = positions_cartesian[:, 1] C[0::3, 2] = -positions_cartesian[:, 2] C[1::3, 1] = positions_cartesian[:, 2] C23 = np.zeros((9 + N3 * 9, 27), dtype="double") col_begin = 0 for alpha, beta in itertools.product(range(3), range(3)): col_end = col_begin + 3 row = beta add = np.array([delta(alpha, 1), 0, -delta(alpha, 2)]) C23[row, col_begin:col_end] += add row = alpha * 3 add = np.array([delta(beta, 1), 0, -delta(beta, 2)]) C23[row, col_begin:col_end] += add row = 3 + beta add = np.array([-delta(alpha, 0), delta(alpha, 2), 0]) C23[row, col_begin:col_end] += add row = alpha * 3 + 1 add = np.array([-delta(beta, 0), delta(beta, 2), 0]) C23[row, col_begin:col_end] += add row = 6 + beta add = np.array([0, -delta(alpha, 1), delta(alpha, 0)]) C23[row, col_begin:col_end] += add row = alpha * 3 + 2 add = np.array([0, -delta(beta, 1), delta(beta, 0)]) C23[row, col_begin:col_end] += add col_begin = col_end k_ids = np.repeat(np.arange(natom), 3) * 27 gamma_ids = np.tile(np.arange(3), natom) row_const = k_ids + gamma_ids + 9 col_begin = 0 for alpha, beta in itertools.product(range(3), range(3)): row = row_const + (alpha * 9 + beta * 3) col_end = col_begin + 3 C23[row, col_begin:col_end] = C col_begin = col_end U, S, V = np.linalg.svd(C23, full_matrices=False) nonzero = np.abs(S) > 1e-15 C23 = U[:, nonzero] print(S) # assert np.linalg.matrix_rank(C23) == 27 # assert np.allclose(C23.T @ C23, np.eye(27)) return C23 def _get_complement_matrix(mat, natom, order=2, n_lp=None, decompr_idx=None): """Get complementary matrix.""" NN = natom**2 NN33 = 9 * NN N333 = 27 * natom NN333 = N333 * natom NNN333 = NN333 * natom if order == 2: n_row = 9 size1 = NN33 elif order == 3: n_row = N333 size1 = NNN333 n_col = mat.shape[1] data = np.tile(mat.T.reshape(-1), NN) col = np.repeat(np.arange(NN * n_col), n_row) row = [] for i, j in itertools.product(range(natom), range(natom)): # id must be examined. begin = i * n_row * natom + j * n_row end = begin + n_row row.extend(np.tile(np.arange(begin, end), n_col)) nonzero = np.abs(data) > 1e-15 row = np.array(row)[nonzero] col = np.array(col)[nonzero] data = data[nonzero] if decompr_idx is None: return csr_array((data, (row, col)), shape=(size1, NN * n_col), dtype="double") size_row = decompr_idx.shape[0] // n_lp return csr_array( (data / np.sqrt(n_lp), (decompr_idx[row], col)), shape=(size_row, NN * n_col), dtype="double", ) def complementary_compr_projector_rot( supercell: SymfcAtoms, trans_perms: np.ndarray, basis_set_fc2: FCBasisSetO2, basis_set_fc3: FCBasisSetO3, use_mkl: bool = False, ) -> csr_array: """Test function for setting rotational invariants.""" n_lp, natom = trans_perms.shape positions_cartesian = (supercell.scaled_positions) @ supercell.cell C23 = _orthogonalize_constraints(positions_cartesian) decompr_idx = get_lat_trans_decompr_indices(trans_perms) c_rot_fc2 = _get_complement_matrix( C23[:9], natom, order=2, decompr_idx=decompr_idx, n_lp=n_lp, ) decompr_idx = get_lat_trans_decompr_indices_O3(trans_perms) c_rot_fc3 = _get_complement_matrix( C23[9:], natom, order=3, decompr_idx=decompr_idx, n_lp=n_lp, ) c_rot_fc2 = dot_product_sparse( basis_set_fc2.compact_compression_matrix.T, c_rot_fc2, use_mkl=True, ) print(c_rot_fc2.shape) c_rot_fc2 = dot_product_sparse( csr_array(basis_set_fc2.basis_set.T), c_rot_fc2, use_mkl=True, ) print(c_rot_fc2.shape) c_rot_fc3 = dot_product_sparse( basis_set_fc3.compact_compression_matrix.T, c_rot_fc3, use_mkl=True, ) print(c_rot_fc3.shape) c_rot_fc3 = dot_product_sparse( csr_array(basis_set_fc3.basis_set.T), c_rot_fc3, use_mkl=True, ) print(c_rot_fc3.shape) c_rot = vstack([c_rot_fc2, c_rot_fc3]) proj = dot_product_sparse(c_rot, c_rot.T, use_mkl=use_mkl) return proj def complementary_compr_projector_rot_reference( supercell: SymfcAtoms, trans_perms: np.ndarray, basis_set_fc2: FCBasisSetO2, basis_set_fc3: FCBasisSetO3, ) -> csr_array: """Test function for setting rotational invariants.""" natom = len(supercell.numbers) positions_cartesian = (supercell.scaled_positions) @ supercell.cell C23 = _orthogonalize_constraints(positions_cartesian) c_rot_fc2 = _get_complement_matrix(C23[:9], natom, order=2) c_rot_fc3 = _get_complement_matrix(C23[9:], natom, order=3) c_rot = vstack([c_rot_fc2, c_rot_fc3]) correlation = c_rot.T @ c_rot diff = correlation - scipy.sparse.identity(c_rot.shape[1]) assert np.allclose(diff.data, 0.0) c_trans_fc2 = get_lat_trans_compr_matrix_O2(trans_perms) c_trans_fc3 = get_lat_trans_compr_matrix_O3(trans_perms) c_rot_fc2 = c_trans_fc2.T @ c_rot_fc2 c_rot_fc2 = dot_product_sparse( basis_set_fc2.compact_compression_matrix.T, c_rot_fc2, use_mkl=True, ) c_rot_fc2 = dot_product_sparse( csr_array(basis_set_fc2.basis_set.T), c_rot_fc2, use_mkl=True, ) c_rot_fc3 = c_trans_fc3.T @ c_rot_fc3 c_rot_fc3 = dot_product_sparse( basis_set_fc3.compact_compression_matrix.T, c_rot_fc3, use_mkl=True, ) c_rot_fc3 = dot_product_sparse( csr_array(basis_set_fc3.basis_set.T), c_rot_fc3, use_mkl=True, ) c_rot = vstack([c_rot_fc2, c_rot_fc3]) proj = dot_product_sparse(c_rot, c_rot.T, use_mkl=True) return proj def complementary_projector_rot_reference(supercell: SymfcAtoms) -> np.ndarray: """Test function for setting rotational invariants.""" natom = len(supercell.numbers) positions_cartesian = (supercell.scaled_positions) @ supercell.cell C23 = _orthogonalize_constraints(positions_cartesian) c_rot_fc2 = _get_complement_matrix(C23[:9], natom, order=2) c_rot_fc3 = _get_complement_matrix(C23[9:], natom, order=3) c_rot = vstack([c_rot_fc2, c_rot_fc3]) c_rot = c_rot.toarray() proj = c_rot @ c_rot.T return proj if __name__ == "__main__": import argparse import signal signal.signal(signal.SIGINT, signal.SIG_DFL) parser = argparse.ArgumentParser() parser.add_argument("--poscar", type=str, default=None, help="poscar") parser.add_argument( "--supercell", nargs=3, type=int, default=None, help="Supercell size (diagonal components)", ) parser.add_argument( "--order", type=int, default=3, help="FC order.", ) args = parser.parse_args() unitcell = Poscar(args.poscar).structure supercell = supercell_diagonal(unitcell, args.supercell) supercell = SymfcAtoms( numbers=supercell.types, cell=supercell.axis.T, scaled_positions=supercell.positions.T, ) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations symfc = Symfc(supercell, use_mkl=True, log_level=1).compute_basis_set(3) basis_set_fc2 = symfc.basis_set[2] basis_set_fc3 = symfc.basis_set[3] import time t1 = time.time() proj = complementary_compr_projector_rot( supercell, trans_perms, basis_set_fc2, basis_set_fc3, use_mkl=True, ) t2 = time.time() if len(supercell.numbers) < 17: proj_ref = complementary_compr_projector_rot_reference( supercell, trans_perms, basis_set_fc2, basis_set_fc3, ) assert np.allclose((proj_ref - proj).data, 0.0) t3 = time.time() print(t2 - t1, t3 - t2) proj = proj.toarray() print(proj.shape) eigvals, eigvecs = np.linalg.eigh(proj) # eigvecs = eigsh_projector(proj, verbose = True) np.set_printoptions(threshold=np.inf) print(eigvals) print(sum(eigvals)) print(eigvecs.shape) nonzero = np.isclose(eigvals, 1.0) print("Number of eigenvectors:", np.count_nonzero(nonzero)) symfc-1.6.0/src/symfc/utils/rotation_dev/run_test_O2.py000066400000000000000000000160071511276756400232030ustar00rootroot00000000000000"""Matrix utility functions for setting rotational invariants.""" import itertools import numpy as np import scipy from pypolymlp.core.interface_vasp import Poscar from pypolymlp.utils.structure_utils import supercell_diagonal from scipy.sparse import csr_array from symfc import Symfc from symfc.basis_sets.basis_sets_O2 import FCBasisSetO2 from symfc.spg_reps import SpgRepsBase from symfc.utils.eig_tools import eigsh_projector from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( get_lat_trans_compr_matrix, get_lat_trans_decompr_indices, ) def _orthogonalize_constraints(positions_cartesian: np.ndarray): """Orthogonalize constraints derived from rotational invariance.""" natom = positions_cartesian.shape[0] N3 = 3 * natom C = np.zeros((N3, 3), dtype="double") C[1::3, 0] = -positions_cartesian[:, 0] C[2::3, 2] = positions_cartesian[:, 0] C[2::3, 1] = -positions_cartesian[:, 1] C[0::3, 0] = positions_cartesian[:, 1] C[0::3, 2] = -positions_cartesian[:, 2] C[1::3, 1] = positions_cartesian[:, 2] proj_C = C @ np.linalg.inv(C.T @ C) @ C.T eigvals, eigvecs = np.linalg.eigh(proj_C) nonzero = np.isclose(eigvals, 1.0) C = eigvecs[:, nonzero] return C def complement_sum_rule(natom, decompr_idx=None, n_lp=None): """Construct sum rule.""" N33 = natom * 9 NN33 = natom**2 * 9 row = [] for j, alpha, beta in itertools.product(range(natom), range(3), range(3)): i = np.arange(natom) * N33 const_add = j * 9 + alpha * 3 + beta row.extend(i + const_add) col = np.repeat(np.arange(N33), natom) data = np.ones(len(col)) / np.sqrt(natom) if decompr_idx is not None: data /= np.sqrt(n_lp) c_sum = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, N33), dtype="double", ) else: c_sum = csr_array( (data, (row, col)), shape=(NN33, N33), dtype="double", ) return c_sum def complement_rotational_sum_rule(supercell, indep_atoms, decompr_idx=None, n_lp=None): """Get complement_rotational_sum_rule.""" positions_cartesian = (supercell.scaled_positions) @ supercell.cell positions_cartesian[:, 0] -= np.average(positions_cartesian[:, 0]) positions_cartesian[:, 1] -= np.average(positions_cartesian[:, 1]) positions_cartesian[:, 2] -= np.average(positions_cartesian[:, 2]) C2 = _orthogonalize_constraints(positions_cartesian) natom = positions_cartesian.shape[0] N3 = natom * 3 N33 = N3 * 3 NN33 = N3 * N3 n_expand = len(indep_atoms) * 3 n_col = n_expand * 3 data = np.tile(C2.T.reshape(-1), n_expand) col = np.repeat(np.arange(n_col), N3) row = [] ids_ialpha = (np.arange(natom) * N33)[:, None] + (np.arange(3) * 3)[None, :] ids_ialpha = ids_ialpha.reshape(-1) for j, beta in itertools.product(indep_atoms, range(3)): ids = ids_ialpha + (j * 9 + beta) row.extend(np.tile(ids, 3)) if decompr_idx is None: c_rot = csr_array((data, (row, col)), shape=(NN33, n_col), dtype="double") else: data /= np.sqrt(n_lp) c_rot = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, n_col), dtype="double", ) return c_rot def complementary_compr_projector_rot_O2_test( supercell: SymfcAtoms, trans_perms: np.ndarray, basis_set_fc2: FCBasisSetO2, use_mkl: bool = False, ) -> csr_array: """Test function for setting rotational invariants.""" n_lp, natom = trans_perms.shape N3 = natom * 3 N33 = N3 * 3 NN33 = N3 * N3 decompr_idx = get_lat_trans_decompr_indices(trans_perms) c_trans = get_lat_trans_compr_matrix(decompr_idx, natom, n_lp) # indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) # indep_atoms = [indep_atoms[0]] # if indep_atoms is None: indep_atoms = list(range(natom)) """ c_rot = complement_rotational_sum_rule(supercell, indep_atoms) c_sum = complement_sum_rule(natom) proj_rot = scipy.sparse.identity(NN33) - c_rot @ c_rot.T proj_sum = scipy.sparse.identity(NN33) - c_sum @ c_sum.T """ c_sum = complement_sum_rule(natom, decompr_idx=decompr_idx, n_lp=n_lp) # proj = scipy.sparse.identity(NN33 // n_lp) - c_sum @ c_sum.T # c = eigsh_projector(proj, verbose=True) print("-------------- Only rotational sum rules ---------------") c_rot = complement_rotational_sum_rule( supercell, indep_atoms, decompr_idx=decompr_idx, n_lp=n_lp, ) proj = scipy.sparse.identity(NN33 // n_lp) - c_rot @ c_rot.T - c_sum @ c_sum.T basis = eigsh_projector(proj, verbose=True) print(basis.shape) # vals, vecs = np.linalg.eigh(proj.toarray()) # print(vals) print(c_trans.shape) basis = c_trans @ basis print(basis.shape) proj_basis = basis @ basis.T positions_cartesian = (supercell.scaled_positions) @ supercell.cell C = np.zeros((N3, 3), dtype="double") C[1::3, 0] = -positions_cartesian[:, 0] C[2::3, 2] = positions_cartesian[:, 0] C[2::3, 1] = -positions_cartesian[:, 1] C[0::3, 0] = positions_cartesian[:, 1] C[0::3, 2] = -positions_cartesian[:, 2] C[1::3, 1] = positions_cartesian[:, 2] trial_vec = np.zeros(NN33, dtype="double") ids_ialpha = (np.arange(natom) * N33)[:, None] + (np.arange(3) * 3)[None, :] ids_ialpha = ids_ialpha.reshape(-1) for gamma in range(3): for j in range(natom): for beta in range(3): ids = ids_ialpha + (j * 9 + beta) trial_vec[ids] = C[:, gamma] projected_vec = proj_basis @ trial_vec print(projected_vec[np.abs(projected_vec) > 1e-14]) return proj if __name__ == "__main__": import argparse import signal signal.signal(signal.SIGINT, signal.SIG_DFL) parser = argparse.ArgumentParser() parser.add_argument("--poscar", type=str, default=None, help="poscar") parser.add_argument( "--supercell", nargs=3, type=int, default=None, help="Supercell size (diagonal components)", ) args = parser.parse_args() unitcell = Poscar(args.poscar).structure supercell = supercell_diagonal(unitcell, args.supercell) supercell = SymfcAtoms( numbers=supercell.types, cell=supercell.axis.T, scaled_positions=supercell.positions.T, ) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations # n_lp, natom = trans_perms.shape symfc = Symfc(supercell, use_mkl=True, log_level=1).compute_basis_set(2) basis_set_fc2 = symfc.basis_set[2] # n_a_compress_mat = np.sqrt(n_lp) * basis_set_fc2.compact_compression_matrix proj1 = complementary_compr_projector_rot_O2_test( supercell, trans_perms, basis_set_fc2, ) # proj2 = complementary_compr_projector_rot_O2_test( # supercell, # trans_perms, # basis_set_fc2, # indep_atoms = [1, 6], # ) # print(proj2 - proj1) symfc-1.6.0/src/symfc/utils/rotation_tools_O2.py000066400000000000000000000060471511276756400217250ustar00rootroot00000000000000"""Matrix utility functions for setting rotational invariants.""" # from typing import Optional # import itertools from typing import Union import numpy as np from scipy.sparse import csr_array from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( get_lat_trans_decompr_indices, ) # try: # from symfc.utils.matrix import dot_product_sparse # except ImportError: # pass def _orthogonalize_constraints(positions_cartesian: np.ndarray): """Orthogonalize constraints derived from rotational invariance.""" natom = positions_cartesian.shape[0] N3 = 3 * natom # Eliminate translational sum rules positions_cartesian[:, 0] -= np.average(positions_cartesian[:, 0]) positions_cartesian[:, 1] -= np.average(positions_cartesian[:, 1]) positions_cartesian[:, 2] -= np.average(positions_cartesian[:, 2]) C = np.zeros((N3, 3), dtype="double") C[1::3, 0] = -positions_cartesian[:, 0] C[2::3, 2] = positions_cartesian[:, 0] C[2::3, 1] = -positions_cartesian[:, 1] C[0::3, 0] = positions_cartesian[:, 1] C[0::3, 2] = -positions_cartesian[:, 2] C[1::3, 1] = positions_cartesian[:, 2] proj_C = C @ np.linalg.inv(C.T @ C) @ C.T eigvals, eigvecs = np.linalg.eigh(proj_C) nonzero = np.isclose(eigvals, 1.0) C = eigvecs[:, nonzero] return C def complementary_compr_projector_rot_sum_rules_O2( supercell: SymfcAtoms, trans_perms: np.ndarray, n_a_compress_mat: Union[np.ndarray, csr_array], use_mkl: bool = False, ) -> csr_array: """Test function for setting rotational invariants.""" # atomic_decompr_idx: Optional[np.ndarray] = None, # fc_cutoff: Optional[FCCutoff] = None, n_lp, natom = trans_perms.shape # TODO: decompr_idx -> atomic_decompr_idx decompr_idx = get_lat_trans_decompr_indices(trans_perms) indep_atoms = list(range(natom)) positions_cartesian = (supercell.scaled_positions) @ supercell.cell C2 = _orthogonalize_constraints(positions_cartesian) N3 = natom * 3 N33 = N3 * 3 NN33 = N3 * N3 n_expand = len(indep_atoms) * 3 n_col = n_expand * 3 data = np.tile(C2.T.reshape(-1), n_expand) col = np.repeat(np.arange(n_col), N3) row = [] ids_ialpha = (np.arange(natom) * N33)[:, None] + (np.arange(3) * 3)[None, :] ids_ialpha = ids_ialpha.reshape(-1) for j, beta in itertools.product(indep_atoms, range(3)): ids = ids_ialpha + (j * 9 + beta) row.extend(np.tile(ids, 3)) data /= np.sqrt(n_lp) c_rot_cmplt = csr_array( (data, (decompr_idx[row], col)), shape=(NN33 // n_lp, n_col), dtype="double", ) c_rot_cmplt = n_a_compress_mat.T @ c_rot_cmplt p_rot_cmplt = c_rot_cmplt @ c_rot_cmplt.T return p_rot_cmplt """ Another option proj = scipy.sparse.identity(NN33 // n_lp) - c_rot_cmplt @ c_rot_cmplt.T c_rot = eigsh_projector(proj, verbose=True) c_rot = n_a_compress_mat.T @ c_rot proj_rot = c_rot @ c_rot.T Then, proj_rot can be used as proj = proj @ proj_rot @ proj eigvecs = eigsh(proj) """ symfc-1.6.0/src/symfc/utils/solver_funcs.py000066400000000000000000000041341511276756400210110ustar00rootroot00000000000000"""Solver functions.""" import numpy as np from scipy.linalg.lapack import get_lapack_funcs from scipy.sparse import csr_array def solve_linear_equation(A: np.ndarray, b: np.ndarray): """Solve linear equation using lapack in scipy. numpy and scipy implementations x = np.linalg.solve(A, b) x = scipy.linalg.solve(A, b, check_finite=False, assume_a='pos') """ (posv,) = get_lapack_funcs(("posv",), (A, b)) _, x, _ = posv(A, b, lower=False, overwrite_a=False, overwrite_b=False) return x def fit(X: np.ndarray, y: np.ndarray): """Solve a normal equation in least-squares. (X.T @ X) @ coefs = X.T @ y n_samples, n_features = X.shape """ coefs = solve_linear_equation(X.T @ X, X.T @ y) return coefs def get_batch_slice(n_data: int, batch_size: int): """Calculate slice indices for a given batch size.""" begin_batch = list(range(0, n_data, batch_size)) if len(begin_batch) > 1: end_batch = list(begin_batch[1:]) + [n_data] if (end_batch[-1] - end_batch[-2]) < batch_size // 5: end_batch[-2] = end_batch[-1] begin_batch = begin_batch[:-1] end_batch = end_batch[:-1] else: end_batch = [n_data] return begin_batch, end_batch def get_displacement_sparse_matrix( atoms: np.ndarray, displacements: np.ndarray, n_atom: int, tol: float = 1e-15, ) -> csr_array: """Return sparse matrix with displacements. Parameter --------- atoms: Indices of atoms displaced, shape = (n_snapshot). displacements: Displacement vectors, shape = (n_snapshot, 3). n_atom: Number of atoms in structure. tol: Tolerance value for defining nonzero elements. """ if atoms.shape[0] != displacements.shape[0]: raise RuntimeError("Sizes of atoms and displacements are inconsistent.") N3 = n_atom * 3 nonzero = np.abs(displacements) > tol rows, cols = np.where(nonzero) cols += atoms[rows] * 3 mat = csr_array( (displacements[nonzero], (rows, cols)), shape=(displacements.shape[0], N3), dtype="double", ) return mat symfc-1.6.0/src/symfc/utils/translation_tools_O1.py000066400000000000000000000031121511276756400224110ustar00rootroot00000000000000"""Functions for introducing translational invariance in 1st order force constants.""" import numpy as np import scipy from scipy.sparse import csr_array def compressed_projector_sum_rules( compress_mat: csr_array, N: int, use_mkl: bool = False ): """Return projection matrix for sum rule compressed by C.""" proj_cplmt = _compressed_complement_projector_sum_rules( compress_mat, N, use_mkl=use_mkl ) return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt def _compressed_complement_projector_sum_rules_algo1( compress_mat: csr_array, N: int, use_mkl: bool = False ): r"""Return complementary projection matrix for sum rule compressed by C. proj_sum_cplmt = [C.T @ Csum(c)] @ [Csum(c).T @ C] = c_sum_cplmt_compr.T @ c_sum_cplmt_compr Matrix shape of proj_sum_cplmt is (C.shape[1], C.shape[1]). C.shape[0] must be equal to NN33. Sum rules are given as sums over i: \sum_i \phi_{ia,jb} = 0 """ N3 = 3 * N row = np.arange(N3) col = np.tile(range(3), N) data = np.zeros(N3) data[:] = 1 / np.sqrt(N) c_sum_cplmt = csr_array((data, (row, col)), shape=(N3, 3)) c_sum_cplmt_compr = c_sum_cplmt.T @ compress_mat proj_sum_cplmt = c_sum_cplmt_compr.T @ c_sum_cplmt_compr return proj_sum_cplmt def _compressed_complement_projector_sum_rules( compress_mat: csr_array, N: int, use_mkl: bool = False ): """Return complementary projection matrix for sum rule compressed by C.""" return _compressed_complement_projector_sum_rules_algo1( compress_mat, N, use_mkl=use_mkl ) symfc-1.6.0/src/symfc/utils/translation_tools_O2.py000066400000000000000000000245171511276756400224260ustar00rootroot00000000000000"""Functions for introducing translational invariance in 2nd order force constants.""" from typing import Optional import numpy as np import scipy from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O2 import _get_atomic_lat_trans_decompr_indices try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass def optimize_batch_size_sum_rules_O2(natom: int, n_batch: int): """Calculate batch size for constructing projector for sum rules.""" if n_batch > natom: raise ValueError("n_batch must be smaller than N.") batch_size = natom * (natom // n_batch) return batch_size def compressed_projector_sum_rules_O2( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: int = 1, use_mkl: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules efficiently using independent atom with respect to lattice translations. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O3 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N, 27N^2). I denotes the unit matrix of size (27N, 27N). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC2(i, j, a, b) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, a, b) to (a, b, j, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, j, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_N33] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_N33].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aN33) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NN9 = natom**2 * 9 NN = natom**2 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, natom)).T.reshape(-1) * 9 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) nonzero = np.zeros((natom, natom), dtype=bool) nonzero[indep_atoms, :] = True nonzero = nonzero.reshape(-1) if fc_cutoff is not None: nonzero_c = fc_cutoff.nonzero_atomic_indices_fc2() nonzero_c = nonzero_c.reshape((natom, natom)).T.reshape(-1) nonzero = nonzero & nonzero_c batch_size = optimize_batch_size_sum_rules_O2(natom, n_batch=n_batch) ab = np.arange(9) for begin, end in zip(*get_batch_slice(NN, batch_size)): size = end - begin size_vector = size * 9 size_row = size_vector // natom nonzero_b = nonzero[begin:end] size_data = np.count_nonzero(nonzero_b) * 9 if size_data == 0: continue decompr_idx_b = decompr_idx[begin:end][nonzero_b] c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 9)], (ab[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NN9 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt def compressed_projector_sum_rules_O2_stable( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: int = 1, use_mkl: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O3 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N, 27N^2). I denotes the unit matrix of size (27N, 27N). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC2(i, j, a, b) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, a, b) to (a, b, j, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, j, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_N33] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_N33].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aN33) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NN9 = natom**2 * 9 NN = natom**2 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, natom)).T.reshape(-1) * 9 if fc_cutoff is not None: nonzero = fc_cutoff.nonzero_atomic_indices_fc2() nonzero = nonzero.reshape((natom, natom)).T.reshape(-1) batch_size = optimize_batch_size_sum_rules_O2(natom, n_batch=n_batch) ab = np.arange(9) for begin, end in zip(*get_batch_slice(NN, batch_size)): size = end - begin size_vector = size * 9 size_row = size_vector // natom if fc_cutoff is None: c_sum_cplmt = csr_array( ( np.ones(size_vector, dtype="double"), ( np.repeat(np.arange(size_row), natom), (ab[:, None] + decompr_idx[begin:end][None, :]).reshape(-1), ), ), shape=(size_row, NN9 // n_lp), dtype="double", ) else: nonzero_b = nonzero[begin:end] decompr_idx_b = decompr_idx[begin:end][nonzero_b] size_data = np.count_nonzero(nonzero_b) * 9 c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 9)], (ab[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NN9 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= n_lp * natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt symfc-1.6.0/src/symfc/utils/translation_tools_O3.py000066400000000000000000000256501511276756400224260ustar00rootroot00000000000000"""Functions for introducing translational invariance in 3rd order force constants.""" from typing import Optional import numpy as np import scipy from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O3 import get_atomic_lat_trans_decompr_indices_O3 try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass def optimize_batch_size_sum_rules_O3(natom: int, n_batch: Optional[int] = None): """Calculate batch size for constructing projector for sum rules.""" if n_batch is None: if natom < 256: n_batch = natom // min(natom, 16) else: n_batch = natom // 4 if n_batch > natom: raise ValueError("n_batch must be smaller than N.") batch_size = natom**2 * (natom // n_batch) return batch_size def compressed_projector_sum_rules_O3( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules efficiently using independent atom with respect to lattice translations. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O3 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N^2, 27N^3). I denotes the unit matrix of size (27N^2, 27N^2). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC3(i, j, k, a, b, c) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, k, a, b, c) to (a, b, c, j, k, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, c, j, k, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_NN333] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_NN333].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aNN333) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NNN27 = natom**3 * 27 NNN = natom**3 NN = natom**2 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, NN)).T.reshape(-1) * 27 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) nonzero = np.zeros((natom, natom, natom), dtype=bool) nonzero[indep_atoms, :, :] = True nonzero = nonzero.reshape(-1) if fc_cutoff is not None: nonzero_c = fc_cutoff.nonzero_atomic_indices_fc3() nonzero_c = nonzero_c.reshape((natom, NN)).T.reshape(-1) nonzero = nonzero & nonzero_c batch_size = optimize_batch_size_sum_rules_O3(natom, n_batch=n_batch) abc = np.arange(27) for begin, end in zip(*get_batch_slice(NNN, batch_size)): size = end - begin size_vector = size * 27 size_row = size_vector // natom nonzero_b = nonzero[begin:end] size_data = np.count_nonzero(nonzero_b) * 27 if size_data == 0: continue if verbose: print("Complementary P (Sum rule):", str(end) + "/" + str(NNN), flush=True) decompr_idx_b = decompr_idx[begin:end][nonzero_b] c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 27)], (abc[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NNN27 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt def compressed_projector_sum_rules_O3_stable( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O3 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N^2, 27N^3). I denotes the unit matrix of size (27N^2, 27N^2). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC3(i, j, k, a, b, c) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, k, a, b, c) to (a, b, c, j, k, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, c, j, k, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_NN333] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_NN333].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aNN333) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NNN27 = natom**3 * 27 NNN = natom**3 NN = natom**2 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, NN)).T.reshape(-1) * 27 if fc_cutoff is not None: nonzero = fc_cutoff.nonzero_atomic_indices_fc3() nonzero = nonzero.reshape((natom, NN)).T.reshape(-1) batch_size = optimize_batch_size_sum_rules_O3(natom, n_batch=n_batch) abc = np.arange(27) for begin, end in zip(*get_batch_slice(NNN, batch_size)): if verbose: print("Complementary P (Sum rule):", str(end) + "/" + str(NNN), flush=True) size = end - begin size_vector = size * 27 size_row = size_vector // natom if fc_cutoff is None: c_sum_cplmt = csr_array( ( np.ones(size_vector, dtype="double"), ( np.repeat(np.arange(size_row), natom), (abc[:, None] + decompr_idx[begin:end][None, :]).reshape(-1), ), ), shape=(size_row, NNN27 // n_lp), dtype="double", ) else: nonzero_b = nonzero[begin:end] decompr_idx_b = decompr_idx[begin:end][nonzero_b] size_data = np.count_nonzero(nonzero_b) * 27 c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 27)], (abc[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NNN27 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= n_lp * natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt symfc-1.6.0/src/symfc/utils/translation_tools_O4.py000066400000000000000000000260001511276756400224150ustar00rootroot00000000000000"""Functions for introducing translational invariance in 4th order force constants.""" from typing import Optional import numpy as np import scipy from scipy.sparse import csr_array from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.solver_funcs import get_batch_slice from symfc.utils.utils import get_indep_atoms_by_lat_trans from symfc.utils.utils_O4 import get_atomic_lat_trans_decompr_indices_O4 try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass def optimize_batch_size_sum_rules_O4(natom: int, n_batch: Optional[int] = None): """Calculate batch size for constructing projector for sum rules.""" if n_batch is None: if natom < 32: n_batch = natom // min(natom, 8) else: n_batch = natom // 4 if n_batch > natom: raise ValueError("n_batch must be smaller than N.") batch_size = natom**3 * (natom // n_batch) return batch_size def compressed_projector_sum_rules_O4( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules efficiently using independent atom with respect to lattice translations. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O4 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N^3, 27N^4). I denotes the unit matrix of size (27N^3, 27N^3). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC4(i, j, k, l, a, b, c, d) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, k, l, a, b, c, d) to (a, b, c, d, j, k, l, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, c, d, j, k, l, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_NNN3333] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_NNN3333].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aNNN333) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NNNN81 = natom**4 * 81 NNNN = natom**4 NNN = natom**3 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, NNN)).T.reshape(-1) * 81 indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) nonzero = np.zeros((natom, natom, natom, natom), dtype=bool) nonzero[indep_atoms, :, :, :] = True nonzero = nonzero.reshape(-1) if fc_cutoff is not None: nonzero_c = fc_cutoff.nonzero_atomic_indices_fc4() nonzero_c = nonzero_c.reshape((natom, NNN)).T.reshape(-1) nonzero = nonzero & nonzero_c batch_size = optimize_batch_size_sum_rules_O4(natom, n_batch=n_batch) abcd = np.arange(81) for begin, end in zip(*get_batch_slice(NNNN, batch_size)): size = end - begin size_vector = size * 81 size_row = size_vector // natom nonzero_b = nonzero[begin:end] size_data = np.count_nonzero(nonzero_b) * 81 if size_data == 0: continue if verbose: print("Complementary P (Sum rule):", str(end) + "/" + str(NNNN), flush=True) decompr_idx_b = decompr_idx[begin:end][nonzero_b] c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 81)], (abcd[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NNNN81 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt def compressed_projector_sum_rules_O4_stable( trans_perms: np.ndarray, n_a_compress_mat: csr_array, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, n_batch: Optional[int] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: r"""Return projection matrix for translational sum rule. Calculate a compressed projector for translational sum rules. This compression is achieved using C_trans and n_a_compress_mat, without the need to allocate C_trans. The implementation utilizes get_atomic_lat_trans_decompr_indices_O4 to ensure efficient memory usage. Return ------ Compressed projector I - P^(c). I - P^(c) = n_a_compress_mat.T @ C_trans.T @ [I - C_sum^(c) @ C_sum^(c).T] @ C_trans @ n_a_compress_mat = I - [n_a_compress_mat.T @ C_trans.T @ C_sum^(c)] @ [C_sum^(c).T @ C_trans @ n_a_compress_mat] Algorithm --------- 1. C_sum^(c).T = [I, I, I, ...] of size (27N^3, 27N^4). I denotes the unit matrix of size (27N^3, 27N^3). C_sum^(c).T is composed of N unit matrices. In this representation, the translational sum rules are given by \sum_i FC4(i, j, k, l, a, b, c, d) = 0. 2. To divide the computation of a compressed projector into several batches, C_sum^(c) and C_trans are permuted from the index order of (i, j, k, l, a, b, c, d) to (a, b, c, d, j, k, l, i). This is represented by C_sum^(c).T @ C_trans = C_sum^(c).T @ S.T @ S @ C_trans, where S denotes the permutation matrix that changes the index order to (a, b, c, d, j, k, l, i). Using this permutation, the translational sum rules are represented as C_sum^(c).T @ S.T = [ [1_N.T, 0_N.T, 0_N.T, ...] [0_N.T, 1_N.T, 0_N.T, ...] [0_N.T, 0_N.T, 1_N.T, ...] ... ], where 1_N and 0_N are column vectors of size N with all elements equal to one and zero, respectively. (Example) C_sum^(c).T @ S.T = [ [1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 ...] [0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 ...] [0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 ...] ... ] In this function, the permutation is achieved by using matrix reshapes. 3. Set C_trans.T @ C_sum^(c) @ C_sum^(c).T @ C_trans = [(C_trans.T @ S.T) @ (S @ C_sum^(c))] @ [(C_sum^(c).T @ S.T) @ (S @ C_trans)] = [T_1, T_2, ..., T_NNN3333] @ (S @ C_sum^(c)) @ (C_sum^(c).T @ S.T) @ [T_1, T_2, ..., T_NNN3333].T = \sum_i t_i @ t_i.T, where t_i = \sum_c T_i[:, c]. t_i is represented by c_sum_cplmt.T in this function. T_i is the submatrix of size (N, n_aNNN333) of permuted C_trans. 4. Compute P^(c) = \sum_i (n_a_compress_mat.T @ t_i) @ (t_i.T @ n_a_compress_mat) 5. Compute P = I - P^(c) """ n_lp, natom = trans_perms.shape NNNN81 = natom**4 * 81 NNNN = natom**4 NNN = natom**3 proj_size = n_a_compress_mat.shape[1] # type: ignore proj_cplmt = csr_array((proj_size, proj_size), dtype="double") if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) decompr_idx = atomic_decompr_idx.reshape((natom, NNN)).T.reshape(-1) * 81 if fc_cutoff is not None: nonzero = fc_cutoff.nonzero_atomic_indices_fc4() nonzero = nonzero.reshape((natom, NNN)).T.reshape(-1) batch_size = optimize_batch_size_sum_rules_O4(natom, n_batch=n_batch) abcd = np.arange(81) for begin, end in zip(*get_batch_slice(NNNN, batch_size)): if verbose: print("Complementary P (Sum rule):", str(end) + "/" + str(NNNN), flush=True) size = end - begin size_vector = size * 81 size_row = size_vector // natom if fc_cutoff is None: c_sum_cplmt = csr_array( ( np.ones(size_vector, dtype="double"), ( np.repeat(np.arange(size_row), natom), (abcd[:, None] + decompr_idx[begin:end][None, :]).reshape(-1), ), ), shape=(size_row, NNNN81 // n_lp), dtype="double", ) else: nonzero_b = nonzero[begin:end] decompr_idx_b = decompr_idx[begin:end][nonzero_b] size_data = np.count_nonzero(nonzero_b) * 81 c_sum_cplmt = csr_array( ( np.ones(size_data, dtype="double"), ( np.repeat(np.arange(size_row), natom)[np.tile(nonzero_b, 81)], (abcd[:, None] + decompr_idx_b[None, :]).reshape(-1), ), ), shape=(size_row, NNNN81 // n_lp), dtype="double", ) c_sum_cplmt = dot_product_sparse(c_sum_cplmt, n_a_compress_mat, use_mkl=use_mkl) proj_cplmt += dot_product_sparse(c_sum_cplmt.T, c_sum_cplmt, use_mkl=use_mkl) proj_cplmt /= n_lp * natom return scipy.sparse.identity(proj_cplmt.shape[0]) - proj_cplmt symfc-1.6.0/src/symfc/utils/utils.py000066400000000000000000000262551511276756400174510ustar00rootroot00000000000000"""Utility functions.""" from __future__ import annotations from collections.abc import Sequence import numpy as np from numpy.typing import NDArray def get_indep_atoms_by_lat_trans(trans_perms: NDArray) -> NDArray: """Return independent atoms by lattice translation symmetry. Parameters ---------- trans_perms : NDArray Atom indices after lattice translations. shape=(lattice_translations, supercell_atoms) Returns ------- NDArray Independent atoms. shape=(n_indep_atoms_by_lattice_translation,), dtype=int """ unique_atoms: list[int] = [] assert np.array_equal(trans_perms[0, :], range(trans_perms.shape[1])) for i, perms in enumerate(trans_perms.T): is_found = False for j in unique_atoms: if j in perms: is_found = True break if not is_found: unique_atoms.append(i) return np.array(unique_atoms, dtype=int) def round_positions(positions: NDArray, tol: float = 1e-13, decimals: int = 5): """Round fractional coordinates of positions (-0.5 <= p < 0.5).""" positions_rint = positions - np.rint(positions) positions_rint[np.abs(positions_rint - 0.5) < tol] = -0.5 positions_rint = np.round(positions_rint, decimals) return positions_rint def argsort_positions(positions: NDArray, tol: float = 1e-13, decimals: int = 5): """Round and sort fractional coordinates of positions (-0.5 <= p < 0.5).""" positions_round = round_positions(positions, tol=tol, decimals=decimals) # Not needed part? positions_rint = 10**decimals * positions_round positions_rint = [tuple(p) for p in positions_rint.astype(int)] # Not needed part? (end) sorted_ids = sorted(range(positions.shape[0]), key=positions_rint.__getitem__) sorted_positions = positions_round[sorted_ids] return sorted_ids, sorted_positions def _find_optimal_decimals(positions: NDArray, tol: float = 1e-13): """Find optimal value of decimals used for sorting atoms.""" n_atom = positions.shape[0] for decimals in range(3, 15): positions_round = round_positions(positions, tol=tol, decimals=decimals) n_atom_uniq = len(set([tuple(pos) for pos in positions_round])) if n_atom_uniq == n_atom: return decimals raise RuntimeError("Optimal decimals not found.") def compute_sg_permutations( positions: NDArray, rotations: NDArray, translations: NDArray, lattice: NDArray, symprec: float = 1e-5, ) -> NDArray: """Compute permutations of atoms by space group operations in supercell. Permutations of atoms of pure translations and coset representatives are first computed. Then permutations of atoms of all the given space group operations are made as the combitation of these two permutations. Parameters ---------- positions : ndarray Fractional positions (like SymfcAtoms.scaled_positions) before applying the space group operation. rotations : ndarray Matrix (rotation) parts of space group operations. shape=(len(operations), 3, 3), dtype='intc' translations : ndarray Vector (translation) parts of space group operations. shape=(len(operations), 3), dtype='double' lattice : ndarray Basis vectors in column vectors (like SymfcAtoms.cell.T). symprec : float Symmetry tolerance of the distance unit. Returns ------- perms : ndarray shape=(len(translations), len(positions)), dtype='intc', order='C' """ trans_perms = [] pure_trans = [] n_atom = positions.shape[0] decimals = _find_optimal_decimals(positions) sorted_ids, sorted_positions = argsort_positions(positions, decimals=decimals) for r, t in zip(rotations, translations): if (r != np.eye(3, dtype=int)).any(): continue trans_positions = positions + t sorted_trans_ids, sorted_trans_positions = argsort_positions( trans_positions, decimals=decimals, ) if np.allclose(sorted_trans_positions - sorted_positions, 0.0): tp = np.zeros(n_atom, dtype=int) tp[sorted_trans_ids] = sorted_ids else: diffs = positions[None, :, :] - trans_positions[:, None, :] diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=2) rows, cols = np.where(dists < symprec) assert len(positions) == len(np.unique(rows)) == len(np.unique(cols)) tp = cols[np.argsort(rows)] trans_perms.append(tp) pure_trans.append(t) trans_perms = np.array(trans_perms, dtype=int) pure_trans = np.array(pure_trans) unique_r = [] unique_t = [] r2ur = [] unique_rotated_positions = [] for r, t in zip(rotations, translations): is_found = False for j, ur in enumerate(unique_r): if (r == ur).all(): is_found = True r2ur.append(j) break if not is_found: r2ur.append(len(unique_r)) unique_r.append(r) unique_t.append(t) unique_rotated_positions.append(positions @ r.T + t) unique_rotation_perms = [] for rotated_positions in unique_rotated_positions: diffs = positions[None, :, :] - rotated_positions[:, None, :] diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=2) rows, cols = np.where(dists < symprec) assert len(positions) == len(np.unique(rows)) == len(np.unique(cols)) unique_rotation_perms.append(cols[np.argsort(rows)]) unique_rotation_perms = np.array(unique_rotation_perms, dtype=int) out = [] for i, t in enumerate(translations): perms = unique_rotation_perms[r2ur[i]] lattice_trans = t - unique_t[r2ur[i]] diffs = pure_trans - lattice_trans diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=1) lat_trans_idx = np.where(dists < symprec) assert len(lat_trans_idx) == 1 out.append(trans_perms[lat_trans_idx[0], perms]) out = np.array(out, dtype="intc", order="C") return out def compute_sg_permutations_stable( positions: NDArray, rotations: NDArray, translations: NDArray, lattice: NDArray, symprec: float = 1e-5, ) -> NDArray: """Compute permutations of atoms by space group operations in supercell. Permutations of atoms of pure translations and coset representatives are first computed. Then permutations of atoms of all the given space group operations are made as the combitation of these two permutations. Parameters ---------- positions : ndarray Fractional positions (like SymfcAtoms.scaled_positions) before applying the space group operation. rotations : ndarray Matrix (rotation) parts of space group operations. shape=(len(operations), 3, 3), dtype='intc' translations : ndarray Vector (translation) parts of space group operations. shape=(len(operations), 3), dtype='double' lattice : ndarray Basis vectors in column vectors (like SymfcAtoms.cell.T). symprec : float Symmetry tolerance of the distance unit. Returns ------- perms : ndarray shape=(len(translations), len(positions)), dtype='intc', order='C' """ trans_perms = [] pure_trans = [] """Bottleneck part""" for r, t in zip(rotations, translations): if (r != np.eye(3, dtype=int)).any(): continue trans_positions = positions + t diffs = positions[None, :, :] - trans_positions[:, None, :] diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=2) rows, cols = np.where(dists < symprec) assert len(positions) == len(np.unique(rows)) == len(np.unique(cols)) trans_perms.append(cols[np.argsort(rows)]) pure_trans.append(t) trans_perms = np.array(trans_perms, dtype=int) pure_trans = np.array(pure_trans) unique_r = [] unique_t = [] r2ur = [] unique_rotated_positions = [] for r, t in zip(rotations, translations): is_found = False for j, ur in enumerate(unique_r): if (r == ur).all(): is_found = True r2ur.append(j) break if not is_found: r2ur.append(len(unique_r)) unique_r.append(r) unique_t.append(t) unique_rotated_positions.append(positions @ r.T + t) unique_rotation_perms = [] for rotated_positions in unique_rotated_positions: diffs = positions[None, :, :] - rotated_positions[:, None, :] diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=2) rows, cols = np.where(dists < symprec) assert len(positions) == len(np.unique(rows)) == len(np.unique(cols)) unique_rotation_perms.append(cols[np.argsort(rows)]) unique_rotation_perms = np.array(unique_rotation_perms, dtype=int) out = [] for i, t in enumerate(translations): perms = unique_rotation_perms[r2ur[i]] lattice_trans = t - unique_t[r2ur[i]] diffs = pure_trans - lattice_trans diffs -= np.rint(diffs) dists = np.linalg.norm(diffs @ lattice.T, axis=1) lat_trans_idx = np.where(dists < symprec) assert len(lat_trans_idx) == 1 out.append(trans_perms[lat_trans_idx[0], perms]) out = np.array(out, dtype="intc", order="C") return out class SymfcAtoms: """Class to represent crystal structure mimicing PhonopyAtoms.""" def __init__( self, numbers: Sequence[int] | NDArray, scaled_positions: Sequence[Sequence[float]] | NDArray, cell: Sequence[Sequence[float]] | NDArray, ): """Init method.""" self._cell = np.array(cell, dtype="double") if self._cell.shape != (3, 3): raise ValueError("cell must be 3x3 array.") self._scaled_positions = np.array(scaled_positions, dtype="double") if self._scaled_positions.ndim != 2 or self._scaled_positions.shape[1] != 3: raise ValueError("scaled_positions must be Nx3 array.") self._numbers = np.array(numbers, dtype="intc") if ( self._numbers.ndim != 1 or self._numbers.shape[0] != self._scaled_positions.shape[0] ): raise ValueError( "numbers must be 1D array with the same length as scaled_positions." ) def __len__(self) -> int: """Return number of atoms.""" return len(self.numbers) @property def cell(self) -> NDArray: """Setter and getter of basis vectors. For getter, copy is returned.""" return self._cell.copy() @property def scaled_positions(self) -> NDArray: """Setter and getter of scaled positions. For getter, copy is returned.""" return self._scaled_positions.copy() @property def numbers(self) -> NDArray: """Setter and getter of atomic numbers. For getter, copy is returned.""" return self._numbers.copy() def totuple(self) -> tuple[NDArray, NDArray, NDArray]: """Return (cell, scaled_position, numbers).""" return (self._cell, self._scaled_positions, self._numbers) symfc-1.6.0/src/symfc/utils/utils_O1.py000066400000000000000000000117701511276756400200040ustar00rootroot00000000000000"""Utility functions for 1st order force constants.""" import numpy as np from scipy.sparse import csr_array, kron from symfc.spg_reps import SpgRepsO1 from .utils import get_indep_atoms_by_lat_trans def get_lat_trans_decompr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by lat-trans-sym. Usage ----- vec[indices] of shape (n_a*3,) gives an array of shape=(N*3,). 1/sqrt(n_lp) must be multiplied manually after decompression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * 3 elements. shape=(N*3,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = N * 3 n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift = trans_perms[:, i_patom] * 3 for a in range(3): indices[index_shift + a] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to compress matrix by lat-trans-sym. Usage ----- vec[indices] of shape (N*3,) vec gives an array of shape=(n_a*3, n_lp). 1/sqrt(n_lp) must be multiplied manually after compression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. Returns ------- indices : ndarray shape=(n_a*N9, n_lp), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = N * 3 n = 0 indices = np.zeros((n_a * 3, n_lp), dtype="int_") for i_patom in indep_atoms: for a in range(3): indices[n, :] = trans_perms[:, i_patom] * 3 + a n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_matrix(decompr_idx: np.ndarray, N: int, n_lp: int) -> csr_array: """Return compression matrix by lattice translation symmetry. `decompr_idx` is obtained by `get_lat_trans_decompr_indices`. Matrix shape is (N3, n_a*3), where n_a is the number of independent atoms by lattice translation symmetry. Data order is (N, 3, n_a, 3) if it is in dense array. """ N3 = N * 3 compression_mat = csr_array( ( np.full(N3, 1 / np.sqrt(n_lp), dtype="double"), (np.arange(N3, dtype=int), decompr_idx), ), shape=(N3, N3 // n_lp), dtype="double", ) return compression_mat def _get_atomic_lat_trans_decompr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by atom-lat-trans-sym. This is atomic permutation only version of get_lat_trans_decompr_indices. Usage ----- vec[indices] of shape (n_a,) gives an array of shape=(N,). 1/sqrt(n_lp) must be multiplied manually after decompression. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattce points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N elements. shape=(N^2*,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_lp, N = trans_perms.shape size_row = N n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift = trans_perms[:, i_patom] indices[index_shift] = n n += 1 assert n * n_lp == size_row return indices def get_compr_coset_reps_sum(spg_reps: SpgRepsO1): """Return compressed projector of coset reps sum.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N * 3 // n_lp coset_reps_sum = csr_array(([], ([], [])), shape=(size, size), dtype="double") atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) C = csr_array( ( np.ones(N, dtype=int), (np.arange(N, dtype=int), atomic_decompr_idx), ), shape=(N, N // n_lp), ) factor = 1 / n_lp / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): mat = spg_reps.get_sigma1_rep(i) mat = mat @ C mat = C.T @ mat coset_reps_sum += kron(mat, spg_reps.r_reps[i] * factor) return coset_reps_sum symfc-1.6.0/src/symfc/utils/utils_O2.py000066400000000000000000000237431511276756400200100ustar00rootroot00000000000000"""Utility functions for 2nd order force constants.""" import itertools from typing import Optional import numpy as np from scipy.sparse import csr_array, kron from symfc.spg_reps import SpgRepsO2 from symfc.utils.cutoff_tools import FCCutoff from .utils import get_indep_atoms_by_lat_trans def get_lat_trans_decompr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by lat-trans-sym. Usage ----- vec[indices] of shape (n_a*N*9,) gives an array of shape=(N**2*9,). 1/sqrt(n_lp) must be multiplied manually after decompression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N9 elements. shape=(N^2*9,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = (N * 3) ** 2 n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N * 9 for j in range(N): index_shift = index_shift_i + trans_perms[:, j] * 9 for ab in range(9): indices[index_shift + ab] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to compress matrix by lat-trans-sym. Usage ----- vec[indices] of shape (N**2*9,) vec gives an array of shape=(n_a*N*9, n_lp). 1/sqrt(n_lp) must be multiplied manually after compression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray shape=(n_a*N9, n_lp), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = (N * 3) ** 2 n = 0 indices = np.zeros((n_a * N * 9, n_lp), dtype="int_") for i_patom in indep_atoms: for j in range(N): for ab in range(9): indices[n, :] = ( trans_perms[:, i_patom] * 9 * N + trans_perms[:, j] * 9 + ab ) n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_matrix(decompr_idx: np.ndarray, N: int, n_lp: int) -> csr_array: """Return compression matrix by lattice translation symmetry. `decompr_idx` is obtained by `get_lat_trans_decompr_indices`. Matrix shape is (NN33, n_a*N33), where n_a is the number of independent atoms by lattice translation symmetry. Data order is (N, N, 3, 3, n_a, N, 3, 3) if it is in dense array. """ NN9 = N**2 * 9 compression_mat = csr_array( ( np.full(NN9, 1 / np.sqrt(n_lp), dtype="double"), (np.arange(NN9, dtype=int), decompr_idx), ), shape=(NN9, NN9 // n_lp), dtype="double", ) return compression_mat def get_lat_trans_compr_matrix_O2(trans_perms: np.ndarray): """Return lat trans compression matrix.""" n_lp, N = trans_perms.shape decompr_idx = get_lat_trans_decompr_indices(trans_perms) c_trans = get_lat_trans_compr_matrix(decompr_idx, N, n_lp) return c_trans def get_perm_compr_matrix(natom: int) -> csr_array: """Return compression matrix by permutation symmetry. Parameters ---------- natom : int Number of atoms in supercell. Matrix shape is (NN33,(N*3)(N*3+1)/2). Non-zero only ijab and jiba column elements for ijab rows. Rows upper right NN33 matrix elements are selected for rows. For the computational performance, get_perm_compr_matrix is implemented in a tricky way effectively using numpy features, and so it is not easy to read. What is expected may be found reading _get_perm_compr_matrix_reference. """ N = natom A = np.transpose( np.arange(N**2 * 9).reshape(N, N, 3, 3), axes=(0, 2, 1, 3) ).reshape(N * 3, N * 3) ut = np.triu_indices_from(A, k=1) diag = np.diagonal(A) row = np.hstack((np.stack((A[ut], A.T[ut]), axis=1).ravel(), diag)) col = np.hstack( ( np.repeat(np.arange(len(ut[0]), dtype=int), 2), np.arange(len(ut[0]), len(ut[0]) + len(diag), dtype=int), ) ) data = np.hstack((np.full(len(ut[0]) * 2, np.sqrt(2) / 2), np.full(len(diag), 1))) return csr_array( (data, (row, col)), shape=(N**2 * 9, (N * 3 * (N * 3 + 1)) // 2), dtype="double", ) def _get_atomic_lat_trans_decompr_indices(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by atom-lat-trans-sym. This is atomic permutation only version of get_lat_trans_decompr_indices. Usage ----- vec[indices] of shape (n_a*N,) gives an array of shape=(N**2,). 1/sqrt(n_lp) must be multiplied manually after decompression. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N elements. shape=(N^2*,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_lp, N = trans_perms.shape size_row = N**2 n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N for j in range(N): index_shift = index_shift_i + trans_perms[:, j] indices[index_shift] = n n += 1 assert n * n_lp == size_row return indices def get_compr_coset_reps_sum(spg_reps: SpgRepsO2): """Return compressed projector of coset reps sum.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**2 * 9 // n_lp coset_reps_sum = csr_array(([], ([], [])), shape=(size, size), dtype="double") atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) C = csr_array( ( np.ones(N**2, dtype=int), (np.arange(N**2, dtype=int), atomic_decompr_idx), ), shape=(N**2, N**2 // n_lp), ) factor = 1 / n_lp / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): mat = spg_reps.get_sigma2_rep(i) mat = mat @ C mat = C.T @ mat coset_reps_sum += kron(mat, spg_reps.r_reps[i] * factor) return coset_reps_sum def _get_perm_compr_matrix_reference(natom: int) -> csr_array: """Return compression matrix by permutation symmetry. This is a reference implementation of get_perm_compr_matrix. The order of columns is difference from get_perm_compr_matrix, but it is OK if C.T@C is the same. Matrix shape is (NN33,(N*3)(N*3+1)/2). Non-zero only ijab and jiba column elements for ijab rows. Rows upper right NN33 matrix elements are selected for rows. """ def to_serial(i: int, a: int, j: int, b: int, natom: int) -> int: """Return NN33-1D index.""" return (i * 9 * natom) + (j * 9) + (a * 3) + b col, row, data = [], [], [] val = np.sqrt(2) / 2 size_row = natom**2 * 9 n = 0 for ia, jb in itertools.combinations_with_replacement(range(natom * 3), 2): i_i = ia // 3 i_a = ia % 3 i_j = jb // 3 i_b = jb % 3 col.append(n) row.append(to_serial(i_i, i_a, i_j, i_b, natom)) if i_i == i_j and i_a == i_b: data.append(1) else: data.append(val) col.append(n) row.append(to_serial(i_j, i_b, i_i, i_a, natom)) data.append(val) n += 1 if (natom * 3) % 2 == 1: assert (natom * 3) * ((natom * 3 + 1) // 2) == n, f"{natom}, {n}" else: assert ((natom * 3) // 2) * (natom * 3 + 1) == n, f"{natom}, {n}" return csr_array((data, (row, col)), shape=(size_row, n), dtype="double") def get_compr_coset_projector_O2( spg_reps: SpgRepsO2, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, c_pt: Optional[csr_array] = None, ) -> csr_array: """Return compr matrix of sum of coset reps.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**2 * 9 // n_lp if c_pt is None else c_pt.shape[1] # type: ignore coset_reps_sum = csr_array((size, size), dtype="double") if atomic_decompr_idx is None: _atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) else: _atomic_decompr_idx = atomic_decompr_idx if fc_cutoff is None: nonzero = None size_data = N**2 col = _atomic_decompr_idx else: nonzero = fc_cutoff.nonzero_atomic_indices_fc2() size_data = np.count_nonzero(nonzero) col = _atomic_decompr_idx[nonzero] factor = 1 / n_lp / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): permutation = spg_reps.get_sigma2_rep(i, nonzero=nonzero) mat = csr_array( ( np.ones(size_data, dtype="int_"), (_atomic_decompr_idx[permutation], col), # type: ignore ), shape=(N**2 // n_lp, N**2 // n_lp), dtype="int_", ) mat = kron(mat, spg_reps.r_reps[i] * factor) if c_pt is not None: mat = c_pt.T @ mat @ c_pt coset_reps_sum += mat return coset_reps_sum symfc-1.6.0/src/symfc/utils/utils_O3.py000066400000000000000000000205331511276756400200030ustar00rootroot00000000000000"""Utility functions for 3rd order force constants.""" from typing import Optional import numpy as np from scipy.sparse import csr_array, kron from symfc.spg_reps.spg_reps_O3 import SpgRepsO3 from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.utils import get_indep_atoms_by_lat_trans try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass def get_atomic_lat_trans_decompr_indices_O3(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by atom-lat-trans-sym. This is atomic permutation only version of get_lat_trans_decompr_indices. Usage ----- vec[indices] of shape (n_a*N*N,) gives an array of shape=(N**3,). 1/sqrt(n_lp) must be multiplied manually after decompression. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N * N elements. shape=(N**3,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_lp, N = trans_perms.shape size_row = N**3 n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N**2 for j in range(N): index_shift_j = index_shift_i + trans_perms[:, j] * N for k in range(N): index_shift = index_shift_j + trans_perms[:, k] indices[index_shift] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_decompr_indices_O3(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by lat-trans-sym. Usage ----- vec[indices] of shape (n_a*N*N*27,) gives an array of shape=(N**3*27,). 1/sqrt(n_lp) must be multiplied manually after decompression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N * N * 27 elements. shape=(N^3*27,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = 27 * N**3 trans_perms = trans_perms.astype("int_") n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N**2 * 27 for j in range(N): index_shift_j = index_shift_i + trans_perms[:, j] * N * 27 for k in range(N): index_shift = index_shift_j + trans_perms[:, k] * 27 for ab in range(27): indices[index_shift + ab] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_matrix_O3(trans_perms): """Return lat trans compression matrix.""" n_lp, N = trans_perms.shape decompr_idx = get_lat_trans_decompr_indices_O3(trans_perms) c_trans = _get_lat_trans_compr_matrix_O3(decompr_idx, N, n_lp) return c_trans def _get_lat_trans_compr_matrix_O3( decompr_idx: np.ndarray, N: int, n_lp: int ) -> csr_array: """Return compression matrix by lattice translation symmetry. `decompr_idx` is obtained by `get_lat_trans_decompr_indices`. Matrix shape is (NNN333, n_a*NN333), where n_a is the number of independent atoms by lattice translation symmetry. Data order is (N, N, N, 3, 3, 3, n_a, N, N, 3, 3, 3) if it is in dense array. """ NNN27 = N**3 * 27 compression_mat = csr_array( ( np.full(NNN27, 1 / np.sqrt(n_lp), dtype="double"), (np.arange(NNN27, dtype=int), decompr_idx), ), shape=(NNN27, NNN27 // n_lp), dtype="double", ) return compression_mat def get_compr_coset_projector_O3( spg_reps: SpgRepsO3, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, c_pt: Optional[csr_array] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: """Return compr matrix of sum of coset reps.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**3 * 27 // n_lp if c_pt is None else c_pt.shape[1] # type: ignore indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) nonzero_indep_atom = np.zeros(N**3, dtype=bool) atom_indices = np.arange(N**3) // N**2 for i in indep_atoms: nonzero_indep_atom[atom_indices == i] = True if fc_cutoff is None: nonzero = nonzero_indep_atom else: nonzero = fc_cutoff.nonzero_atomic_indices_fc3() nonzero = nonzero & nonzero_indep_atom size_data = np.count_nonzero(nonzero) col = atomic_decompr_idx[nonzero] n_cosets = min([int(np.sqrt(len(spg_reps.unique_rotation_indices))), 4]) cosets = [csr_array(([], ([], [])), shape=(size, size), dtype="double")] * n_cosets factor = 1 / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): if verbose: n_rot = len(spg_reps.unique_rotation_indices) print("Coset sum:", i + 1, "/", n_rot, flush=True) permutation = spg_reps.get_sigma3_rep(i, nonzero=nonzero) """Equivalent to mat = C.T @ spg_reps.get_sigma3_rep(i) @ C C: atomic_lat_trans_compr_mat, shape=(NNN, NNN/n_lp)""" mat = csr_array( ( np.ones(size_data, dtype="int_"), (atomic_decompr_idx[permutation], col), ), shape=(N**3 // n_lp, N**3 // n_lp), dtype="int_", ) mat = kron(mat, spg_reps.r_reps[i] * factor).tocsr() if c_pt is not None: mat = dot_product_sparse(c_pt.T, mat, use_mkl=use_mkl) mat = dot_product_sparse(mat, c_pt, use_mkl=use_mkl) cosets[i % n_cosets] += mat return sum(cosets) # type: ignore def get_compr_coset_projector_O3_stable( spg_reps: SpgRepsO3, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, c_pt: Optional[csr_array] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: """Return compr matrix of sum of coset reps.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**3 * 27 // n_lp if c_pt is None else c_pt.shape[1] # type: ignore if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) if fc_cutoff is None: nonzero = None size_data = N**3 col = atomic_decompr_idx else: nonzero = fc_cutoff.nonzero_atomic_indices_fc3() size_data = np.count_nonzero(nonzero) col = atomic_decompr_idx[nonzero] n_cosets = min([int(np.sqrt(len(spg_reps.unique_rotation_indices))), 4]) cosets = [csr_array(([], ([], [])), shape=(size, size), dtype="double")] * n_cosets factor = 1 / n_lp / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): if verbose: n_rot = len(spg_reps.unique_rotation_indices) print("Coset sum:", i + 1, "/", n_rot, flush=True) permutation = spg_reps.get_sigma3_rep(i, nonzero=nonzero) """Equivalent to mat = C.T @ spg_reps.get_sigma3_rep(i) @ C C: atomic_lat_trans_compr_mat, shape=(NNN, NNN/n_lp)""" mat = csr_array( ( np.ones(size_data, dtype="int_"), (atomic_decompr_idx[permutation], col), ), shape=(N**3 // n_lp, N**3 // n_lp), dtype="int_", ) mat = kron(mat, spg_reps.r_reps[i] * factor).tocsr() if c_pt is not None: mat = dot_product_sparse(c_pt.T, mat, use_mkl=use_mkl) mat = dot_product_sparse(mat, c_pt, use_mkl=use_mkl) cosets[i % n_cosets] += mat return sum(cosets) # type: ignore symfc-1.6.0/src/symfc/utils/utils_O4.py000066400000000000000000000211611511276756400200020ustar00rootroot00000000000000"""Utility functions for 4th order force constants.""" from typing import Optional import numpy as np from scipy.sparse import csr_array, kron from symfc.spg_reps import SpgRepsO4 from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.utils import get_indep_atoms_by_lat_trans try: from symfc.utils.matrix import dot_product_sparse except ImportError: pass def get_atomic_lat_trans_decompr_indices_O4(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by atom-lat-trans-sym. This is atomic permutation only version of get_lat_trans_decompr_indices. Usage ----- vec[indices] of shape (n_a*N*N*N,) gives an array of shape=(N**4,). 1/sqrt(n_lp) must be multiplied manually after decompression. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N * N * N elements. shape=(N**4,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_lp, N = trans_perms.shape size_row = N**4 n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N**3 for j in range(N): index_shift_j = index_shift_i + trans_perms[:, j] * N**2 for k in range(N): index_shift_k = index_shift_j + trans_perms[:, k] * N for ll in range(N): index_shift = index_shift_k + trans_perms[:, ll] indices[index_shift] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_decompr_indices_O4(trans_perms: np.ndarray) -> np.ndarray: """Return indices to de-compress compressed matrix by lat-trans-sym. Usage ----- vec[indices] of shape (n_a*N*N*N*81,) gives an array of shape=(N**4*81,). 1/sqrt(n_lp) must be multiplied manually after decompression to mimic get_lat_trans_compr_matrix. Parameters ---------- trans_perms : ndarray Permutation of atomic indices by lattice translational symmetry. dtype='intc'. shape=(n_l, N), where n_l and N are the numbers of lattice points and atoms in supercell. Returns ------- indices : ndarray Indices of n_a * N * N * N * 81 elements. shape=(N^4*81,), dtype='int_'. """ indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) n_a = len(indep_atoms) N = trans_perms.shape[1] n_lp = N // n_a size_row = 81 * N**4 trans_perms = trans_perms.astype("int_") n = 0 indices = np.zeros(size_row, dtype="int_") for i_patom in indep_atoms: index_shift_i = trans_perms[:, i_patom] * N**3 * 81 for j in range(N): index_shift_j = index_shift_i + trans_perms[:, j] * N**2 * 81 for k in range(N): index_shift_k = index_shift_j + trans_perms[:, k] * N * 81 for ll in range(N): index_shift = index_shift_k + trans_perms[:, ll] * 81 for ab in range(81): indices[index_shift + ab] = n n += 1 assert n * n_lp == size_row return indices def get_lat_trans_compr_matrix_O4(trans_perms): """Return lat trans compression matrix.""" n_lp, N = trans_perms.shape decompr_idx = get_lat_trans_decompr_indices_O4(trans_perms) c_trans = _get_lat_trans_compr_matrix_O4(decompr_idx, N, n_lp) return c_trans def _get_lat_trans_compr_matrix_O4( decompr_idx: np.ndarray, N: int, n_lp: int ) -> csr_array: """Return compression matrix by lattice translation symmetry. `decompr_idx` is obtained by `get_lat_trans_decompr_indices`. Matrix shape is (NNNN3333, n_a*NNN3333), where n_a is the number of independent atoms by lattice translation symmetry. Data order is (N, N, N, N, 3, 3, 3, 3, n_a, N, N, N, 3, 3, 3, 3) if it is in dense array. """ NNNN81 = N**4 * 81 compression_mat = csr_array( ( np.full(NNNN81, 1 / np.sqrt(n_lp), dtype="double"), (np.arange(NNNN81, dtype=int), decompr_idx), ), shape=(NNNN81, NNNN81 // n_lp), dtype="double", ) return compression_mat def get_compr_coset_projector_O4( spg_reps: SpgRepsO4, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, c_pt: Optional[csr_array] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: """Return compr projector of sum of coset reps.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**4 * 81 // n_lp if c_pt is None else c_pt.shape[1] # type: ignore indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) nonzero_indep_atom = np.zeros(N**4, dtype=bool) atom_indices = np.arange(N**4) // N**3 for i in indep_atoms: nonzero_indep_atom[atom_indices == i] = True if fc_cutoff is None: nonzero = nonzero_indep_atom else: nonzero = fc_cutoff.nonzero_atomic_indices_fc4() nonzero = nonzero & nonzero_indep_atom size_data = np.count_nonzero(nonzero) col = atomic_decompr_idx[nonzero] n_cosets = min([int(np.sqrt(len(spg_reps.unique_rotation_indices))), 4]) cosets = [csr_array(([], ([], [])), shape=(size, size), dtype="double")] * n_cosets factor = 1 / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): if verbose: n_rot = len(spg_reps.unique_rotation_indices) print("Coset sum:", i + 1, "/", n_rot, flush=True) permutation = spg_reps.get_sigma4_rep(i, nonzero=nonzero) """Equivalent to mat = C.T @ spg_reps.get_sigma4_rep(i) @ C C: atomic_lat_trans_compr_mat, shape=(NNNN, NNNN/n_lp)""" mat = csr_array( ( np.ones(size_data, dtype="int_"), (atomic_decompr_idx[permutation], col), ), shape=(N**4 // n_lp, N**4 // n_lp), dtype="int_", ) mat = kron(mat, spg_reps.r_reps[i] * factor).tocsr() if c_pt is not None: mat = dot_product_sparse(c_pt.T, mat, use_mkl=use_mkl) mat = dot_product_sparse(mat, c_pt, use_mkl=use_mkl) cosets[i % n_cosets] += mat return sum(cosets) # type: ignore def get_compr_coset_projector_O4_stable( spg_reps: SpgRepsO4, atomic_decompr_idx: Optional[np.ndarray] = None, fc_cutoff: Optional[FCCutoff] = None, c_pt: Optional[csr_array] = None, use_mkl: bool = False, verbose: bool = False, ) -> csr_array: """Return compr projector of sum of coset reps.""" trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape size = N**4 * 81 // n_lp if c_pt is None else c_pt.shape[1] # type: ignore if atomic_decompr_idx is None: atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) if fc_cutoff is None: nonzero = None size_data = N**4 col = atomic_decompr_idx else: nonzero = fc_cutoff.nonzero_atomic_indices_fc4() size_data = np.count_nonzero(nonzero) col = atomic_decompr_idx[nonzero] n_cosets = min([int(np.sqrt(len(spg_reps.unique_rotation_indices))), 4]) cosets = [csr_array(([], ([], [])), shape=(size, size), dtype="double")] * n_cosets factor = 1 / n_lp / len(spg_reps.unique_rotation_indices) for i, _ in enumerate(spg_reps.unique_rotation_indices): if verbose: n_rot = len(spg_reps.unique_rotation_indices) print("Coset sum:", i + 1, "/", n_rot, flush=True) permutation = spg_reps.get_sigma4_rep(i, nonzero=nonzero) """Equivalent to mat = C.T @ spg_reps.get_sigma4_rep(i) @ C C: atomic_lat_trans_compr_mat, shape=(NNNN, NNNN/n_lp)""" mat = csr_array( ( np.ones(size_data, dtype="int_"), (atomic_decompr_idx[permutation], col), ), shape=(N**4 // n_lp, N**4 // n_lp), dtype="int_", ) mat = kron(mat, spg_reps.r_reps[i] * factor).tocsr() if c_pt is not None: mat = dot_product_sparse(c_pt.T, mat, use_mkl=use_mkl) mat = dot_product_sparse(mat, c_pt, use_mkl=use_mkl) cosets[i % n_cosets] += mat return sum(cosets) # type: ignore symfc-1.6.0/src/symfc/version.py000066400000000000000000000000741511276756400166250ustar00rootroot00000000000000"""Version number of the package.""" __version__ = "1.6.0" symfc-1.6.0/tests/000077500000000000000000000000001511276756400140175ustar00rootroot00000000000000symfc-1.6.0/tests/NaCl-unitcell.yaml000066400000000000000000000022031511276756400173320ustar00rootroot00000000000000lattice: - [ 5.690301476175671, 0.000000000000000, 0.000000000000000 ] # a - [ 0.000000000000000, 5.690301476175671, 0.000000000000000 ] # b - [ 0.000000000000000, 0.000000000000000, 5.690301476175671 ] # c points: - symbol: Na # 1 coordinates: [ 0.000000000000000, 0.000000000000000, 0.000000000000000 ] mass: 22.989769 - symbol: Na # 2 coordinates: [ 0.000000000000000, 0.500000000000000, 0.500000000000000 ] mass: 22.989769 - symbol: Na # 3 coordinates: [ 0.500000000000000, 0.000000000000000, 0.500000000000000 ] mass: 22.989769 - symbol: Na # 4 coordinates: [ 0.500000000000000, 0.500000000000000, 0.000000000000000 ] mass: 22.989769 - symbol: Cl # 5 coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] mass: 35.453000 - symbol: Cl # 6 coordinates: [ 0.500000000000000, 0.000000000000000, 0.000000000000000 ] mass: 35.453000 - symbol: Cl # 7 coordinates: [ 0.000000000000000, 0.500000000000000, 0.000000000000000 ] mass: 35.453000 - symbol: Cl # 8 coordinates: [ 0.000000000000000, 0.000000000000000, 0.500000000000000 ] mass: 35.453000 symfc-1.6.0/tests/basis_sets/000077500000000000000000000000001511276756400161565ustar00rootroot00000000000000symfc-1.6.0/tests/basis_sets/test_basis_sets_O2.py000066400000000000000000000112751511276756400222740ustar00rootroot00000000000000"""Tests of FCBasisSetO2.""" from __future__ import annotations from pathlib import Path import numpy as np import pytest from symfc.basis_sets import FCBasisSetO2 from symfc.solvers.solver_O2 import FCSolverO2 from symfc.utils.utils import SymfcAtoms cwd = Path(__file__).parent def test_fc_basis_set_o2(): """Test symmetry adapted basis sets of FCBasisSetO2.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO2(supercell, log_level=1).run() np.testing.assert_allclose( np.sort(sbs.basis_set), [[-np.sqrt(2) / 2], [np.sqrt(2) / 2]] ) comp_mat = sbs.compression_matrix proj = comp_mat @ comp_mat.T np.testing.assert_allclose(proj.data, [1.0 / 6.0] * proj.size) ref_col = [0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0] ref_row = [0, 4, 8, 9, 13, 17, 18, 22, 26, 27, 31, 35] np.testing.assert_array_equal(comp_mat.tocoo().col, ref_col) np.testing.assert_array_equal(comp_mat.tocoo().row, ref_row) compact_comp_mat = sbs.compact_compression_matrix compact_proj = compact_comp_mat @ compact_comp_mat.T np.testing.assert_allclose(compact_proj.data, [1.0 / 6.0] * compact_proj.size) ref_col = [0, 0, 0, 1, 1, 1] ref_row = [0, 4, 8, 9, 13, 17] np.testing.assert_array_equal(compact_comp_mat.tocoo().col, ref_col) np.testing.assert_array_equal(compact_comp_mat.tocoo().row, ref_row) assert np.linalg.norm(sbs.basis_set) == pytest.approx(1.0) def test_fc_basis_set_o2_nacl222(cell_nacl_222: SymfcAtoms): """Test symmetry adapted basis sets of FCBasisSetO2 by nacl222.""" sbs = FCBasisSetO2(cell_nacl_222, log_level=1).run() assert sbs.basis_set.shape[0] == 33 assert sbs.basis_set.shape[1] == 31 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(0.96875) sbs = FCBasisSetO2(cell_nacl_222, cutoff=5.0, log_level=1).run() assert sbs.basis_set.shape[0] == 12 assert sbs.basis_set.shape[1] == 10 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(0.3125) def test_fc_basis_set_o2_wurtzite332(cell_wurtzite_332: SymfcAtoms): """Test symmetry adapted basis sets of FCBasisSetO2 by wurtzite332.""" sbs = FCBasisSetO2(cell_wurtzite_332, log_level=1).run() assert sbs.basis_set.shape[0] == 130 assert sbs.basis_set.shape[1] == 126 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(7) sbs = FCBasisSetO2(cell_wurtzite_332, cutoff=5.0, log_level=1).run() assert sbs.basis_set.shape[0] == 49 assert sbs.basis_set.shape[1] == 45 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(2.5) @pytest.mark.parametrize("is_compact_fc", [True, False]) def test_fc2_NaCl_222( ph_nacl_222: tuple[SymfcAtoms, np.ndarray, np.ndarray], is_compact_fc: bool ): """Test force constants by NaCl 64 atoms supercell.""" _assert_fc(ph_nacl_222, "NaCl_222", is_compact_fc) @pytest.mark.parametrize("is_compact_fc", [True, False]) def test_fc2_SnO2_223( ph_sno2_223: tuple[SymfcAtoms, np.ndarray, np.ndarray], is_compact_fc: bool ): """Test force constants by SnO2 72 atoms supercell.""" _assert_fc(ph_sno2_223, "SnO2_223", is_compact_fc) def test_fc2_SiO2_221(ph_sio2_221: tuple[SymfcAtoms, np.ndarray, np.ndarray]): """Test force constants by SiO2 36 atoms supercell.""" _assert_fc(ph_sio2_221, "SiO2_221") def test_fc2_GaN_442(ph_gan_442: tuple[SymfcAtoms, np.ndarray, np.ndarray]): """Test force constants by GaN 128 atoms supercell.""" _assert_fc(ph_gan_442, "GaN_442") def test_fc2_GaN_222(ph_gan_222: tuple[SymfcAtoms, np.ndarray, np.ndarray]): """Test force constants by GaN 32 atoms supercell.""" _assert_fc(ph_gan_222, "GaN_222") def _assert_fc( ph: tuple[SymfcAtoms, np.ndarray, np.ndarray], name: str, is_compact_fc: bool = True ): supercell, displacements, forces = ph basis_set = FCBasisSetO2(supercell, log_level=1).run() print(basis_set) fc_solver = FCSolverO2(basis_set, log_level=1).solve(displacements, forces) if is_compact_fc: fc = fc_solver.compact_fc # np.savetxt(f"compact_fc_{name}.xz", fc.ravel()) fc_ref = np.loadtxt(cwd / ".." / f"compact_fc_{name}.xz").reshape(fc.shape) else: fc = fc_solver.full_fc fc_ref = np.loadtxt(cwd / ".." / f"full_fc_{name}.xz").reshape(fc.shape) np.testing.assert_allclose(fc, fc_ref, atol=1e-6) symfc-1.6.0/tests/basis_sets/test_basis_sets_O3.py000066400000000000000000000155141511276756400222750ustar00rootroot00000000000000"""Tests of FCBasisSetO3.""" from pathlib import Path import numpy as np import pytest from symfc.basis_sets import FCBasisSetO2, FCBasisSetO3 from symfc.solvers import FCSolverO2O3 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O3 import get_lat_trans_compr_matrix_O3 cwd = Path(__file__).parent def test_fc_basis_set_o3(): """Test symmetry adapted basis sets of FCBasisSetO3.""" a = 5.4335600299999998 lattice = np.array([[a, 0, 0], [0, a, 0], [0, 0, a]]) positions = np.array( [ [0.875, 0.875, 0.875], [0.875, 0.375, 0.375], [0.375, 0.875, 0.375], [0.375, 0.375, 0.875], [0.125, 0.125, 0.125], [0.125, 0.625, 0.625], [0.625, 0.125, 0.625], [0.625, 0.625, 0.125], ] ) numbers = [1, 1, 1, 1, 1, 1, 1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO3(supercell, log_level=1).run() basis_ref = [ 0.671875, 0.656250, 0.765625, 1.000000, 0.750000, 0.875000, 0.562500, 0.765625, 0.718750, 0.671875, 1.000000, 0.875000, 0.875000, 0.625000, 0.750000, 0.562500, 0.875000, ] np.testing.assert_allclose( np.sort([v @ v for v in sbs.basis_set]), np.sort(basis_ref) ) assert np.linalg.norm(sbs.basis_set) ** 2 == pytest.approx(13.0) np.testing.assert_array_equal( sbs.compact_compression_matrix.tocoo().row[[0, 6, 9]], [5, 32, 42] ) norm = sbs.compression_matrix.data[[0, 6, 9]] @ [ -0.1443375672974064, 0.058925565098878946, 0.0833333333333333, ] norm /= np.linalg.norm(sbs.compression_matrix.data[[0, 6, 9]]) norm /= np.linalg.norm( [-0.1443375672974064, 0.058925565098878946, 0.0833333333333333] ) assert norm == pytest.approx(1.0) or norm == pytest.approx(-1.0) np.testing.assert_array_equal( sbs.compression_matrix.tocoo().row[[0, 6, 9]], [5, 32, 42] ) norm = sbs.compression_matrix.data[[0, 6, 9]] @ [ -0.1443375672974064, 0.058925565098878946, 0.0833333333333333, ] norm /= np.linalg.norm(sbs.compression_matrix.data[[0, 6, 9]]) norm /= np.linalg.norm( [-0.1443375672974064, 0.058925565098878946, 0.0833333333333333] ) assert norm == pytest.approx(1.0) or norm == pytest.approx(-1.0) lat_trans_compr_matrix_O3 = get_lat_trans_compr_matrix_O3( sbs.translation_permutations ) np.testing.assert_allclose(lat_trans_compr_matrix_O3.data, [0.5] * 13824) assert lat_trans_compr_matrix_O3.indices[-1] == 2726 def test_si_111_fc3(ph3_si_111_fc3: tuple[SymfcAtoms, np.ndarray, np.ndarray]): """Test fc2 and fc3 by Si-111-222 supercells and compared with ALM. This test with ALM is skipped when ALM is not installed. """ supercell, displacements, forces = ph3_si_111_fc3 basis_set_o2 = FCBasisSetO2(supercell, log_level=1).run() basis_set_o3 = FCBasisSetO3(supercell, log_level=1).run() fc_solver = FCSolverO2O3([basis_set_o2, basis_set_o3], log_level=1).solve( displacements, forces ) fc2, fc3 = fc_solver.compact_fc fc2_ref = np.loadtxt(cwd / ".." / "compact_fc_Si_111_fc3_2.xz").reshape(fc2.shape) fc3_ref = np.loadtxt(cwd / ".." / "compact_fc_Si_111_fc3_3.xz").reshape(fc3.shape) np.testing.assert_allclose(fc2_ref, fc2, atol=1e-6) np.testing.assert_allclose(fc3_ref, fc3, atol=1e-6) def test_fc_basis_set_o3_wurtzite(): """Test symmetry adapted basis sets of FCBasisSetO3.""" lattice = np.array( [ [3.786186160293827, 0, 0], [-1.893093080146913, 3.278933398271515, 0], [0, 0, 6.212678269409001], ] ) positions = np.array( [ [0.333333333333333, 0.666666666666667, 0.002126465711614], [0.666666666666667, 0.333333333333333, 0.502126465711614], [0.333333333333333, 0.666666666666667, 0.376316514288389], [0.666666666666667, 0.333333333333333, 0.876316514288389], ] ) numbers = [1, 1, 2, 2] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO3(supercell, log_level=1).run() assert sbs.basis_set.shape[0] == 40 assert sbs.basis_set.shape[1] == 18 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(18.0) sbs = FCBasisSetO3(supercell, cutoff=3.0, log_level=1).run() assert sbs.basis_set.shape[0] == 22 assert sbs.basis_set.shape[1] == 6 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(6.0) def test_fc_basis_set_o3_diamond(): """Test symmetry adapted basis sets of FCBasisSetO4.""" a = 5.4335600299999998 lattice = np.array([[a, 0, 0], [0, a, 0], [0, 0, a]]) positions = np.array( [ [0.875, 0.875, 0.875], [0.875, 0.375, 0.375], [0.375, 0.875, 0.375], [0.375, 0.375, 0.875], [0.125, 0.125, 0.125], [0.125, 0.625, 0.625], [0.625, 0.125, 0.625], [0.625, 0.625, 0.125], ] ) numbers = [1, 1, 1, 1, 1, 1, 1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO3(supercell, log_level=1).run() assert sbs.basis_set.shape[0] == 17 assert sbs.basis_set.shape[1] == 13 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(3.25) sbs = FCBasisSetO3(supercell, cutoff=3.5, log_level=1).run() assert sbs.basis_set.shape[0] == 5 assert sbs.basis_set.shape[1] == 3 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(0.75) def test_fc_basis_set_o3_wurtzite_332(cell_wurtzite_332: SymfcAtoms): """Test symmetry adapted basis sets of FCBasisSetO3.""" supercell = cell_wurtzite_332 """ sbs = FCBasisSetO3(supercell, cutoff=6.0, log_level=1).run() assert sbs.basis_set.shape[0] == 2162 assert sbs.basis_set.shape[1] == 1950 """ sbs = FCBasisSetO3(supercell, cutoff=5.0, log_level=1).run() assert sbs.basis_set.shape[0] == 698 assert sbs.basis_set.shape[1] == 569 sbs = FCBasisSetO3(supercell, cutoff=4.0, log_level=1).run() assert sbs.basis_set.shape[0] == 306 assert sbs.basis_set.shape[1] == 218 sbs = FCBasisSetO3(supercell, cutoff=3.8, log_level=1).run() assert sbs.basis_set.shape[0] == 270 assert sbs.basis_set.shape[1] == 187 sbs = FCBasisSetO3(supercell, cutoff=3.0, log_level=1).run() assert sbs.basis_set.shape[0] == 34 assert sbs.basis_set.shape[1] == 9 symfc-1.6.0/tests/basis_sets/test_basis_sets_O4.py000066400000000000000000000112271511276756400222730ustar00rootroot00000000000000"""Tests of FCBasisSetO3.""" from pathlib import Path import numpy as np import pytest from symfc.basis_sets import FCBasisSetO4 from symfc.utils.utils import SymfcAtoms cwd = Path(__file__).parent def test_fc_basis_set_o4(): """Test symmetry adapted basis sets of FCBasisSetO4.""" a = 5.4335600299999998 lattice = np.array([[a, 0, 0], [0, a, 0], [0, 0, a]]) positions = np.array( [ [0.875, 0.875, 0.875], [0.875, 0.375, 0.375], [0.375, 0.875, 0.375], [0.375, 0.375, 0.875], [0.125, 0.125, 0.125], [0.125, 0.625, 0.625], [0.625, 0.125, 0.625], [0.625, 0.625, 0.125], ] ) numbers = [1, 1, 1, 1, 1, 1, 1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO4(supercell, log_level=1).run() assert sbs.basis_set.shape[0] == 115 assert sbs.basis_set.shape[1] == 72 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(18.0) sbs = FCBasisSetO4(supercell, cutoff=3.5, log_level=1).run() assert sbs.basis_set.shape[0] == 11 assert sbs.basis_set.shape[1] == 2 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(0.5) def test_fc_basis_set_o4_wurtzite(): """Test symmetry adapted basis sets of FCBasisSetO4.""" lattice = np.array( [ [3.786186160293827, 0, 0], [-1.893093080146913, 3.278933398271515, 0], [0, 0, 6.212678269409001], ] ) positions = np.array( [ [0.333333333333333, 0.666666666666667, 0.002126465711614], [0.666666666666667, 0.333333333333333, 0.502126465711614], [0.333333333333333, 0.666666666666667, 0.376316514288389], [0.666666666666667, 0.333333333333333, 0.876316514288389], ] ) numbers = [1, 1, 2, 2] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) sbs = FCBasisSetO4(supercell, log_level=1).run() assert sbs.basis_set.shape[0] == 120 assert sbs.basis_set.shape[1] == 34 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(34.0) sbs = FCBasisSetO4(supercell, cutoff=3.0, log_level=1).run() assert sbs.basis_set.shape[0] == 34 assert sbs.basis_set.shape[1] == 2 compact_basis = sbs.compact_compression_matrix @ sbs.basis_set assert np.linalg.norm(compact_basis) ** 2 == pytest.approx(2.0) def test_fc_basis_set_o3_wurtzite_221(): """Test symmetry adapted basis sets of FCBasisSetO3.""" lattice = np.array( [ [7.572372320587654, 0.0, 0.0], [-3.786186160293826, 6.55786679654303, 0.0], [0.0, 0.0, 6.212678269409001], ] ) positions = np.array( [ [0.166666666666666, 0.333333333333333, 0.002126465711614], [0.666666666666667, 0.333333333333333, 0.002126465711614], [0.166666666666666, 0.833333333333333, 0.002126465711614], [0.666666666666667, 0.833333333333333, 0.002126465711614], [0.333333333333333, 0.166666666666666, 0.502126465711614], [0.833333333333333, 0.166666666666666, 0.502126465711614], [0.333333333333333, 0.666666666666667, 0.502126465711614], [0.833333333333333, 0.666666666666667, 0.502126465711614], [0.166666666666666, 0.333333333333333, 0.376316514288389], [0.666666666666667, 0.333333333333333, 0.376316514288389], [0.166666666666666, 0.833333333333333, 0.376316514288389], [0.666666666666667, 0.833333333333333, 0.376316514288389], [0.333333333333333, 0.166666666666666, 0.876316514288389], [0.833333333333333, 0.166666666666666, 0.876316514288389], [0.333333333333333, 0.666666666666667, 0.876316514288389], [0.833333333333333, 0.666666666666667, 0.876316514288389], ] ) numbers = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) """ sbs = FCBasisSetO4(supercell, cutoff=4.5, log_level=1).run() assert sbs.basis_set.shape[0] == 3733 assert sbs.basis_set.shape[1] == 2749 """ sbs = FCBasisSetO4(supercell, cutoff=4.0, log_level=1).run() assert sbs.basis_set.shape[0] == 948 assert sbs.basis_set.shape[1] == 515 sbs = FCBasisSetO4(supercell, cutoff=3.5, log_level=1).run() assert sbs.basis_set.shape[0] == 61 assert sbs.basis_set.shape[1] == 4 symfc-1.6.0/tests/basis_sets/test_basis_sets_base.py000066400000000000000000000006261511276756400227240ustar00rootroot00000000000000"""Tests of FCBasisSetBase.""" from __future__ import annotations import numpy as np import pytest from symfc.basis_sets import FCBasisSetBase from symfc.utils.utils import SymfcAtoms def test_base_fc_basis_set(ph_nacl_222: tuple[SymfcAtoms, np.ndarray, np.ndarray]): """Test that FCBasisSet can not be instantiate.""" with pytest.raises(TypeError): _ = FCBasisSetBase(ph_nacl_222[0]) symfc-1.6.0/tests/compact_fc_GaN_222.xz000066400000000000000000000110701511276756400176110ustar00rootroot000000000000007zXZִF!t/rm]Sq(W>%#3dȄ}u%)WS ȱD)[HSP_ȅQPoBc-Lě2FM$(}r^Q#igD>A`jRMH(kg,JKmWaAܟZ2(m 4Zc]R-g͒RLjg/ (yF"Y(#æ Zms뉏vV -(G̊?ːؖ8Y`H?s@q;BoZ. _\_OnW:糱OB#(gCGu%n ׄ;Nnkd'U9+3+ S-IGke"7uGLYgΰ}`1ci9"j k(E }r:0`%HWmGA&,,{M:ӭ;yjOK4AIRᢖ9n[]Fۡzۿ :|ڴ!kuѰ)i1W#>~^sYY7iBlI;%y(sQ~Dɵws5dQ3~v1VߒgʨTp-M!52sj䰄'cyȠ,f@!Y%ؔ \TWRxwb[W?h_aҪXeb o~阒Zdf&`,]84Az`l .jІC7!x9c6:diZ7zt3PZ-VܵAH*zͅ/9O2'$?{T~TLNe@jЫ-z)i0B`_2KDd=!RϧȰR+C[tQ\mv61*ebVw ,Tq‰ᰤ8a7$~4U 0bʖ39l=sA؜{w tUiU:sM-[}."Z^0WPo0X6\2kR,A$?H! fHko+N:*sqd,+͔Qy)\Wvgϛ:^si(`J -TB+_zd.BQʃ5}-d_os $/n!D܏Up3% 2dU:8f6OO>:S߈ņ!u' Qb>z հ~:e7 l%*:5B'` vNYw${ﳫi+NocyO#!(GgkbONtEVo[]>wY:\ & -؏.m8Ṧh*BZcICu%pLO^d,Uf N 7Ʋb־a&ߴ|[=gv 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EWyΰv,[Y"8hc_A^JM&dU"1tc/7WFY:B={AE[@doطj D||@tmX3R(u 0~a>$3xsvz|>{ntpMNv)(q6e`{" 6dXƬZ(&r0kK.2A-q49U5qj?Hv4UDE`20GoXdiLH ѻ?} b~^![)\m A!CMQc[=ü.F,gHM>QI y-}ůn7} S)!21*qpnļ NC׽ABzB]Gq.D~k) *C HvĕpC  &. =d 㭾ˁ4xfNS^@ ;bX/rnqixb?`qr#[߮f%ƣ9Y2tbYMRWDF^1c}o)c.:gBMmZՃ-eHQ {ۺ8U~E,ӯp/@Jqm/Ld~l0Ҵύ 2h;Z0֛g<D$|F\'JĕΫD6rt(olZ E9l-ċ9xcUu6}0.5_U 1 ZвF'w~5ʠ<^4Q\her myX›$,˖Fɕꎴ L L.b7CB xuC Xgݭ1j9d$tb#D畇P4 Y=@NTADiVOܪ[..>~<׀.uR2c{#}}j~W$IWNM zڕ_&y # 3Z8xTO%ZE^J,ZPU?r 痛_c &5kfHc)Tk38_~Y뻺0A"COtJP.YRDZ"Gqsw3f-Wz!ّ52Wbl Dc~~[硙ZW\O(M902{w׼CC:5}n3 {[ۊfos|QQH2 6S8Nav3e rDDx~8荘CZ@83FmDjta!܏H9`rx$ءZewXl<7(ģoT&7=A[X74>K =EQV`C&(~ݗbhȧTjW:zM~~s3~ 3Ʊ pl\\X, ;/T09#`uHDO[!l6D:/).8/!W3UP.j6vnW%ʅvXZؓGjmC:\B̤~&\mTߦ+dD[p6S'f6R~1 'N˗P0;b.- SymfcAtoms: """Return unitcell of NaCl.""" lattice = [ [5.690301476175671, 0.000000000000000, 0.000000000000000], [0.000000000000000, 5.690301476175671, 0.000000000000000], [0.000000000000000, 0.000000000000000, 5.690301476175671], ] points = [ [0.000000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.500000000000000, 0.500000000000000], [0.500000000000000, 0.000000000000000, 0.500000000000000], [0.500000000000000, 0.500000000000000, 0.000000000000000], [0.500000000000000, 0.500000000000000, 0.500000000000000], [0.500000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.500000000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 0.500000000000000], ] numbers = [11, 11, 11, 11, 17, 17, 17, 17] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) return cell @pytest.fixture(scope="session") def cell_gan_111() -> SymfcAtoms: """Return unitcell of GaN.""" lattice = [ [3.180765520000000, 0.000000000000000, 0.000000000000000], [-1.590382760000000, 2.754623740000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 5.182605840000000], ] points = [ [0.333333329999992, 0.666666669999983, 0.124191919999999], [0.666666669999991, 0.333333329999998, 0.624191919999998], [0.333333329999992, 0.666666669999983, 0.500808080000002], [0.666666669999991, 0.333333329999998, 0.000808080000000], ] numbers = [7, 7, 31, 31] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) return cell @pytest.fixture(scope="session") def cell_wurtzite_332() -> SymfcAtoms: """Return 3x3x2 supercell of wurtzite.""" lattice = [ [11.35855848088148, 0.0, 0.0], [-5.679279240440739, 9.836800194814545, 0.0], [0.0, 0.0, 12.425356538818003], ] positions = [ [0.111111111111111, 0.222222222222222, 0.001063232855807], [0.444444444444444, 0.222222222222222, 0.001063232855807], [0.777777777777778, 0.222222222222222, 0.001063232855807], [0.111111111111111, 0.555555555555556, 0.001063232855807], [0.444444444444444, 0.555555555555556, 0.001063232855807], [0.777777777777778, 0.555555555555556, 0.001063232855807], [0.111111111111111, 0.888888888888889, 0.001063232855807], [0.444444444444444, 0.888888888888889, 0.001063232855807], [0.777777777777778, 0.888888888888889, 0.001063232855807], [0.111111111111111, 0.222222222222222, 0.501063232855807], [0.444444444444444, 0.222222222222222, 0.501063232855807], [0.777777777777778, 0.222222222222222, 0.501063232855807], [0.111111111111111, 0.555555555555556, 0.501063232855807], [0.444444444444444, 0.555555555555556, 0.501063232855807], [0.777777777777778, 0.555555555555556, 0.501063232855807], [0.111111111111111, 0.888888888888889, 0.501063232855807], [0.444444444444444, 0.888888888888889, 0.501063232855807], [0.777777777777778, 0.888888888888889, 0.501063232855807], [0.222222222222222, 0.111111111111111, 0.251063232855807], [0.555555555555556, 0.111111111111111, 0.251063232855807], [0.888888888888889, 0.111111111111111, 0.251063232855807], [0.222222222222222, 0.444444444444444, 0.251063232855807], [0.555555555555556, 0.444444444444444, 0.251063232855807], [0.888888888888889, 0.444444444444444, 0.251063232855807], [0.222222222222222, 0.777777777777778, 0.251063232855807], [0.555555555555556, 0.777777777777778, 0.251063232855807], [0.888888888888889, 0.777777777777778, 0.251063232855807], [0.222222222222222, 0.111111111111111, 0.751063232855807], [0.555555555555556, 0.111111111111111, 0.751063232855807], [0.888888888888889, 0.111111111111111, 0.751063232855807], [0.222222222222222, 0.444444444444444, 0.751063232855807], [0.555555555555556, 0.444444444444444, 0.751063232855807], [0.888888888888889, 0.444444444444444, 0.751063232855807], [0.222222222222222, 0.777777777777778, 0.751063232855807], [0.555555555555556, 0.777777777777778, 0.751063232855807], [0.888888888888889, 0.777777777777778, 0.751063232855807], [0.111111111111111, 0.222222222222222, 0.188158257144195], [0.444444444444444, 0.222222222222222, 0.188158257144195], [0.777777777777778, 0.222222222222222, 0.188158257144195], [0.111111111111111, 0.555555555555556, 0.188158257144195], [0.444444444444444, 0.555555555555556, 0.188158257144195], [0.777777777777778, 0.555555555555556, 0.188158257144195], [0.111111111111111, 0.888888888888889, 0.188158257144195], [0.444444444444444, 0.888888888888889, 0.188158257144195], [0.777777777777778, 0.888888888888889, 0.188158257144195], [0.111111111111111, 0.222222222222222, 0.688158257144195], [0.444444444444444, 0.222222222222222, 0.688158257144195], [0.777777777777778, 0.222222222222222, 0.688158257144195], [0.111111111111111, 0.555555555555556, 0.688158257144195], [0.444444444444444, 0.555555555555556, 0.688158257144195], [0.777777777777778, 0.555555555555556, 0.688158257144195], [0.111111111111111, 0.888888888888889, 0.688158257144195], [0.444444444444444, 0.888888888888889, 0.688158257144195], [0.777777777777778, 0.888888888888889, 0.688158257144195], [0.222222222222222, 0.111111111111111, 0.438158257144194], [0.555555555555556, 0.111111111111111, 0.438158257144194], [0.888888888888889, 0.111111111111111, 0.438158257144194], [0.222222222222222, 0.444444444444444, 0.438158257144194], [0.555555555555556, 0.444444444444444, 0.438158257144194], [0.888888888888889, 0.444444444444444, 0.438158257144194], [0.222222222222222, 0.777777777777778, 0.438158257144194], [0.555555555555556, 0.777777777777778, 0.438158257144194], [0.888888888888889, 0.777777777777778, 0.438158257144194], [0.222222222222222, 0.111111111111111, 0.938158257144194], [0.555555555555556, 0.111111111111111, 0.938158257144194], [0.888888888888889, 0.111111111111111, 0.938158257144194], [0.222222222222222, 0.444444444444444, 0.938158257144194], [0.555555555555556, 0.444444444444444, 0.938158257144194], [0.888888888888889, 0.444444444444444, 0.938158257144194], [0.222222222222222, 0.777777777777778, 0.938158257144194], [0.555555555555556, 0.777777777777778, 0.938158257144194], [0.888888888888889, 0.777777777777778, 0.938158257144194], ] numbers = [1 for i in range(36)] + [2 for i in range(36)] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) return supercell @pytest.fixture(scope="session") def cell_nacl_222() -> SymfcAtoms: """Return 2x2x2 supercell of NaCl.""" lattice = [ [11.281120000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 11.281120000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 11.281120000000000], ] points = [ [0.000000000000000, 0.000000000000000, 0.000000000000000], [0.500000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.500000000000000, 0.000000000000000], [0.500000000000000, 0.500000000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 0.500000000000000], [0.500000000000000, 0.000000000000000, 0.500000000000000], [0.000000000000000, 0.500000000000000, 0.500000000000000], [0.500000000000000, 0.500000000000000, 0.500000000000000], [0.000000000000000, 0.250000000000000, 0.250000000000000], [0.500000000000000, 0.250000000000000, 0.250000000000000], [0.000000000000000, 0.750000000000000, 0.250000000000000], [0.500000000000000, 0.750000000000000, 0.250000000000000], [0.000000000000000, 0.250000000000000, 0.750000000000000], [0.500000000000000, 0.250000000000000, 0.750000000000000], [0.000000000000000, 0.750000000000000, 0.750000000000000], [0.500000000000000, 0.750000000000000, 0.750000000000000], [0.250000000000000, 0.000000000000000, 0.250000000000000], [0.750000000000000, 0.000000000000000, 0.250000000000000], [0.250000000000000, 0.500000000000000, 0.250000000000000], [0.750000000000000, 0.500000000000000, 0.250000000000000], [0.250000000000000, 0.000000000000000, 0.750000000000000], [0.750000000000000, 0.000000000000000, 0.750000000000000], [0.250000000000000, 0.500000000000000, 0.750000000000000], [0.750000000000000, 0.500000000000000, 0.750000000000000], [0.250000000000000, 0.250000000000000, 0.000000000000000], [0.750000000000000, 0.250000000000000, 0.000000000000000], [0.250000000000000, 0.750000000000000, 0.000000000000000], [0.750000000000000, 0.750000000000000, 0.000000000000000], [0.250000000000000, 0.250000000000000, 0.500000000000000], [0.750000000000000, 0.250000000000000, 0.500000000000000], [0.250000000000000, 0.750000000000000, 0.500000000000000], [0.750000000000000, 0.750000000000000, 0.500000000000000], [0.250000000000000, 0.250000000000000, 0.250000000000000], [0.750000000000000, 0.250000000000000, 0.250000000000000], [0.250000000000000, 0.750000000000000, 0.250000000000000], [0.750000000000000, 0.750000000000000, 0.250000000000000], [0.250000000000000, 0.250000000000000, 0.750000000000000], [0.750000000000000, 0.250000000000000, 0.750000000000000], [0.250000000000000, 0.750000000000000, 0.750000000000000], [0.750000000000000, 0.750000000000000, 0.750000000000000], [0.250000000000000, 0.000000000000000, 0.000000000000000], [0.750000000000000, 0.000000000000000, 0.000000000000000], [0.250000000000000, 0.500000000000000, 0.000000000000000], [0.750000000000000, 0.500000000000000, 0.000000000000000], [0.250000000000000, 0.000000000000000, 0.500000000000000], [0.750000000000000, 0.000000000000000, 0.500000000000000], [0.250000000000000, 0.500000000000000, 0.500000000000000], [0.750000000000000, 0.500000000000000, 0.500000000000000], [0.000000000000000, 0.250000000000000, 0.000000000000000], [0.500000000000000, 0.250000000000000, 0.000000000000000], [0.000000000000000, 0.750000000000000, 0.000000000000000], [0.500000000000000, 0.750000000000000, 0.000000000000000], [0.000000000000000, 0.250000000000000, 0.500000000000000], [0.500000000000000, 0.250000000000000, 0.500000000000000], [0.000000000000000, 0.750000000000000, 0.500000000000000], [0.500000000000000, 0.750000000000000, 0.500000000000000], [0.000000000000000, 0.000000000000000, 0.250000000000000], [0.500000000000000, 0.000000000000000, 0.250000000000000], [0.000000000000000, 0.500000000000000, 0.250000000000000], [0.500000000000000, 0.500000000000000, 0.250000000000000], [0.000000000000000, 0.000000000000000, 0.750000000000000], [0.500000000000000, 0.000000000000000, 0.750000000000000], [0.000000000000000, 0.500000000000000, 0.750000000000000], [0.500000000000000, 0.500000000000000, 0.750000000000000], ] numbers = [ 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, ] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) return cell @pytest.fixture(scope="session") def ph_nacl_222(cell_nacl_222) -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return NaCl-222 data.""" N = 20 dfset = np.loadtxt(cwd / "dfset_NaCl_222_rd.xz") d = dfset[:, :3].reshape(N, -1, 3) f = dfset[:, 3:].reshape(N, -1, 3) return cell_nacl_222, d, f @pytest.fixture(scope="session") def ph_sno2_223() -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return SnO2-223 data.""" lattice = [ [9.545162500000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 9.545162500000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 9.649138170000001], ] points = [ [0.096865350000001, 0.403134649999997, 0.166666666666667], [0.596865350000001, 0.403134649999997, 0.166666666666667], [0.096865350000001, 0.903134649999997, 0.166666666666667], [0.596865350000001, 0.903134649999997, 0.166666666666667], [0.096865350000001, 0.403134649999997, 0.500000000000000], [0.596865350000001, 0.403134649999997, 0.500000000000000], [0.096865350000001, 0.903134649999997, 0.500000000000000], [0.596865350000001, 0.903134649999997, 0.500000000000000], [0.096865350000001, 0.403134649999997, 0.833333333333333], [0.596865350000001, 0.403134649999997, 0.833333333333333], [0.096865350000001, 0.903134649999997, 0.833333333333333], [0.596865350000001, 0.903134649999997, 0.833333333333333], [0.403134649999997, 0.096865350000001, 0.166666666666667], [0.903134649999997, 0.096865350000001, 0.166666666666667], [0.403134649999997, 0.596865350000001, 0.166666666666667], [0.903134649999997, 0.596865350000001, 0.166666666666667], [0.403134649999997, 0.096865350000001, 0.500000000000000], [0.903134649999997, 0.096865350000001, 0.500000000000000], [0.403134649999997, 0.596865350000001, 0.500000000000000], [0.903134649999997, 0.596865350000001, 0.500000000000000], [0.403134649999997, 0.096865350000001, 0.833333333333333], [0.903134649999997, 0.096865350000001, 0.833333333333333], [0.403134649999997, 0.596865350000001, 0.833333333333333], [0.903134649999997, 0.596865350000001, 0.833333333333333], [0.153134649999998, 0.153134649999998, 0.000000000000000], [0.653134649999997, 0.153134649999998, 0.000000000000000], [0.153134649999998, 0.653134649999997, 0.000000000000000], [0.653134649999997, 0.653134649999997, 0.000000000000000], [0.153134649999998, 0.153134649999998, 0.333333333333333], [0.653134649999997, 0.153134649999998, 0.333333333333333], [0.153134649999998, 0.653134649999997, 0.333333333333333], [0.653134649999997, 0.653134649999997, 0.333333333333333], [0.153134649999998, 0.153134649999998, 0.666666666666667], [0.653134649999997, 0.153134649999998, 0.666666666666667], [0.153134649999998, 0.653134649999997, 0.666666666666667], [0.653134649999997, 0.653134649999997, 0.666666666666667], [0.346865350000003, 0.346865350000003, 0.000000000000000], [0.846865350000003, 0.346865350000003, 0.000000000000000], [0.346865350000003, 0.846865350000003, 0.000000000000000], [0.846865350000003, 0.846865350000003, 0.000000000000000], [0.346865350000003, 0.346865350000003, 0.333333333333333], [0.846865350000003, 0.346865350000003, 0.333333333333333], [0.346865350000003, 0.846865350000003, 0.333333333333333], [0.846865350000003, 0.846865350000003, 0.333333333333333], [0.346865350000003, 0.346865350000003, 0.666666666666667], [0.846865350000003, 0.346865350000003, 0.666666666666667], [0.346865350000003, 0.846865350000003, 0.666666666666667], [0.846865350000003, 0.846865350000003, 0.666666666666667], [0.250000000000000, 0.250000000000000, 0.166666666666667], [0.750000000000000, 0.250000000000000, 0.166666666666667], [0.250000000000000, 0.750000000000000, 0.166666666666667], [0.750000000000000, 0.750000000000000, 0.166666666666667], [0.250000000000000, 0.250000000000000, 0.500000000000000], [0.750000000000000, 0.250000000000000, 0.500000000000000], [0.250000000000000, 0.750000000000000, 0.500000000000000], [0.750000000000000, 0.750000000000000, 0.500000000000000], [0.250000000000000, 0.250000000000000, 0.833333333333333], [0.750000000000000, 0.250000000000000, 0.833333333333333], [0.250000000000000, 0.750000000000000, 0.833333333333333], [0.750000000000000, 0.750000000000000, 0.833333333333333], [0.000000000000000, 0.000000000000000, 0.000000000000000], [0.500000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.500000000000000, 0.000000000000000], [0.500000000000000, 0.500000000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 0.333333333333333], [0.500000000000000, 0.000000000000000, 0.333333333333333], [0.000000000000000, 0.500000000000000, 0.333333333333333], [0.500000000000000, 0.500000000000000, 0.333333333333333], [0.000000000000000, 0.000000000000000, 0.666666666666667], [0.500000000000000, 0.000000000000000, 0.666666666666667], [0.000000000000000, 0.500000000000000, 0.666666666666667], [0.500000000000000, 0.500000000000000, 0.666666666666667], ] numbers = [ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, ] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) N = 40 dfset = np.loadtxt(cwd / "dfset_SnO2_223_rd.xz") d = dfset[:, :3].reshape(N, -1, 3) f = dfset[:, 3:].reshape(N, -1, 3) return cell, d, f @pytest.fixture(scope="session") def ph_sio2_221() -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return SiO2-221 data.""" lattice = [ [9.919749440000000, 0.000000000000000, 0.000000000000000], [-4.959874720000000, 8.590755020000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 5.453559970000000], ] points = [ [0.293442579999996, 0.367112664999997, 0.213280300000002], [0.793442579999996, 0.367112664999997, 0.213280300000002], [0.293442579999996, 0.867112664999997, 0.213280300000002], [0.793442579999996, 0.867112664999997, 0.213280300000002], [0.073670084999997, 0.206557419999994, 0.879946960000002], [0.573670084999997, 0.206557419999994, 0.879946960000002], [0.073670084999997, 0.706557419999994, 0.879946960000002], [0.573670084999997, 0.706557419999994, 0.879946960000002], [0.132887334999998, 0.426329914999998, 0.546613630000002], [0.632887334999998, 0.426329914999998, 0.546613630000002], [0.132887334999998, 0.926329914999998, 0.546613630000002], [0.632887334999998, 0.926329914999998, 0.546613630000002], [0.367112665000005, 0.293442580000006, 0.786719699999998], [0.867112665000005, 0.293442580000006, 0.786719699999998], [0.367112665000005, 0.793442580000006, 0.786719699999998], [0.867112665000005, 0.793442580000006, 0.786719699999998], [0.426329914999999, 0.132887335000003, 0.453386369999998], [0.926329914999999, 0.132887335000003, 0.453386369999998], [0.426329914999999, 0.632887335000003, 0.453386369999998], [0.926329914999999, 0.632887335000003, 0.453386369999998], [0.206557420000001, 0.073670085000000, 0.120053040000000], [0.706557420000001, 0.073670085000000, 0.120053040000000], [0.206557420000001, 0.573670085000000, 0.120053040000000], [0.706557420000001, 0.573670085000000, 0.120053040000000], [0.235454159999997, 0.235454160000002, 0.000000000000000], [0.735454159999998, 0.235454160000002, 0.000000000000000], [0.235454159999997, 0.735454160000002, 0.000000000000000], [0.735454159999998, 0.735454160000002, 0.000000000000000], [0.264545839999997, 0.000000000000000, 0.333333330000000], [0.764545839999997, 0.000000000000000, 0.333333330000000], [0.264545839999997, 0.500000000000000, 0.333333330000000], [0.764545839999997, 0.500000000000000, 0.333333330000000], [0.999999999999995, 0.264545839999998, 0.666666670000000], [0.499999999999996, 0.264545839999998, 0.666666670000000], [0.999999999999995, 0.764545839999998, 0.666666670000000], [0.499999999999996, 0.764545839999998, 0.666666670000000], ] numbers = [ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, ] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) N = 40 dfset = np.loadtxt(cwd / "dfset_SiO2_221_rd.xz") d = dfset[:, :3].reshape(N, -1, 3) f = dfset[:, 3:].reshape(N, -1, 3) return cell, d, f @pytest.fixture(scope="session") def ph_gan_222() -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return GaN-222 data.""" lattice = [ [6.361531040000000, 0.000000000000000, 0.000000000000000], [-3.180765520000000, 5.509247480000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 10.365211680000000], ] points = [ [0.166666664999996, 0.333333334999992, 0.062095959999999], [0.666666664999996, 0.333333334999992, 0.062095959999999], [0.166666664999996, 0.833333334999992, 0.062095959999999], [0.666666664999996, 0.833333334999992, 0.062095959999999], [0.166666664999996, 0.333333334999992, 0.562095960000000], [0.666666664999996, 0.333333334999992, 0.562095960000000], [0.166666664999996, 0.833333334999992, 0.562095960000000], [0.666666664999996, 0.833333334999992, 0.562095960000000], [0.333333334999996, 0.166666664999999, 0.312095959999999], [0.833333334999996, 0.166666664999999, 0.312095959999999], [0.333333334999996, 0.666666664999999, 0.312095959999999], [0.833333334999996, 0.666666664999999, 0.312095959999999], [0.333333334999996, 0.166666664999999, 0.812095959999999], [0.833333334999996, 0.166666664999999, 0.812095959999999], [0.333333334999996, 0.666666664999999, 0.812095959999999], [0.833333334999996, 0.666666664999999, 0.812095959999999], [0.166666664999996, 0.333333334999992, 0.250404040000001], [0.666666664999996, 0.333333334999992, 0.250404040000001], [0.166666664999996, 0.833333334999992, 0.250404040000001], [0.666666664999996, 0.833333334999992, 0.250404040000001], [0.166666664999996, 0.333333334999992, 0.750404040000001], [0.666666664999996, 0.333333334999992, 0.750404040000001], [0.166666664999996, 0.833333334999992, 0.750404040000001], [0.666666664999996, 0.833333334999992, 0.750404040000001], [0.333333334999996, 0.166666664999999, 0.000404040000000], [0.833333334999996, 0.166666664999999, 0.000404040000000], [0.333333334999996, 0.666666664999999, 0.000404040000000], [0.833333334999996, 0.666666664999999, 0.000404040000000], [0.333333334999996, 0.166666664999999, 0.500404040000000], [0.833333334999996, 0.166666664999999, 0.500404040000000], [0.333333334999996, 0.666666664999999, 0.500404040000000], [0.833333334999996, 0.666666664999999, 0.500404040000000], ] numbers = [ 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, ] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) N = 40 dfset = np.loadtxt(cwd / "dfset_GaN_222_rd.xz") d = dfset[:, :3].reshape(N, -1, 3) f = dfset[:, 3:].reshape(N, -1, 3) return cell, d, f @pytest.fixture(scope="session") def ph_gan_442() -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return GaN-442 data.""" lattice = [ [12.723062080000000, 0.000000000000000, 0.000000000000000], [-6.361531040000000, 11.018494960000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 10.365211680000000], ] points = [ [0.083333332499998, 0.166666667499996, 0.062095959999999], [0.333333332499998, 0.166666667499996, 0.062095959999999], [0.583333332499998, 0.166666667499996, 0.062095959999999], [0.833333332499998, 0.166666667499996, 0.062095959999999], [0.083333332499998, 0.416666667499996, 0.062095959999999], [0.333333332499998, 0.416666667499996, 0.062095959999999], [0.583333332499998, 0.416666667499996, 0.062095959999999], [0.833333332499998, 0.416666667499996, 0.062095959999999], [0.083333332499998, 0.666666667499996, 0.062095959999999], [0.333333332499998, 0.666666667499996, 0.062095959999999], [0.583333332499998, 0.666666667499996, 0.062095959999999], [0.833333332499998, 0.666666667499996, 0.062095959999999], [0.083333332499998, 0.916666667499996, 0.062095959999999], [0.333333332499998, 0.916666667499996, 0.062095959999999], [0.583333332499998, 0.916666667499996, 0.062095959999999], [0.833333332499998, 0.916666667499996, 0.062095959999999], [0.083333332499998, 0.166666667499996, 0.562095960000000], [0.333333332499998, 0.166666667499996, 0.562095960000000], [0.583333332499998, 0.166666667499996, 0.562095960000000], [0.833333332499998, 0.166666667499996, 0.562095960000000], [0.083333332499998, 0.416666667499996, 0.562095960000000], [0.333333332499998, 0.416666667499996, 0.562095960000000], [0.583333332499998, 0.416666667499996, 0.562095960000000], [0.833333332499998, 0.416666667499996, 0.562095960000000], [0.083333332499998, 0.666666667499996, 0.562095960000000], [0.333333332499998, 0.666666667499996, 0.562095960000000], [0.583333332499998, 0.666666667499996, 0.562095960000000], [0.833333332499998, 0.666666667499996, 0.562095960000000], [0.083333332499998, 0.916666667499996, 0.562095960000000], [0.333333332499998, 0.916666667499996, 0.562095960000000], [0.583333332499998, 0.916666667499996, 0.562095960000000], [0.833333332499998, 0.916666667499996, 0.562095960000000], [0.166666667499998, 0.083333332499999, 0.312095959999999], [0.416666667499998, 0.083333332499999, 0.312095959999999], [0.666666667499998, 0.083333332499999, 0.312095959999999], [0.916666667499998, 0.083333332499999, 0.312095959999999], [0.166666667499998, 0.333333332500000, 0.312095959999999], [0.416666667499998, 0.333333332500000, 0.312095959999999], [0.666666667499998, 0.333333332500000, 0.312095959999999], [0.916666667499998, 0.333333332500000, 0.312095959999999], [0.166666667499998, 0.583333332500000, 0.312095959999999], [0.416666667499998, 0.583333332500000, 0.312095959999999], [0.666666667499998, 0.583333332500000, 0.312095959999999], [0.916666667499998, 0.583333332500000, 0.312095959999999], [0.166666667499998, 0.833333332500000, 0.312095959999999], [0.416666667499998, 0.833333332500000, 0.312095959999999], [0.666666667499998, 0.833333332500000, 0.312095959999999], [0.916666667499998, 0.833333332500000, 0.312095959999999], [0.166666667499998, 0.083333332499999, 0.812095959999999], [0.416666667499998, 0.083333332499999, 0.812095959999999], [0.666666667499998, 0.083333332499999, 0.812095959999999], [0.916666667499998, 0.083333332499999, 0.812095959999999], [0.166666667499998, 0.333333332500000, 0.812095959999999], [0.416666667499998, 0.333333332500000, 0.812095959999999], [0.666666667499998, 0.333333332500000, 0.812095959999999], [0.916666667499998, 0.333333332500000, 0.812095959999999], [0.166666667499998, 0.583333332500000, 0.812095959999999], [0.416666667499998, 0.583333332500000, 0.812095959999999], [0.666666667499998, 0.583333332500000, 0.812095959999999], [0.916666667499998, 0.583333332500000, 0.812095959999999], [0.166666667499998, 0.833333332500000, 0.812095959999999], [0.416666667499998, 0.833333332500000, 0.812095959999999], [0.666666667499998, 0.833333332500000, 0.812095959999999], [0.916666667499998, 0.833333332500000, 0.812095959999999], [0.083333332499998, 0.166666667499996, 0.250404040000001], [0.333333332499998, 0.166666667499996, 0.250404040000001], [0.583333332499998, 0.166666667499996, 0.250404040000001], [0.833333332499998, 0.166666667499996, 0.250404040000001], [0.083333332499998, 0.416666667499996, 0.250404040000001], [0.333333332499998, 0.416666667499996, 0.250404040000001], [0.583333332499998, 0.416666667499996, 0.250404040000001], [0.833333332499998, 0.416666667499996, 0.250404040000001], [0.083333332499998, 0.666666667499996, 0.250404040000001], [0.333333332499998, 0.666666667499996, 0.250404040000001], [0.583333332499998, 0.666666667499996, 0.250404040000001], [0.833333332499998, 0.666666667499996, 0.250404040000001], [0.083333332499998, 0.916666667499996, 0.250404040000001], [0.333333332499998, 0.916666667499996, 0.250404040000001], [0.583333332499998, 0.916666667499996, 0.250404040000001], [0.833333332499998, 0.916666667499996, 0.250404040000001], [0.083333332499998, 0.166666667499996, 0.750404040000001], [0.333333332499998, 0.166666667499996, 0.750404040000001], [0.583333332499998, 0.166666667499996, 0.750404040000001], [0.833333332499998, 0.166666667499996, 0.750404040000001], [0.083333332499998, 0.416666667499996, 0.750404040000001], [0.333333332499998, 0.416666667499996, 0.750404040000001], [0.583333332499998, 0.416666667499996, 0.750404040000001], [0.833333332499998, 0.416666667499996, 0.750404040000001], [0.083333332499998, 0.666666667499996, 0.750404040000001], [0.333333332499998, 0.666666667499996, 0.750404040000001], [0.583333332499998, 0.666666667499996, 0.750404040000001], [0.833333332499998, 0.666666667499996, 0.750404040000001], [0.083333332499998, 0.916666667499996, 0.750404040000001], [0.333333332499998, 0.916666667499996, 0.750404040000001], [0.583333332499998, 0.916666667499996, 0.750404040000001], [0.833333332499998, 0.916666667499996, 0.750404040000001], [0.166666667499998, 0.083333332499999, 0.000404040000000], [0.416666667499998, 0.083333332499999, 0.000404040000000], [0.666666667499998, 0.083333332499999, 0.000404040000000], [0.916666667499998, 0.083333332499999, 0.000404040000000], [0.166666667499998, 0.333333332500000, 0.000404040000000], [0.416666667499998, 0.333333332500000, 0.000404040000000], [0.666666667499998, 0.333333332500000, 0.000404040000000], [0.916666667499998, 0.333333332500000, 0.000404040000000], [0.166666667499998, 0.583333332500000, 0.000404040000000], [0.416666667499998, 0.583333332500000, 0.000404040000000], [0.666666667499998, 0.583333332500000, 0.000404040000000], [0.916666667499998, 0.583333332500000, 0.000404040000000], [0.166666667499998, 0.833333332500000, 0.000404040000000], [0.416666667499998, 0.833333332500000, 0.000404040000000], [0.666666667499998, 0.833333332500000, 0.000404040000000], [0.916666667499998, 0.833333332500000, 0.000404040000000], [0.166666667499998, 0.083333332499999, 0.500404040000000], [0.416666667499998, 0.083333332499999, 0.500404040000000], [0.666666667499998, 0.083333332499999, 0.500404040000000], [0.916666667499998, 0.083333332499999, 0.500404040000000], [0.166666667499998, 0.333333332500000, 0.500404040000000], [0.416666667499998, 0.333333332500000, 0.500404040000000], [0.666666667499998, 0.333333332500000, 0.500404040000000], [0.916666667499998, 0.333333332500000, 0.500404040000000], [0.166666667499998, 0.583333332500000, 0.500404040000000], [0.416666667499998, 0.583333332500000, 0.500404040000000], [0.666666667499998, 0.583333332500000, 0.500404040000000], [0.916666667499998, 0.583333332500000, 0.500404040000000], [0.166666667499998, 0.833333332500000, 0.500404040000000], [0.416666667499998, 0.833333332500000, 0.500404040000000], [0.666666667499998, 0.833333332500000, 0.500404040000000], [0.916666667499998, 0.833333332500000, 0.500404040000000], ] numbers = [ 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, ] cell = SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers) N = 40 dfset = np.loadtxt(cwd / "dfset_GaN_442_rd.xz") d = dfset[:, :3].reshape(N, -1, 3) f = dfset[:, 3:].reshape(N, -1, 3) return cell, d, f @pytest.fixture(scope="session") def ph3_si_111_fc3() -> tuple[SymfcAtoms, np.ndarray, np.ndarray]: """Return Si-111 data for fc3.""" lattice = [ [5.433560030000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 5.433560030000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 5.433560030000000], ] points = [ [0.875000000000000, 0.875000000000000, 0.875000000000000], [0.875000000000000, 0.375000000000000, 0.375000000000000], [0.375000000000000, 0.875000000000000, 0.375000000000000], [0.375000000000000, 0.375000000000000, 0.875000000000000], [0.125000000000000, 0.125000000000000, 0.125000000000000], 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E0w,>E#Y?m|B{dH%XgzF)8{ALlj8YUڢCԑ0|*}p4_^|@^Nȴ ]џ(&D}]ݰ:e,P O/%c~.@d3/=zS#qiMXMCS_}-R75xS"ˁZf x&23DBm0_buut#:yiBGQQMgJMێsHPY9 utd};/̗my%Cep%YuRH9p#8ʜ߫KRKDkXlnCiam#v :eG9 |"+P\AQSc'Q'?~ ]DkTqu;ݎ*oUI?v 4* n w' ["otX&kP/L10ծOՎn+ }u:2OP K:cpŸP-y`RpJ[ya"T(nQp,'*8ҞکZ[ xCl:+o.c 4F0$6j&Aəm .٭.8  *b 9E.Ac4m;eILif#:J*bE{SJ0MVxҵ՗\BgYײ\oA(yri??;DkeS+j?yɊ,$48zU%m&)\*؃bW@j^ҙfW?m4^Yp$ˁB|0`㟨t 1e-12)[0] assert np.all(np.isclose(eigvecs[:, i][nonzero], eigvecs[nonzero[0], i])) def test_eigsh_and_eigh(): """Test eigsh.""" # Atomic permutations of four atoms row = np.repeat(np.arange(12), 4) col = np.array([i + j for j in range(3) for i in range(0, 12, 3)]) col = np.tile(col, 4) data = np.full(len(col), 0.25) proj = csr_array((data, (row, col)), shape=(12, 12), dtype=float) eigvecs = eigsh(proj, log_level=0) eigvecs = eigvecs.toarray() _assert_four_atoms_eigvecs(eigvecs) eigvecs = eigsh(proj, is_large_block=True, log_level=0) eigvecs = eigvecs.recover() _assert_four_atoms_eigvecs(eigvecs) eigvecs = eigh(proj.toarray(), log_level=0) _assert_four_atoms_eigvecs(eigvecs) symfc-1.6.0/tests/test_sparse_solver.py000066400000000000000000000026621511276756400203250ustar00rootroot00000000000000"""Tests of Symfc.""" from __future__ import annotations from pathlib import Path import numpy as np from scipy.sparse import csr_array from symfc import Symfc from symfc.solvers.solver_O2 import run_solver_O2 from symfc.utils.utils import SymfcAtoms cwd = Path(__file__).parent def test_api_NaCl_222_with_dataset_fd( ph_nacl_222: tuple[SymfcAtoms, np.ndarray, np.ndarray], ): """Test solver with sparse displacements and forces as input.""" supercell, displacements, forces = ph_nacl_222 symfc = Symfc( supercell, ) symfc.displacements = displacements symfc.forces = forces symfc.compute_basis_set(orders=(2,)) n_data, n_atom, _ = forces.shape f = forces.reshape(n_data, -1) d = csr_array(displacements.reshape(n_data, -1)) fc2_basis = symfc.basis_set[2] compress_mat_fc2 = fc2_basis.compact_compression_matrix basis_set_fc2 = fc2_basis.blocked_basis_set atomic_decompr_idx_fc2 = fc2_basis.atomic_decompr_idx coefs = run_solver_O2( d, f, compress_mat_fc2, basis_set_fc2, atomic_decompr_idx_fc2, use_sparse_disps=True, use_mkl=False, ) fc = fc2_basis.blocked_basis_set.dot(coefs) fc = np.array( (compress_mat_fc2 @ fc).reshape((-1, n_atom, 3, 3)), dtype="double", order="C" ) fc_ref = np.loadtxt(cwd / "compact_fc_NaCl_222.xz").reshape(fc.shape) np.testing.assert_allclose(fc, fc_ref) symfc-1.6.0/tests/utils/000077500000000000000000000000001511276756400151575ustar00rootroot00000000000000symfc-1.6.0/tests/utils/test_block_tree.py000066400000000000000000000114211511276756400207000ustar00rootroot00000000000000"""Tests of block matrix functions.""" import numpy as np from symfc.utils.matrix import BlockMatrixNode def test_block_matrix(): """Test BlockMatrix.""" mat = np.array( [ [5, 4, 3, 2, 7, 4, 0, 0], [4, 3, 2, 1, 8, 3, 0, 0], [0, 0, 6, 2, 8, 7, 7, 2], [0, 0, 1, 4, 3, 3, 1, 8], [0, 0, 0, 0, 5, 3, 9, 1], [0, 0, 0, 0, 3, 4, 1, 2], [0, 0, 0, 0, 7, 8, 0, 0], [0, 0, 0, 0, 7, 2, 0, 0], ] ) block1_1 = BlockMatrixNode( rows=[0, 1], col_begin=0, col_end=2, data=mat[0:2, 0:2], index=0, ) block1_2 = BlockMatrixNode( rows=[0, 1], col_begin=2, col_end=4, data=mat[0:2, 2:4], next_sibling=block1_1, index=1, ) block1_3 = BlockMatrixNode( rows=[2, 3], col_begin=2, col_end=4, data=mat[2:4, 2:4], next_sibling=block1_2, index=2, ) block1 = BlockMatrixNode( rows=[0, 1, 2, 3], col_begin=0, col_end=4, first_child=block1_3, index=3, ) block2_1 = BlockMatrixNode( rows=[0, 1], col_begin=0, col_end=2, data=mat[0:2, 4:6], index=4, ) block2_2 = BlockMatrixNode( rows=[2, 3], col_begin=0, col_end=2, data=mat[2:4, 4:6], next_sibling=block2_1, index=5, ) block2_3 = BlockMatrixNode( rows=[2, 3], col_begin=2, col_end=4, data=mat[2:4, 6:8], next_sibling=block2_2, index=6, ) block2 = BlockMatrixNode( rows=[0, 1, 2, 3], col_begin=4, col_end=8, first_child=block2_3, next_sibling=block1, index=7, ) block3_1 = BlockMatrixNode( rows=[0, 1], col_begin=0, col_end=2, data=mat[4:6, 4:6], index=8, ) block3_2 = BlockMatrixNode( rows=[0, 1], col_begin=2, col_end=4, data=mat[4:6, 6:8], next_sibling=block3_1, index=9, ) block3_3 = BlockMatrixNode( rows=[2, 3], col_begin=0, col_end=2, data=mat[6:8, 4:6], next_sibling=block3_2, index=10, ) block3 = BlockMatrixNode( rows=[4, 5, 6, 7], col_begin=4, col_end=8, first_child=block3_3, next_sibling=block2, index=11, ) cblock1 = BlockMatrixNode( rows=[0, 1], col_begin=0, col_end=1, data=np.array([[4], [5]]), index=101, ) cblock2 = BlockMatrixNode( rows=[2, 3], col_begin=0, col_end=1, data=np.array([[1], [3]]), next_sibling=cblock1, index=102, ) cblock3 = BlockMatrixNode( rows=[2, 3], col_begin=1, col_end=2, data=np.array([[2], [4]]), next_sibling=cblock2, index=103, ) cmplt = BlockMatrixNode( rows=[0, 1, 2, 3], col_begin=0, col_end=2, first_child=cblock3, root=True, index=104, ) eigvecs = np.array([[1, 0], [2, 1]]) mat[4:8, 0:2] = cmplt.dot(eigvecs) block4 = BlockMatrixNode( rows=[4, 5, 6, 7], col_begin=0, col_end=2, data=eigvecs, next_sibling=block3, compress=cmplt, index=12, ) bm = BlockMatrixNode( rows=[0, 1, 2, 3, 4, 5, 6, 7], col_begin=0, col_end=8, first_child=block4, root=True, index=13, ) mat2 = np.array([[3, 1], [5, 7], [2, 8], [5, 9], [3, 2], [4, 5], [7, 2], [2, 1]]) vec2 = np.array([3, 1, 5, 7, 2, 3, 3, 1]) np.testing.assert_array_equal(bm.recover(), mat) np.testing.assert_array_equal(bm.dot(mat2), mat @ mat2) np.testing.assert_array_equal(bm.dot(vec2), mat @ vec2) np.testing.assert_array_equal(bm.transpose_dot(mat2), mat.T @ mat2) np.testing.assert_array_equal(bm.transpose_dot(vec2), mat.T @ vec2) # mat3 = np.array( # [ # [3, 1, 4, 3, 0, 0, 2, 0], # [5, 7, 3, 1, 2, 1, 0, 6], # [2, 8, 7, 4, 3, 9, 9, 0], # [5, 9, 0, 2, 0, 0, 0, 2], # [2, 3, 4, 9, 1, 0, 1, 2], # [0, 2, 0, 2, 0, 8, 0, 3], # [5, 9, 0, 2, 0, 0, 2, 2], # [0, 0, 0, 2, 2, 0, 9, 1], # ] # ) # np.testing.assert_array_equal( # bm.compress_matrix(mat3), # mat.T @ mat3 @ mat, # ) # np.testing.assert_array_equal( # bm.compress_matrix(csr_array(mat3)), # mat.T @ mat3 @ mat, # ) perm = np.array([2, 1, 0, 4, 3, 6, 5, 7]) bm.rows = perm bm.set_root_indices() mat = mat[perm] np.testing.assert_array_equal(bm.dot(mat2), mat @ mat2) symfc-1.6.0/tests/utils/test_cutoff_tools.py000066400000000000000000000465751511276756400213170ustar00rootroot00000000000000"""Tests of FC cutoff tools.""" from __future__ import annotations from collections.abc import Sequence from pathlib import Path import numpy as np from numpy.typing import NDArray from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.utils import SymfcAtoms cwd = Path(__file__).parent def test_FCCutoff(): """Test FCCutoff using NaCl conventional unit cell 2x2x2.""" lattice = [ [11.281120000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 11.281120000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 11.281120000000000], ] points = [ [0.000000000000000, 0.000000000000000, 0.000000000000000], [0.500000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.500000000000000, 0.000000000000000], [0.500000000000000, 0.500000000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 0.500000000000000], [0.500000000000000, 0.000000000000000, 0.500000000000000], [0.000000000000000, 0.500000000000000, 0.500000000000000], [0.500000000000000, 0.500000000000000, 0.500000000000000], [0.000000000000000, 0.250000000000000, 0.250000000000000], [0.500000000000000, 0.250000000000000, 0.250000000000000], [0.000000000000000, 0.750000000000000, 0.250000000000000], [0.500000000000000, 0.750000000000000, 0.250000000000000], [0.000000000000000, 0.250000000000000, 0.750000000000000], [0.500000000000000, 0.250000000000000, 0.750000000000000], [0.000000000000000, 0.750000000000000, 0.750000000000000], [0.500000000000000, 0.750000000000000, 0.750000000000000], [0.250000000000000, 0.000000000000000, 0.250000000000000], [0.750000000000000, 0.000000000000000, 0.250000000000000], [0.250000000000000, 0.500000000000000, 0.250000000000000], [0.750000000000000, 0.500000000000000, 0.250000000000000], [0.250000000000000, 0.000000000000000, 0.750000000000000], [0.750000000000000, 0.000000000000000, 0.750000000000000], [0.250000000000000, 0.500000000000000, 0.750000000000000], [0.750000000000000, 0.500000000000000, 0.750000000000000], [0.250000000000000, 0.250000000000000, 0.000000000000000], [0.750000000000000, 0.250000000000000, 0.000000000000000], [0.250000000000000, 0.750000000000000, 0.000000000000000], [0.750000000000000, 0.750000000000000, 0.000000000000000], [0.250000000000000, 0.250000000000000, 0.500000000000000], [0.750000000000000, 0.250000000000000, 0.500000000000000], [0.250000000000000, 0.750000000000000, 0.500000000000000], [0.750000000000000, 0.750000000000000, 0.500000000000000], [0.250000000000000, 0.250000000000000, 0.250000000000000], [0.750000000000000, 0.250000000000000, 0.250000000000000], [0.250000000000000, 0.750000000000000, 0.250000000000000], [0.750000000000000, 0.750000000000000, 0.250000000000000], [0.250000000000000, 0.250000000000000, 0.750000000000000], [0.750000000000000, 0.250000000000000, 0.750000000000000], [0.250000000000000, 0.750000000000000, 0.750000000000000], [0.750000000000000, 0.750000000000000, 0.750000000000000], [0.250000000000000, 0.000000000000000, 0.000000000000000], [0.750000000000000, 0.000000000000000, 0.000000000000000], [0.250000000000000, 0.500000000000000, 0.000000000000000], [0.750000000000000, 0.500000000000000, 0.000000000000000], [0.250000000000000, 0.000000000000000, 0.500000000000000], [0.750000000000000, 0.000000000000000, 0.500000000000000], [0.250000000000000, 0.500000000000000, 0.500000000000000], [0.750000000000000, 0.500000000000000, 0.500000000000000], [0.000000000000000, 0.250000000000000, 0.000000000000000], [0.500000000000000, 0.250000000000000, 0.000000000000000], [0.000000000000000, 0.750000000000000, 0.000000000000000], [0.500000000000000, 0.750000000000000, 0.000000000000000], [0.000000000000000, 0.250000000000000, 0.500000000000000], [0.500000000000000, 0.250000000000000, 0.500000000000000], [0.000000000000000, 0.750000000000000, 0.500000000000000], [0.500000000000000, 0.750000000000000, 0.500000000000000], [0.000000000000000, 0.000000000000000, 0.250000000000000], [0.500000000000000, 0.000000000000000, 0.250000000000000], [0.000000000000000, 0.500000000000000, 0.250000000000000], [0.500000000000000, 0.500000000000000, 0.250000000000000], [0.000000000000000, 0.000000000000000, 0.750000000000000], [0.500000000000000, 0.000000000000000, 0.750000000000000], [0.000000000000000, 0.500000000000000, 0.750000000000000], [0.500000000000000, 0.500000000000000, 0.750000000000000], ] numbers = [ 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, ] n_nonzero_elem = _check(lattice, points, numbers) ref = [11200, 32896, 185536, 262144] np.testing.assert_equal(ref, n_nonzero_elem) def test_FCCutoff_gan_443(): """Test FCCutoff using GaN 4x4x3. import spglib from phonopy.structure.atoms import PhonopyAtoms from phonopy.structure.cells import get_supercell dataset = spglib.get_symmetry_dataset( (cell_gan_111.cell, cell_gan_111.scaled_positions, cell_gan_111.numbers) ) phonopy_cell = PhonopyAtoms( cell=dataset.std_lattice, scaled_positions=dataset.std_positions, numbers=dataset.std_types, ) scell = get_supercell(phonopy_cell, [[4, 0, 0], [0, 4, 0], [0, 0, 3]]) for v in scell.cell: print(f"[{v[0]:.10f}, {v[1]:.10f}, {v[2]:.10f}],") for v in scell.scaled_positions: print(f"[{v[0]:.10f}, {v[1]:.10f}, {v[2]:.10f}],") print(", ".join([f"{n}" for n in scell.numbers])) """ lattice = [ [12.7230620734, 0.0000000000, 0.0000000000], [-6.3615310367, 11.0184949695, 0.0000000000], [0.0000000000, 0.0000000000, 15.5478175200], ] points = [ [0.0833333333, 0.1666666667, 0.0413973067], [0.3333333333, 0.1666666667, 0.0413973067], [0.5833333333, 0.1666666667, 0.0413973067], [0.8333333333, 0.1666666667, 0.0413973067], [0.0833333333, 0.4166666667, 0.0413973067], [0.3333333333, 0.4166666667, 0.0413973067], [0.5833333333, 0.4166666667, 0.0413973067], [0.8333333333, 0.4166666667, 0.0413973067], [0.0833333333, 0.6666666667, 0.0413973067], [0.3333333333, 0.6666666667, 0.0413973067], [0.5833333333, 0.6666666667, 0.0413973067], [0.8333333333, 0.6666666667, 0.0413973067], [0.0833333333, 0.9166666667, 0.0413973067], [0.3333333333, 0.9166666667, 0.0413973067], [0.5833333333, 0.9166666667, 0.0413973067], [0.8333333333, 0.9166666667, 0.0413973067], [0.0833333333, 0.1666666667, 0.3747306400], [0.3333333333, 0.1666666667, 0.3747306400], [0.5833333333, 0.1666666667, 0.3747306400], [0.8333333333, 0.1666666667, 0.3747306400], [0.0833333333, 0.4166666667, 0.3747306400], [0.3333333333, 0.4166666667, 0.3747306400], [0.5833333333, 0.4166666667, 0.3747306400], [0.8333333333, 0.4166666667, 0.3747306400], [0.0833333333, 0.6666666667, 0.3747306400], [0.3333333333, 0.6666666667, 0.3747306400], [0.5833333333, 0.6666666667, 0.3747306400], [0.8333333333, 0.6666666667, 0.3747306400], [0.0833333333, 0.9166666667, 0.3747306400], [0.3333333333, 0.9166666667, 0.3747306400], [0.5833333333, 0.9166666667, 0.3747306400], [0.8333333333, 0.9166666667, 0.3747306400], [0.0833333333, 0.1666666667, 0.7080639733], [0.3333333333, 0.1666666667, 0.7080639733], [0.5833333333, 0.1666666667, 0.7080639733], [0.8333333333, 0.1666666667, 0.7080639733], [0.0833333333, 0.4166666667, 0.7080639733], [0.3333333333, 0.4166666667, 0.7080639733], [0.5833333333, 0.4166666667, 0.7080639733], [0.8333333333, 0.4166666667, 0.7080639733], [0.0833333333, 0.6666666667, 0.7080639733], [0.3333333333, 0.6666666667, 0.7080639733], [0.5833333333, 0.6666666667, 0.7080639733], [0.8333333333, 0.6666666667, 0.7080639733], [0.0833333333, 0.9166666667, 0.7080639733], [0.3333333333, 0.9166666667, 0.7080639733], [0.5833333333, 0.9166666667, 0.7080639733], [0.8333333333, 0.9166666667, 0.7080639733], [0.1666666667, 0.0833333333, 0.2080639733], [0.4166666667, 0.0833333333, 0.2080639733], [0.6666666667, 0.0833333333, 0.2080639733], [0.9166666667, 0.0833333333, 0.2080639733], [0.1666666667, 0.3333333333, 0.2080639733], [0.4166666667, 0.3333333333, 0.2080639733], [0.6666666667, 0.3333333333, 0.2080639733], [0.9166666667, 0.3333333333, 0.2080639733], [0.1666666667, 0.5833333333, 0.2080639733], [0.4166666667, 0.5833333333, 0.2080639733], [0.6666666667, 0.5833333333, 0.2080639733], [0.9166666667, 0.5833333333, 0.2080639733], [0.1666666667, 0.8333333333, 0.2080639733], [0.4166666667, 0.8333333333, 0.2080639733], [0.6666666667, 0.8333333333, 0.2080639733], [0.9166666667, 0.8333333333, 0.2080639733], [0.1666666667, 0.0833333333, 0.5413973067], [0.4166666667, 0.0833333333, 0.5413973067], [0.6666666667, 0.0833333333, 0.5413973067], [0.9166666667, 0.0833333333, 0.5413973067], [0.1666666667, 0.3333333333, 0.5413973067], [0.4166666667, 0.3333333333, 0.5413973067], [0.6666666667, 0.3333333333, 0.5413973067], [0.9166666667, 0.3333333333, 0.5413973067], [0.1666666667, 0.5833333333, 0.5413973067], [0.4166666667, 0.5833333333, 0.5413973067], [0.6666666667, 0.5833333333, 0.5413973067], [0.9166666667, 0.5833333333, 0.5413973067], [0.1666666667, 0.8333333333, 0.5413973067], [0.4166666667, 0.8333333333, 0.5413973067], [0.6666666667, 0.8333333333, 0.5413973067], [0.9166666667, 0.8333333333, 0.5413973067], [0.1666666667, 0.0833333333, 0.8747306400], [0.4166666667, 0.0833333333, 0.8747306400], [0.6666666667, 0.0833333333, 0.8747306400], [0.9166666667, 0.0833333333, 0.8747306400], [0.1666666667, 0.3333333333, 0.8747306400], [0.4166666667, 0.3333333333, 0.8747306400], [0.6666666667, 0.3333333333, 0.8747306400], [0.9166666667, 0.3333333333, 0.8747306400], [0.1666666667, 0.5833333333, 0.8747306400], [0.4166666667, 0.5833333333, 0.8747306400], [0.6666666667, 0.5833333333, 0.8747306400], [0.9166666667, 0.5833333333, 0.8747306400], [0.1666666667, 0.8333333333, 0.8747306400], [0.4166666667, 0.8333333333, 0.8747306400], [0.6666666667, 0.8333333333, 0.8747306400], [0.9166666667, 0.8333333333, 0.8747306400], [0.0833333333, 0.1666666667, 0.1669360267], [0.3333333333, 0.1666666667, 0.1669360267], [0.5833333333, 0.1666666667, 0.1669360267], [0.8333333333, 0.1666666667, 0.1669360267], [0.0833333333, 0.4166666667, 0.1669360267], [0.3333333333, 0.4166666667, 0.1669360267], [0.5833333333, 0.4166666667, 0.1669360267], [0.8333333333, 0.4166666667, 0.1669360267], [0.0833333333, 0.6666666667, 0.1669360267], [0.3333333333, 0.6666666667, 0.1669360267], [0.5833333333, 0.6666666667, 0.1669360267], [0.8333333333, 0.6666666667, 0.1669360267], [0.0833333333, 0.9166666667, 0.1669360267], [0.3333333333, 0.9166666667, 0.1669360267], [0.5833333333, 0.9166666667, 0.1669360267], [0.8333333333, 0.9166666667, 0.1669360267], [0.0833333333, 0.1666666667, 0.5002693600], [0.3333333333, 0.1666666667, 0.5002693600], [0.5833333333, 0.1666666667, 0.5002693600], [0.8333333333, 0.1666666667, 0.5002693600], [0.0833333333, 0.4166666667, 0.5002693600], [0.3333333333, 0.4166666667, 0.5002693600], [0.5833333333, 0.4166666667, 0.5002693600], [0.8333333333, 0.4166666667, 0.5002693600], [0.0833333333, 0.6666666667, 0.5002693600], [0.3333333333, 0.6666666667, 0.5002693600], [0.5833333333, 0.6666666667, 0.5002693600], [0.8333333333, 0.6666666667, 0.5002693600], [0.0833333333, 0.9166666667, 0.5002693600], [0.3333333333, 0.9166666667, 0.5002693600], [0.5833333333, 0.9166666667, 0.5002693600], [0.8333333333, 0.9166666667, 0.5002693600], [0.0833333333, 0.1666666667, 0.8336026933], [0.3333333333, 0.1666666667, 0.8336026933], [0.5833333333, 0.1666666667, 0.8336026933], [0.8333333333, 0.1666666667, 0.8336026933], [0.0833333333, 0.4166666667, 0.8336026933], [0.3333333333, 0.4166666667, 0.8336026933], [0.5833333333, 0.4166666667, 0.8336026933], [0.8333333333, 0.4166666667, 0.8336026933], [0.0833333333, 0.6666666667, 0.8336026933], [0.3333333333, 0.6666666667, 0.8336026933], [0.5833333333, 0.6666666667, 0.8336026933], [0.8333333333, 0.6666666667, 0.8336026933], [0.0833333333, 0.9166666667, 0.8336026933], [0.3333333333, 0.9166666667, 0.8336026933], [0.5833333333, 0.9166666667, 0.8336026933], [0.8333333333, 0.9166666667, 0.8336026933], [0.1666666667, 0.0833333333, 0.0002693600], [0.4166666667, 0.0833333333, 0.0002693600], [0.6666666667, 0.0833333333, 0.0002693600], [0.9166666667, 0.0833333333, 0.0002693600], [0.1666666667, 0.3333333333, 0.0002693600], [0.4166666667, 0.3333333333, 0.0002693600], [0.6666666667, 0.3333333333, 0.0002693600], [0.9166666667, 0.3333333333, 0.0002693600], [0.1666666667, 0.5833333333, 0.0002693600], [0.4166666667, 0.5833333333, 0.0002693600], [0.6666666667, 0.5833333333, 0.0002693600], [0.9166666667, 0.5833333333, 0.0002693600], [0.1666666667, 0.8333333333, 0.0002693600], [0.4166666667, 0.8333333333, 0.0002693600], [0.6666666667, 0.8333333333, 0.0002693600], [0.9166666667, 0.8333333333, 0.0002693600], [0.1666666667, 0.0833333333, 0.3336026933], [0.4166666667, 0.0833333333, 0.3336026933], [0.6666666667, 0.0833333333, 0.3336026933], [0.9166666667, 0.0833333333, 0.3336026933], [0.1666666667, 0.3333333333, 0.3336026933], [0.4166666667, 0.3333333333, 0.3336026933], [0.6666666667, 0.3333333333, 0.3336026933], [0.9166666667, 0.3333333333, 0.3336026933], [0.1666666667, 0.5833333333, 0.3336026933], [0.4166666667, 0.5833333333, 0.3336026933], [0.6666666667, 0.5833333333, 0.3336026933], [0.9166666667, 0.5833333333, 0.3336026933], [0.1666666667, 0.8333333333, 0.3336026933], [0.4166666667, 0.8333333333, 0.3336026933], [0.6666666667, 0.8333333333, 0.3336026933], [0.9166666667, 0.8333333333, 0.3336026933], [0.1666666667, 0.0833333333, 0.6669360267], [0.4166666667, 0.0833333333, 0.6669360267], [0.6666666667, 0.0833333333, 0.6669360267], [0.9166666667, 0.0833333333, 0.6669360267], [0.1666666667, 0.3333333333, 0.6669360267], [0.4166666667, 0.3333333333, 0.6669360267], [0.6666666667, 0.3333333333, 0.6669360267], [0.9166666667, 0.3333333333, 0.6669360267], [0.1666666667, 0.5833333333, 0.6669360267], [0.4166666667, 0.5833333333, 0.6669360267], [0.6666666667, 0.5833333333, 0.6669360267], [0.9166666667, 0.5833333333, 0.6669360267], [0.1666666667, 0.8333333333, 0.6669360267], [0.4166666667, 0.8333333333, 0.6669360267], [0.6666666667, 0.8333333333, 0.6669360267], [0.9166666667, 0.8333333333, 0.6669360267], ] numbers = [ 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, ] n_nonzero_elem = _check(lattice, points, numbers) print(n_nonzero_elem) ref = [69312, 665280, 3840768, 6644736] np.testing.assert_equal(ref, n_nonzero_elem) def _check( lattice: Sequence[Sequence[float]] | NDArray, points: Sequence[Sequence[float]] | NDArray, numbers: Sequence[int] | NDArray, ): n_nonzero_elem = [] for cutoff in (4, 6, 8, 10): fccutoff = FCCutoff( SymfcAtoms(cell=lattice, scaled_positions=points, numbers=numbers), cutoff=cutoff, ) nonzero_idx = [ f"{i}" for i, nonzero in enumerate(fccutoff.nonzero_atomic_indices_fc3()) if nonzero ] n_nonzero_elem.append(len(nonzero_idx)) return n_nonzero_elem symfc-1.6.0/tests/utils/test_eig_tools.py000066400000000000000000000100651511276756400205560ustar00rootroot00000000000000"""Tests of eigsh functions.""" import numpy as np import pytest from scipy.sparse import csr_array from symfc.utils.eig_tools import ( _compr_projector, eigh_projector, eigh_projector_division, eigsh_projector, eigsh_projector_sumrule, ) def test_eigsh_projector(): """Test eigsh_projector and eigsh_projector_sumrule.""" # Atomic permutations of four atoms row = np.repeat(np.arange(12), 4) col = np.array([i + j for j in range(3) for i in range(0, 12, 3)]) col = np.tile(col, 4) data = np.full(len(col), 0.25) proj = csr_array((data, (row, col)), shape=(12, 12), dtype=float) eigvecs = eigsh_projector(proj, verbose=False) eigvecs = eigvecs.toarray() assert eigvecs.shape[1] == 3 assert np.linalg.norm(eigvecs[:, 0]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 1]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 2]) == pytest.approx(1.0) assert eigvecs[:, 0] @ eigvecs[:, 1] == pytest.approx(0.0) assert eigvecs[:, 1] @ eigvecs[:, 2] == pytest.approx(0.0) assert eigvecs[:, 2] @ eigvecs[:, 0] == pytest.approx(0.0) for i in range(3): nonzero = np.where(np.abs(eigvecs[:, i]) > 1e-12)[0] assert np.all(np.isclose(eigvecs[:, i][nonzero], eigvecs[nonzero[0], i])) eigvecs = eigsh_projector_sumrule(proj, verbose=False) eigvecs = eigvecs.recover() assert eigvecs.shape[1] == 3 assert np.linalg.norm(eigvecs[:, 0]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 1]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 2]) == pytest.approx(1.0) assert eigvecs[:, 0] @ eigvecs[:, 1] == pytest.approx(0.0) assert eigvecs[:, 1] @ eigvecs[:, 2] == pytest.approx(0.0) assert eigvecs[:, 2] @ eigvecs[:, 0] == pytest.approx(0.0) for i in range(3): nonzero = np.where(np.abs(eigvecs[:, i]) > 1e-12)[0] assert np.all(np.isclose(eigvecs[:, i][nonzero], eigvecs[nonzero[0], i])) def test_eigh_projector(): """Test eigh_projector and eigh_projector_use_submatrix.""" # Atomic permutations of four atoms row = np.repeat(np.arange(12), 4) col = np.array([i + j for j in range(3) for i in range(0, 12, 3)]) col = np.tile(col, 4) data = np.full(len(col), 0.25) proj = csr_array((data, (row, col)), shape=(12, 12), dtype=float).toarray() eigvecs = eigh_projector(proj, verbose=False) assert eigvecs.shape[1] == 3 assert np.linalg.norm(eigvecs[:, 0]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 1]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 2]) == pytest.approx(1.0) assert eigvecs[:, 0] @ eigvecs[:, 1] == pytest.approx(0.0) assert eigvecs[:, 1] @ eigvecs[:, 2] == pytest.approx(0.0) assert eigvecs[:, 2] @ eigvecs[:, 0] == pytest.approx(0.0) for i in range(3): nonzero = np.where(np.abs(eigvecs[:, i]) > 1e-12)[0] assert np.all(np.isclose(eigvecs[:, i][nonzero], eigvecs[nonzero[0], i])) eigvecs = eigh_projector_division(proj, verbose=False) assert eigvecs.shape[1] == 3 assert np.linalg.norm(eigvecs[:, 0]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 1]) == pytest.approx(1.0) assert np.linalg.norm(eigvecs[:, 2]) == pytest.approx(1.0) assert eigvecs[:, 0] @ eigvecs[:, 1] == pytest.approx(0.0) assert eigvecs[:, 1] @ eigvecs[:, 2] == pytest.approx(0.0) assert eigvecs[:, 2] @ eigvecs[:, 0] == pytest.approx(0.0) for i in range(3): nonzero = np.where(np.abs(eigvecs[:, i]) > 1e-12)[0] assert np.all(np.isclose(eigvecs[:, i][nonzero], eigvecs[nonzero[0], i])) def test_compr_projector(): """Test compr_projector.""" row = col = [0, 2, 5] data = [1, 1, 1] proj = csr_array((data, (row, col)), shape=(6, 6), dtype=int) proj_rev, compr = _compr_projector(proj) assert proj_rev.shape == (3, 3) np.testing.assert_allclose(proj_rev.toarray(), np.eye(3)) assert compr.shape == (6, 3) compr_ref = np.zeros(compr.shape, dtype=int) for icol, irow in enumerate(row): compr_ref[irow, icol] = 1 np.testing.assert_allclose(compr.toarray(), compr_ref) symfc-1.6.0/tests/utils/test_graph.py000066400000000000000000000014321511276756400176710ustar00rootroot00000000000000"""Tests of graph.""" import numpy as np from scipy.sparse import csr_array from symfc.utils.graph import connected_components def test_connected_components(): """Test connected_component using DFS.""" mat = np.array( [ [5, 4, 0, 2, 0, 0, 0, 0], [4, 3, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 4, 0, 0, 0, 0], [0, 0, 0, 0, 5, 3, 0, 1], [0, 0, 0, 0, 3, 4, 1, 2], [0, 0, 0, 0, 7, 8, 1, 0], [0, 0, 0, 0, 7, 2, 0, 1], ] ) mat = csr_array(mat + mat.T) group = connected_components(mat) np.testing.assert_array_equal(group[0], [0, 1, 3]) np.testing.assert_array_equal(group[1], [2]) np.testing.assert_array_equal(group[2], [4, 5, 6, 7]) symfc-1.6.0/tests/utils/test_permutation_tools_O2.py000066400000000000000000000044171511276756400227250ustar00rootroot00000000000000"""Tests of functions in permutation_tools_O2.""" import numpy as np import pytest from symfc.spg_reps import SpgRepsBase from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.permutation_tools_O2 import ( _N3N3_to_NNand33, compr_permutation_lat_trans_O2, ) from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import _get_atomic_lat_trans_decompr_indices def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_N3N3_to_NNand33(): """Test N3N3_to_NNand33.""" N = 3 combs = np.array([[0, 1], [2, 4], [5, 8]]) vecNN, vec33 = _N3N3_to_NNand33(combs, N) np.testing.assert_allclose(vecNN, [0, 1, 5]) np.testing.assert_allclose(vec33, [1, 7, 8]) def test_projector_permutation_lat_trans_O2(): """Test projector_permutation_lat_trans_O2.""" atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) c_pt = compr_permutation_lat_trans_O2( trans_perms, atomic_decompr_idx=atomic_decompr_idx, fc_cutoff=None, ) proj = c_pt @ c_pt.T assert proj.trace() == pytest.approx(12.0) assert proj.shape == (18, 18) proj_ref = np.zeros(proj.shape) proj_ref[([0, 4, 8, 9, 13, 17], [0, 4, 8, 9, 13, 17])] = 1.0 for iset in [[1, 3], [2, 6], [5, 7], [10, 12], [11, 15], [14, 16]]: row, col = np.meshgrid(*[iset, iset]) row = row.reshape(-1) col = col.reshape(-1) proj_ref[(row, col)] = 0.5 np.testing.assert_allclose(proj.toarray(), proj_ref) c_pt = compr_permutation_lat_trans_O2( trans_perms, atomic_decompr_idx=atomic_decompr_idx, fc_cutoff=FCCutoff(supercell, cutoff=1), ) proj = c_pt @ c_pt.T proj_ref_cutoff = np.zeros_like(proj_ref) proj_ref_cutoff[0:9, 0:9] = proj_ref[0:9, 0:9] assert proj.trace() == pytest.approx(6.0) assert proj.shape == (18, 18) np.testing.assert_allclose(proj.toarray(), proj_ref_cutoff) symfc-1.6.0/tests/utils/test_permutation_tools_O3.py000066400000000000000000000043071511276756400227240ustar00rootroot00000000000000"""Tests of functions in permutation_tools_O3.""" import numpy as np import pytest from symfc.spg_reps import SpgRepsBase from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.permutation_tools_O3 import ( _N3N3N3_to_NNNand333, compr_permutation_lat_trans_O3, ) from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O3 import get_atomic_lat_trans_decompr_indices_O3 def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_N3N3N3_to_NNNand333(): """Test N3N33_to_NNNand333.""" N = 3 combs = np.array([[0, 1, 2], [2, 4, 6], [3, 5, 8]]) vecNNN, vec333 = _N3N3N3_to_NNNand333(combs, N) np.testing.assert_allclose(vecNNN, [0, 5, 14]) np.testing.assert_allclose(vec333, [5, 21, 8]) def test_projector_permutation_lat_trans_O3(): """Test projector_permutation_lat_trans_O3.""" atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) c_pt = compr_permutation_lat_trans_O3( trans_perms, atomic_decompr_idx=atomic_decompr_idx, fc_cutoff=None, ) proj = c_pt @ c_pt.T assert proj.trace() == pytest.approx(28.0) assert proj.shape == (108, 108) assert len(proj.data) == 498 assert np.count_nonzero(np.isclose(proj.data, 1)) == 3 assert np.count_nonzero(np.isclose(proj.data, 1.0 / 3.0)) == 135 assert np.count_nonzero(np.isclose(proj.data, 1.0 / 6.0)) == 360 c_pt = compr_permutation_lat_trans_O3( trans_perms, atomic_decompr_idx=atomic_decompr_idx, fc_cutoff=FCCutoff(supercell, cutoff=1), ) proj = c_pt @ c_pt.T assert proj.trace() == pytest.approx(10.0) assert len(proj.data) == 93 assert np.count_nonzero(np.isclose(proj.data, 1)) == 3 assert np.count_nonzero(np.isclose(proj.data, 1.0 / 3.0)) == 54 assert np.count_nonzero(np.isclose(proj.data, 1.0 / 6.0)) == 36 symfc-1.6.0/tests/utils/test_permutation_tools_O4.py000066400000000000000000000007171511276756400227260ustar00rootroot00000000000000"""Tests of functions in permutation_tools_O4.""" import numpy as np from symfc.utils.permutation_tools_O4 import ( _N3N3N3N3_to_NNNNand3333, ) def test_N3N3N3N3_to_NNNNand3333(): """Test N3N33_to_NNNand333.""" N = 3 combs = np.array([[0, 1, 2, 5], [2, 4, 6, 8], [3, 4, 5, 8]]) vecNNNN, vec3333 = _N3N3N3N3_to_NNNNand3333(combs, N) np.testing.assert_allclose(vecNNNN, [1, 17, 41]) np.testing.assert_allclose(vec3333, [17, 65, 17]) symfc-1.6.0/tests/utils/test_solver_funcs.py000066400000000000000000000032611511276756400213020ustar00rootroot00000000000000"""Tests of functions in solver_funcs.""" import numpy as np from symfc.utils.solver_funcs import ( fit, get_batch_slice, get_displacement_sparse_matrix, ) def test_fit(): """Test linear regression fit.""" X = np.array( [ [1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 4.0, 8.0], [1.0, 4.0, 16.0, 64.0], [1.0, 6.0, 36.0, 216.0], [1.0, 8.0, 64.0, 512.0], [1.0, 10.0, 100.0, 1000.0], ] ) y = np.array([14.1, 41.9, 179.2, 458.7, 931.0, 1642.6]) coefs = fit(X, y) np.testing.assert_allclose(coefs, [3.18818393, 3.46030191, 6.13455974, 0.99131806]) def test_batch_slice(): """Test get_batch_slice.""" begin, end = get_batch_slice(n_data=500, batch_size=50) assert len(begin) == len(end) == 10 diff = np.array(end) - np.array(begin) np.testing.assert_array_equal(diff, [50] * 10) begin, end = get_batch_slice(n_data=1000, batch_size=36) assert len(begin) == len(end) == 28 diff = np.array(end) - np.array(begin) np.testing.assert_array_equal(diff[:-1], [36] * 27) assert diff[-1] == 28 def test_sparse_matrix(): """Test get_displacement_sparse_matrix.""" n_atom = 64 atoms = np.array([0, 0, 32, 32]) displacements = [ [0.01, 0.0, 0.02], [0.0, 0.01, 0.0], [0.01, 0.02, 0.0], [0.0, 0.01, 0.0], ] displacements = np.array(displacements) mat = get_displacement_sparse_matrix(atoms, displacements, n_atom, tol=1e-15) rows, cols = mat.nonzero() assert list(rows) == [0, 0, 1, 2, 2, 3] assert list(cols) == [0, 2, 1, 96, 97, 97] assert list(mat.data) == [0.01, 0.02, 0.01, 0.01, 0.02, 0.01] symfc-1.6.0/tests/utils/test_translation_tools_O2.py000066400000000000000000000032051511276756400227060ustar00rootroot00000000000000"""Tests of functions in translation_tools_O2.""" import numpy as np import pytest import scipy from symfc.spg_reps import SpgRepsBase from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.translation_tools_O2 import compressed_projector_sum_rules_O2 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import _get_atomic_lat_trans_decompr_indices def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_compressed_projector_sum_rules_O2(): """Test compressed_projector_sum_rules_O2.""" atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) n_a_compress_mat = scipy.sparse.identity(18) proj = compressed_projector_sum_rules_O2( trans_perms, n_a_compress_mat, atomic_decompr_idx, fc_cutoff=None, ) eigvals, _ = np.linalg.eigh(proj.toarray()) assert proj.shape == (18, 18) assert np.count_nonzero(np.isclose(eigvals, 1.0)) == 9 proj = compressed_projector_sum_rules_O2( trans_perms, n_a_compress_mat, atomic_decompr_idx, fc_cutoff=FCCutoff(supercell, cutoff=1), ) """If the cutoff implementation is changed, the trace value may also change.""" assert proj.trace() == pytest.approx(13.5) assert len(proj.data) == 18 symfc-1.6.0/tests/utils/test_translation_tools_O3.py000066400000000000000000000032161511276756400227110ustar00rootroot00000000000000"""Tests of functions in translation_tools_O3.""" import numpy as np import pytest import scipy from symfc.spg_reps import SpgRepsBase from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.translation_tools_O3 import compressed_projector_sum_rules_O3 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O3 import get_atomic_lat_trans_decompr_indices_O3 def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_compressed_projector_sum_rules_O3(): """Test compressed_projector_sum_rules_O3.""" atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) n_a_compress_mat = scipy.sparse.identity(108) proj = compressed_projector_sum_rules_O3( trans_perms, n_a_compress_mat, atomic_decompr_idx, fc_cutoff=None, ) eigvals, _ = np.linalg.eigh(proj.toarray()) assert proj.shape == (108, 108) assert np.count_nonzero(np.isclose(eigvals, 1.0)) == 54 proj = compressed_projector_sum_rules_O3( trans_perms, n_a_compress_mat, atomic_decompr_idx, fc_cutoff=FCCutoff(supercell, cutoff=1), ) """If the cutoff implementation is changed, the trace value may also change.""" assert proj.trace() == pytest.approx(94.5) assert len(proj.data) == 108 symfc-1.6.0/tests/utils/test_utils.py000066400000000000000000000433031511276756400177330ustar00rootroot00000000000000"""Tests of matrix manipulating functions.""" from __future__ import annotations from pathlib import Path from typing import cast import numpy as np import pytest from numpy.typing import NDArray from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import ( SymfcAtoms, compute_sg_permutations, compute_sg_permutations_stable, get_indep_atoms_by_lat_trans, ) cwd = Path(__file__).parent def test_get_indep_atoms_by_lattice_translation( ph_nacl_222: tuple[SymfcAtoms, NDArray, NDArray], ): """Test of get_indep_atoms_by_lattice_translation.""" supercell, _, _ = ph_nacl_222 sym_op_reps = SpgRepsBase(supercell) trans_perms = sym_op_reps.translation_permutations assert trans_perms.shape == (32, 64) indep_atoms = get_indep_atoms_by_lat_trans(trans_perms) np.testing.assert_array_equal(indep_atoms, [0, 32]) def test_compute_sg_permutations( ph_gan_222: tuple[SymfcAtoms, NDArray, NDArray], cell_gan_111: SymfcAtoms ): """Test compute_sg_permutations.""" pytest.importorskip("spglib") import spglib from spglib.spglib import Cell as SpgCell supercell, _, _ = ph_gan_222 primitive = cell_gan_111 dataset = spglib.get_symmetry_dataset(cast(SpgCell, supercell.totuple())) assert dataset is not None primitive_dataset = spglib.get_symmetry_dataset(cast(SpgCell, primitive.totuple())) assert primitive_dataset is not None perms = compute_sg_permutations( primitive.scaled_positions, primitive_dataset.rotations, primitive_dataset.translations, primitive.cell, ) ref_perms = [ [0, 1, 2, 3], [1, 0, 3, 2], [0, 1, 2, 3], [1, 0, 3, 2], [0, 1, 2, 3], [1, 0, 3, 2], [1, 0, 3, 2], [0, 1, 2, 3], [1, 0, 3, 2], [0, 1, 2, 3], [1, 0, 3, 2], [0, 1, 2, 3], ] np.testing.assert_array_equal(ref_perms, perms) perms_super = compute_sg_permutations( supercell.scaled_positions, dataset.rotations, dataset.translations, supercell.cell, ) # np.savetxt("perms_super.dat", perms_super, fmt="%d") perms_super_ref = np.loadtxt(cwd / ".." / "perms_super.dat", dtype=int) np.testing.assert_array_equal(perms_super_ref, perms_super) def test_compute_sg_permutations_compare_stable(): """Test compute_sg_permutations and compare with compute_sg_permutations_stable.""" pytest.importorskip("spglib") import spglib from spglib.spglib import Cell as SpgCell axis = np.array([[0.0, 1.0, 1.0], [7.0, 6.0, 7.0], [8.0, 8.0, 8.0]]) positions = np.array( [ [0.000000000000000, 0.000000000000000, 0.000000000000000], [0.000000000000000, 0.000000000000000, 0.125000000000000], [0.000000000000000, 0.000000000000000, 0.250000000000000], [0.000000000000000, 0.000000000000000, 0.375000000000000], [0.000000000000000, 0.000000000000000, 0.500000000000000], [0.000000000000000, 0.000000000000000, 0.625000000000000], [0.000000000000000, 0.000000000000000, 0.750000000000000], [0.000000000000000, 0.000000000000000, 0.875000000000000], ] ) types = np.zeros(8, dtype=int) cell = axis, positions, types dataset = spglib.get_symmetry_dataset(cast(SpgCell, cell)) assert dataset is not None perms = compute_sg_permutations( positions, dataset.rotations, dataset.translations, axis, ) perms_stable = compute_sg_permutations_stable( positions, dataset.rotations, dataset.translations, axis, ) ref_perms = [ [0, 1, 2, 3, 4, 5, 6, 7], [0, 7, 6, 5, 4, 3, 2, 1], [0, 7, 6, 5, 4, 3, 2, 1], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 7, 6, 5, 4, 3, 2, 1], [0, 7, 6, 5, 4, 3, 2, 1], [0, 1, 2, 3, 4, 5, 6, 7], [0, 7, 6, 5, 4, 3, 2, 1], [0, 1, 2, 3, 4, 5, 6, 7], [0, 1, 2, 3, 4, 5, 6, 7], [0, 7, 6, 5, 4, 3, 2, 1], [1, 2, 3, 4, 5, 6, 7, 0], [1, 0, 7, 6, 5, 4, 3, 2], [1, 0, 7, 6, 5, 4, 3, 2], [1, 2, 3, 4, 5, 6, 7, 0], [1, 2, 3, 4, 5, 6, 7, 0], [1, 0, 7, 6, 5, 4, 3, 2], [1, 0, 7, 6, 5, 4, 3, 2], [1, 2, 3, 4, 5, 6, 7, 0], [1, 0, 7, 6, 5, 4, 3, 2], [1, 2, 3, 4, 5, 6, 7, 0], [1, 2, 3, 4, 5, 6, 7, 0], [1, 0, 7, 6, 5, 4, 3, 2], [2, 3, 4, 5, 6, 7, 0, 1], [2, 1, 0, 7, 6, 5, 4, 3], [2, 1, 0, 7, 6, 5, 4, 3], [2, 3, 4, 5, 6, 7, 0, 1], [2, 3, 4, 5, 6, 7, 0, 1], [2, 1, 0, 7, 6, 5, 4, 3], [2, 1, 0, 7, 6, 5, 4, 3], [2, 3, 4, 5, 6, 7, 0, 1], [2, 1, 0, 7, 6, 5, 4, 3], [2, 3, 4, 5, 6, 7, 0, 1], [2, 3, 4, 5, 6, 7, 0, 1], [2, 1, 0, 7, 6, 5, 4, 3], [3, 4, 5, 6, 7, 0, 1, 2], [3, 2, 1, 0, 7, 6, 5, 4], [3, 2, 1, 0, 7, 6, 5, 4], [3, 4, 5, 6, 7, 0, 1, 2], [3, 4, 5, 6, 7, 0, 1, 2], [3, 2, 1, 0, 7, 6, 5, 4], [3, 2, 1, 0, 7, 6, 5, 4], [3, 4, 5, 6, 7, 0, 1, 2], [3, 2, 1, 0, 7, 6, 5, 4], [3, 4, 5, 6, 7, 0, 1, 2], [3, 4, 5, 6, 7, 0, 1, 2], [3, 2, 1, 0, 7, 6, 5, 4], [4, 5, 6, 7, 0, 1, 2, 3], [4, 3, 2, 1, 0, 7, 6, 5], [4, 3, 2, 1, 0, 7, 6, 5], [4, 5, 6, 7, 0, 1, 2, 3], [4, 5, 6, 7, 0, 1, 2, 3], [4, 3, 2, 1, 0, 7, 6, 5], [4, 3, 2, 1, 0, 7, 6, 5], [4, 5, 6, 7, 0, 1, 2, 3], [4, 3, 2, 1, 0, 7, 6, 5], [4, 5, 6, 7, 0, 1, 2, 3], [4, 5, 6, 7, 0, 1, 2, 3], [4, 3, 2, 1, 0, 7, 6, 5], [5, 6, 7, 0, 1, 2, 3, 4], [5, 4, 3, 2, 1, 0, 7, 6], [5, 4, 3, 2, 1, 0, 7, 6], [5, 6, 7, 0, 1, 2, 3, 4], [5, 6, 7, 0, 1, 2, 3, 4], [5, 4, 3, 2, 1, 0, 7, 6], [5, 4, 3, 2, 1, 0, 7, 6], [5, 6, 7, 0, 1, 2, 3, 4], [5, 4, 3, 2, 1, 0, 7, 6], [5, 6, 7, 0, 1, 2, 3, 4], [5, 6, 7, 0, 1, 2, 3, 4], [5, 4, 3, 2, 1, 0, 7, 6], [6, 7, 0, 1, 2, 3, 4, 5], [6, 5, 4, 3, 2, 1, 0, 7], [6, 5, 4, 3, 2, 1, 0, 7], [6, 7, 0, 1, 2, 3, 4, 5], [6, 7, 0, 1, 2, 3, 4, 5], [6, 5, 4, 3, 2, 1, 0, 7], [6, 5, 4, 3, 2, 1, 0, 7], [6, 7, 0, 1, 2, 3, 4, 5], [6, 5, 4, 3, 2, 1, 0, 7], [6, 7, 0, 1, 2, 3, 4, 5], [6, 7, 0, 1, 2, 3, 4, 5], [6, 5, 4, 3, 2, 1, 0, 7], [7, 0, 1, 2, 3, 4, 5, 6], [7, 6, 5, 4, 3, 2, 1, 0], [7, 6, 5, 4, 3, 2, 1, 0], [7, 0, 1, 2, 3, 4, 5, 6], [7, 0, 1, 2, 3, 4, 5, 6], [7, 6, 5, 4, 3, 2, 1, 0], [7, 6, 5, 4, 3, 2, 1, 0], [7, 0, 1, 2, 3, 4, 5, 6], [7, 6, 5, 4, 3, 2, 1, 0], [7, 0, 1, 2, 3, 4, 5, 6], [7, 0, 1, 2, 3, 4, 5, 6], [7, 6, 5, 4, 3, 2, 1, 0], ] np.testing.assert_array_equal(perms, perms_stable) np.testing.assert_array_equal(perms, ref_perms) def test_compute_sg_permutations_compare_stable_nacl( ph_nacl_222: tuple[SymfcAtoms, NDArray, NDArray], ): """Test of compute_sg_permutations for NaCl.""" pytest.importorskip("spglib") import spglib from spglib.spglib import Cell as SpgCell supercell, _, _ = ph_nacl_222 dataset = spglib.get_symmetry_dataset(cast(SpgCell, supercell.totuple())) assert dataset is not None perms = compute_sg_permutations( supercell.scaled_positions, dataset.rotations, dataset.translations, supercell.cell, ) perms_stable = compute_sg_permutations_stable( supercell.scaled_positions, dataset.rotations, dataset.translations, supercell.cell, ) np.testing.assert_array_equal(perms, perms_stable) def test_compute_sg_permutations_compare_stable_sio2( ph_sio2_221: tuple[SymfcAtoms, NDArray, NDArray], ): """Test of compute_sg_permutations for SiO2.""" pytest.importorskip("spglib") import spglib from spglib.spglib import Cell as SpgCell supercell, _, _ = ph_sio2_221 dataset = spglib.get_symmetry_dataset(cast(SpgCell, supercell.totuple())) assert dataset is not None perms = compute_sg_permutations( supercell.scaled_positions, dataset.rotations, dataset.translations, supercell.cell, ) perms_stable = compute_sg_permutations_stable( supercell.scaled_positions, dataset.rotations, dataset.translations, supercell.cell, ) np.testing.assert_array_equal(perms, perms_stable) def test_compute_sg_permutations_compare_stable_baal2o4(): """Test compute_sg_permutations for BaAl2O4.""" pytest.importorskip("spglib") import spglib from spglib.spglib import Cell as SpgCell types = np.zeros(8, dtype=int) axis = np.array( [ [10.4020522437529994, 0.0000000000000000, 0.0000000000000000], [-5.2010261218764997, 9.0084414945829998, 0.0000000000000000], [0.00000000000000000, 0.0000000000000000, 17.8057491600000013], ] ) types = np.hstack( [np.zeros(16, dtype=int), np.ones(32, dtype=int), np.ones(64, dtype=int) * 2] ) positions = np.array( [ [0.0000000000000000, 0.0000000000000000, 0.1250000000000000], [0.5000000000000000, 0.0000000000000000, 0.1250000000000000], [0.0000000000000050, 0.5000000000000000, 0.1250000000000000], [0.4999999999999950, 0.5000000000000000, 0.1250000000000000], [0.0000000000000000, 0.0000000000000000, 0.6250000000000000], [0.5000000000000000, 0.0000000000000000, 0.6250000000000000], [0.0000000000000050, 0.5000000000000000, 0.6250000000000000], [0.4999999999999950, 0.5000000000000000, 0.6250000000000000], [0.0000000000000000, 0.0000000000000000, 0.3750000000000000], [0.5000000000000000, 0.0000000000000000, 0.3750000000000000], [0.0000000000000050, 0.5000000000000000, 0.3750000000000000], [0.4999999999999950, 0.5000000000000000, 0.3750000000000000], [0.0000000000000000, 0.0000000000000000, 0.8750000000000000], [0.5000000000000000, 0.0000000000000000, 0.8750000000000000], [0.0000000000000050, 0.5000000000000000, 0.8750000000000000], [0.4999999999999950, 0.5000000000000000, 0.8750000000000000], [0.1666666666666690, 0.3333333333333370, 0.2768253700000010], [0.6666666666666690, 0.3333333333333370, 0.2768253700000010], [0.1666666666666730, 0.8333333333333370, 0.2768253700000010], [0.6666666666666640, 0.8333333333333370, 0.2768253700000010], [0.1666666666666690, 0.3333333333333370, 0.7768253699999890], [0.6666666666666690, 0.3333333333333370, 0.7768253699999890], [0.1666666666666730, 0.8333333333333370, 0.7768253699999890], [0.6666666666666640, 0.8333333333333370, 0.7768253699999890], [0.3333333333333270, 0.1666666666666630, 0.0268253700000010], [0.8333333333333270, 0.1666666666666630, 0.0268253700000010], [0.3333333333333320, 0.6666666666666630, 0.0268253700000010], [0.8333333333333310, 0.6666666666666630, 0.0268253700000010], [0.3333333333333270, 0.1666666666666630, 0.5268253700000010], [0.8333333333333270, 0.1666666666666630, 0.5268253700000010], [0.3333333333333320, 0.6666666666666630, 0.5268253700000010], [0.8333333333333310, 0.6666666666666630, 0.5268253700000010], [0.3333333333333270, 0.1666666666666630, 0.2231746299999990], [0.8333333333333270, 0.1666666666666630, 0.2231746299999990], [0.3333333333333320, 0.6666666666666630, 0.2231746299999990], [0.8333333333333310, 0.6666666666666630, 0.2231746299999990], [0.3333333333333270, 0.1666666666666630, 0.7231746300000110], [0.8333333333333270, 0.1666666666666630, 0.7231746300000110], [0.3333333333333320, 0.6666666666666630, 0.7231746300000110], [0.8333333333333310, 0.6666666666666630, 0.7231746300000110], [0.1666666666666690, 0.3333333333333370, 0.4731746299999990], [0.6666666666666690, 0.3333333333333370, 0.4731746299999990], [0.1666666666666730, 0.8333333333333370, 0.4731746299999990], [0.6666666666666640, 0.8333333333333370, 0.4731746299999990], [0.1666666666666690, 0.3333333333333370, 0.9731746300000110], [0.6666666666666690, 0.3333333333333370, 0.9731746300000110], [0.1666666666666730, 0.8333333333333370, 0.9731746300000110], [0.6666666666666640, 0.8333333333333370, 0.9731746300000110], [0.1749127449999990, 0.1749127449999980, 0.2500000000000000], [0.6749127449999970, 0.1749127449999980, 0.2500000000000000], [0.1749127450000020, 0.6749127449999980, 0.2500000000000000], [0.6749127450000020, 0.6749127449999980, 0.2500000000000000], [0.1749127449999990, 0.1749127449999980, 0.7500000000000000], [0.6749127449999970, 0.1749127449999980, 0.7500000000000000], [0.1749127450000020, 0.6749127449999980, 0.7500000000000000], [0.6749127450000020, 0.6749127449999980, 0.7500000000000000], [0.0000000000000030, 0.3250872550000020, 0.2500000000000000], [0.5000000000000030, 0.3250872550000020, 0.2500000000000000], [0.9999999999999990, 0.8250872550000020, 0.2500000000000000], [0.5000000000000010, 0.8250872550000020, 0.2500000000000000], [0.0000000000000030, 0.3250872550000020, 0.7500000000000000], [0.5000000000000030, 0.3250872550000020, 0.7500000000000000], [0.9999999999999990, 0.8250872550000020, 0.7500000000000000], [0.5000000000000010, 0.8250872550000020, 0.7500000000000000], [0.9999999999999990, 0.1749127449999980, 0.0000000000000000], [0.5000000000000010, 0.1749127449999980, 0.0000000000000000], [0.9999999999999970, 0.6749127449999980, 0.0000000000000000], [0.4999999999999970, 0.6749127449999980, 0.0000000000000000], [0.9999999999999990, 0.1749127449999980, 0.5000000000000000], [0.5000000000000010, 0.1749127449999980, 0.5000000000000000], [0.9999999999999970, 0.6749127449999980, 0.5000000000000000], [0.4999999999999970, 0.6749127449999980, 0.5000000000000000], [0.1666666666666690, 0.3333333333333370, 0.3750000000000000], [0.6666666666666690, 0.3333333333333370, 0.3750000000000000], [0.1666666666666730, 0.8333333333333370, 0.3750000000000000], [0.6666666666666640, 0.8333333333333370, 0.3750000000000000], [0.1666666666666690, 0.3333333333333370, 0.8750000000000000], [0.6666666666666690, 0.3333333333333370, 0.8750000000000000], [0.1666666666666730, 0.8333333333333370, 0.8750000000000000], [0.6666666666666640, 0.8333333333333370, 0.8750000000000000], [0.3250872549999980, 0.3250872550000020, 0.0000000000000000], [0.8250872549999980, 0.3250872550000020, 0.0000000000000000], [0.3250872550000010, 0.8250872550000020, 0.0000000000000000], [0.8250872550000030, 0.8250872550000020, 0.0000000000000000], [0.3250872549999980, 0.3250872550000020, 0.5000000000000000], [0.8250872549999980, 0.3250872550000020, 0.5000000000000000], [0.3250872550000010, 0.8250872550000020, 0.5000000000000000], [0.8250872550000030, 0.8250872550000020, 0.5000000000000000], [0.3250872550000040, 0.0000000000000000, 0.2500000000000000], [0.8250872550000040, 0.0000000000000000, 0.2500000000000000], [0.3250872550000020, 0.5000000000000000, 0.2500000000000000], [0.8250872550000000, 0.5000000000000000, 0.2500000000000000], [0.3250872550000040, 0.0000000000000000, 0.7500000000000000], [0.8250872550000040, 0.0000000000000000, 0.7500000000000000], [0.3250872550000020, 0.5000000000000000, 0.7500000000000000], [0.8250872550000000, 0.5000000000000000, 0.7500000000000000], [0.3333333333333270, 0.1666666666666630, 0.1250000000000000], [0.8333333333333270, 0.1666666666666630, 0.1250000000000000], [0.3333333333333320, 0.6666666666666630, 0.1250000000000000], [0.8333333333333310, 0.6666666666666630, 0.1250000000000000], [0.3333333333333270, 0.1666666666666630, 0.6250000000000000], [0.8333333333333270, 0.1666666666666630, 0.6250000000000000], [0.3333333333333320, 0.6666666666666630, 0.6250000000000000], [0.8333333333333310, 0.6666666666666630, 0.6250000000000000], [0.1749127449999960, 0.0000000000000000, 0.0000000000000000], [0.6749127449999960, 0.0000000000000000, 0.0000000000000000], [0.1749127449999980, 0.5000000000000000, 0.0000000000000000], [0.6749127450000000, 0.5000000000000000, 0.0000000000000000], [0.1749127449999960, 0.0000000000000000, 0.5000000000000000], [0.6749127449999960, 0.0000000000000000, 0.5000000000000000], [0.1749127449999980, 0.5000000000000000, 0.5000000000000000], [0.6749127450000000, 0.5000000000000000, 0.5000000000000000], ] ) cell = axis, positions, types dataset = spglib.get_symmetry_dataset(cast(SpgCell, cell)) assert dataset is not None perms = compute_sg_permutations( positions, dataset.rotations, dataset.translations, axis, ) perms_stable = compute_sg_permutations_stable( positions, dataset.rotations, dataset.translations, axis, ) np.testing.assert_array_equal(perms, perms_stable) symfc-1.6.0/tests/utils/test_utils_O1.py000066400000000000000000000020031511276756400202620ustar00rootroot00000000000000"""Tests of functions in utils_O1.""" import numpy as np from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O1 import ( _get_atomic_lat_trans_decompr_indices, get_lat_trans_decompr_indices, ) def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_lat_trans_indices(): """Test lat_trans_indices.""" decompr_idx = get_lat_trans_decompr_indices(trans_perms) np.testing.assert_array_equal(decompr_idx, [0, 1, 2, 0, 1, 2]) atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) np.testing.assert_array_equal(atomic_decompr_idx, [0, 0]) symfc-1.6.0/tests/utils/test_utils_O2.py000066400000000000000000000266021511276756400202760ustar00rootroot00000000000000"""Tests of matrix manipulating functions for 2nd order force constants.""" import numpy as np from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( _get_atomic_lat_trans_decompr_indices, _get_perm_compr_matrix_reference, get_lat_trans_compr_indices, get_lat_trans_compr_matrix, get_lat_trans_decompr_indices, get_perm_compr_matrix, ) def test_get_perm_compr_matrix(): """Test of get_perm_compr_matrix.""" C1 = get_perm_compr_matrix(8) C2 = _get_perm_compr_matrix_reference(8) np.testing.assert_array_almost_equal((C1 @ C1.T).toarray(), (C2 @ C2.T).toarray()) np.testing.assert_array_almost_equal((C1.T @ C1).toarray(), (C2.T @ C2).toarray()) def test_get_lat_trans_decompr_indices(cell_nacl_111: SymfcAtoms): """Test of get_lat_trans_decompr_indices. The one dimensional array with row-size of compr-mat. Every element indicates column position. """ ref = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 9, 10, 11, 12, 13, 14, 15, 16, 17, 0, 1, 2, 3, 4, 5, 6, 7, 8, 27, 28, 29, 30, 31, 32, 33, 34, 35, 18, 19, 20, 21, 22, 23, 24, 25, 26, 45, 46, 47, 48, 49, 50, 51, 52, 53, 36, 37, 38, 39, 40, 41, 42, 43, 44, 63, 64, 65, 66, 67, 68, 69, 70, 71, 54, 55, 56, 57, 58, 59, 60, 61, 62, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 27, 28, 29, 30, 31, 32, 33, 34, 35, 18, 19, 20, 21, 22, 23, 24, 25, 26, 9, 10, 11, 12, 13, 14, 15, 16, 17, 0, 1, 2, 3, 4, 5, 6, 7, 8, 63, 64, 65, 66, 67, 68, 69, 70, 71, 54, 55, 56, 57, 58, 59, 60, 61, 62, 45, 46, 47, 48, 49, 50, 51, 52, 53, 36, 37, 38, 39, 40, 41, 42, 43, 44, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 81, 82, 83, 84, 85, 86, 87, 88, 89, 72, 73, 74, 75, 76, 77, 78, 79, 80, 99, 100, 101, 102, 103, 104, 105, 106, 107, 90, 91, 92, 93, 94, 95, 96, 97, 98, 117, 118, 119, 120, 121, 122, 123, 124, 125, 108, 109, 110, 111, 112, 113, 114, 115, 116, 135, 136, 137, 138, 139, 140, 141, 142, 143, 126, 127, 128, 129, 130, 131, 132, 133, 134, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 99, 100, 101, 102, 103, 104, 105, 106, 107, 90, 91, 92, 93, 94, 95, 96, 97, 98, 81, 82, 83, 84, 85, 86, 87, 88, 89, 72, 73, 74, 75, 76, 77, 78, 79, 80, 135, 136, 137, 138, 139, 140, 141, 142, 143, 126, 127, 128, 129, 130, 131, 132, 133, 134, 117, 118, 119, 120, 121, 122, 123, 124, 125, 108, 109, 110, 111, 112, 113, 114, 115, 116, ] trans_perms_ref = np.array( [ [0, 1, 2, 3, 4, 5, 6, 7], [3, 2, 1, 0, 7, 6, 5, 4], [2, 3, 0, 1, 6, 7, 4, 5], [1, 0, 3, 2, 5, 4, 7, 6], ], dtype=int, ) spg_reps = SpgRepsBase(cell_nacl_111) trans_perms = spg_reps.translation_permutations assert trans_perms.shape == (4, 8) check_trans_perms(trans_perms, trans_perms_ref) decompr_idx = get_lat_trans_decompr_indices(trans_perms_ref) np.testing.assert_array_equal(ref, decompr_idx) def test_get_lat_trans_compr_indices(cell_nacl_111: SymfcAtoms): """Test get_lat_trans_compr_indices. The two dimensional array (n_a * N * 9, n_lp) stores NN33 indices where compression matrix elements are non-zero. """ trans_perms_ref = np.array( [ [0, 1, 2, 3, 4, 5, 6, 7], [3, 2, 1, 0, 7, 6, 5, 4], [2, 3, 0, 1, 6, 7, 4, 5], [1, 0, 3, 2, 5, 4, 7, 6], ], dtype=int, ) spg_reps = SpgRepsBase(cell_nacl_111) trans_perms = spg_reps.translation_permutations n_lp, N = trans_perms.shape assert trans_perms.shape == (4, 8) check_trans_perms(trans_perms, trans_perms_ref) decompr_mat = get_lat_trans_decompr_indices(trans_perms_ref) compr_mat = get_lat_trans_compr_matrix(decompr_mat, N, n_lp).toarray() compr_idx = get_lat_trans_compr_indices(trans_perms_ref) for c, elem_idx in enumerate(compr_idx): for r in elem_idx: np.testing.assert_almost_equal(compr_mat[r, c], 0.5) def test_get_atomic_lat_trans_decompr_indices(cell_nacl_111: SymfcAtoms): """Test of get_atomic_lat_trans_decompr_indices. This function is an atomic version of get_lat_trans_decompr_indices. The one dimensional array with row-size of compr-mat. Every element indicates column position. """ ref = [ 0, 1, 2, 3, 4, 5, 6, 7, 1, 0, 3, 2, 5, 4, 7, 6, 2, 3, 0, 1, 6, 7, 4, 5, 3, 2, 1, 0, 7, 6, 5, 4, 8, 9, 10, 11, 12, 13, 14, 15, 9, 8, 11, 10, 13, 12, 15, 14, 10, 11, 8, 9, 14, 15, 12, 13, 11, 10, 9, 8, 15, 14, 13, 12, ] trans_perms_ref = np.array( [ [0, 1, 2, 3, 4, 5, 6, 7], [3, 2, 1, 0, 7, 6, 5, 4], [2, 3, 0, 1, 6, 7, 4, 5], [1, 0, 3, 2, 5, 4, 7, 6], ], dtype=int, ) spg_reps = SpgRepsBase(cell_nacl_111) trans_perms = spg_reps.translation_permutations assert trans_perms.shape == (4, 8) check_trans_perms(trans_perms, trans_perms_ref) atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms_ref) np.testing.assert_array_equal(ref, atomic_decompr_idx) def check_trans_perms(trans_perms, trans_perms_ref): """Check the order of rows of trans_perms. The row order of trans_perms may depend on symmetry finding condition. """ idxs = [] for row in trans_perms: match = np.where(((trans_perms_ref - row) == 0).all(axis=1))[0] assert len(match) == 1 idxs.append(match[0]) np.testing.assert_array_equal(np.sort(idxs), range(len(idxs))) symfc-1.6.0/tests/utils/test_utils_O2_2.py000066400000000000000000000036671511276756400205250ustar00rootroot00000000000000"""Tests of functions in utils_O2.""" import numpy as np import pytest from symfc.spg_reps import SpgRepsO2 from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O2 import ( _get_atomic_lat_trans_decompr_indices, get_compr_coset_projector_O2, get_lat_trans_compr_matrix, get_lat_trans_decompr_indices, ) def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsO2(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms, spg_reps supercell, trans_perms, spg_reps = structure_bcc() def test_lat_trans(): """Test lat_trans_indices and lat_trans_compr_matrix.""" decompr_idx = get_lat_trans_decompr_indices(trans_perms) atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) np.testing.assert_array_equal(atomic_decompr_idx, [0, 1, 1, 0]) decompr_idx_from_atomic = ( atomic_decompr_idx[:, None] * 9 + np.arange(9)[None, :] ).reshape(-1) np.testing.assert_array_equal(decompr_idx, decompr_idx_from_atomic) N, n_lp = trans_perms.shape c_trans = get_lat_trans_compr_matrix(decompr_idx, N, n_lp) row, col = c_trans.nonzero() np.testing.assert_array_equal(decompr_idx, col) np.testing.assert_allclose(c_trans.data, [0.7071067811865475] * len(decompr_idx)) def test_coset_projector_O2(): """Test get_compr_coset_projector_O2.""" atomic_decompr_idx = _get_atomic_lat_trans_decompr_indices(trans_perms) coset = get_compr_coset_projector_O2(spg_reps, atomic_decompr_idx) assert coset.trace() == pytest.approx(2.0) assert np.sum(coset.data) == pytest.approx(6.0) for irow in [1, 2, 3, 5, 6, 7, 10, 11, 12, 14, 15, 16]: assert coset[[irow]].sum() == pytest.approx(0.0) symfc-1.6.0/tests/utils/test_utils_O3.py000066400000000000000000000055161511276756400203000ustar00rootroot00000000000000"""Tests of functions in utils_O3.""" import numpy as np import pytest from symfc.spg_reps import SpgRepsO3 from symfc.utils.cutoff_tools import FCCutoff from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O3 import ( get_atomic_lat_trans_decompr_indices_O3, get_compr_coset_projector_O3, get_lat_trans_compr_matrix_O3, get_lat_trans_decompr_indices_O3, ) def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsO3(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms, spg_reps def structure_bcc_rev(): """Get modified bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.49, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsO3(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms, spg_reps supercell, trans_perms, spg_reps = structure_bcc() def test_lat_trans(): """Test lat_trans_indices and lat_trans_compr_matrix.""" decompr_idx = get_lat_trans_decompr_indices_O3(trans_perms) atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) np.testing.assert_array_equal(atomic_decompr_idx, [0, 1, 2, 3, 3, 2, 1, 0]) decompr_idx_from_atomic = ( atomic_decompr_idx[:, None] * 27 + np.arange(27)[None, :] ).reshape(-1) np.testing.assert_array_equal(decompr_idx, decompr_idx_from_atomic) c_trans = get_lat_trans_compr_matrix_O3(trans_perms) row, col = c_trans.nonzero() np.testing.assert_array_equal(decompr_idx, col) np.testing.assert_allclose(c_trans.data, [0.7071067811865475] * len(decompr_idx)) def test_coset_projector_O3(): """Test get_compr_coset_projector_O3.""" atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms) coset = get_compr_coset_projector_O3(spg_reps, atomic_decompr_idx) assert coset.trace() == pytest.approx(0.0) assert np.sum(coset.data) == pytest.approx(0.0) supercell_rev, trans_perms_rev, spg_reps_rev = structure_bcc_rev() atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O3(trans_perms_rev) coset = get_compr_coset_projector_O3(spg_reps_rev, atomic_decompr_idx) assert coset.trace() == pytest.approx(16.0) assert np.sum(coset.data) == pytest.approx(0.0) coset = get_compr_coset_projector_O3( spg_reps_rev, atomic_decompr_idx, fc_cutoff=FCCutoff(supercell_rev, cutoff=1), ) assert coset.trace() == pytest.approx(4.0) assert np.sum(coset.data) == pytest.approx(0.0) symfc-1.6.0/tests/utils/test_utils_O4.py000066400000000000000000000027311511276756400202750ustar00rootroot00000000000000"""Tests of functions in utils_O4.""" import numpy as np from symfc.spg_reps import SpgRepsBase from symfc.utils.utils import SymfcAtoms from symfc.utils.utils_O4 import ( get_atomic_lat_trans_decompr_indices_O4, get_lat_trans_compr_matrix_O4, get_lat_trans_decompr_indices_O4, ) def structure_bcc(): """Get bcc structure.""" lattice = np.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]) positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5]]) numbers = [1, 1] supercell = SymfcAtoms(cell=lattice, scaled_positions=positions, numbers=numbers) spg_reps = SpgRepsBase(supercell) trans_perms = spg_reps.translation_permutations return supercell, trans_perms supercell, trans_perms = structure_bcc() def test_lat_trans(): """Test lat_trans_indices and lat_trans_compr_matrix.""" decompr_idx = get_lat_trans_decompr_indices_O4(trans_perms) atomic_decompr_idx = get_atomic_lat_trans_decompr_indices_O4(trans_perms) np.testing.assert_array_equal( atomic_decompr_idx, [0, 1, 2, 3, 4, 5, 6, 7, 7, 6, 5, 4, 3, 2, 1, 0] ) decompr_idx_from_atomic = ( atomic_decompr_idx[:, None] * 81 + np.arange(81)[None, :] ).reshape(-1) np.testing.assert_array_equal(decompr_idx, decompr_idx_from_atomic) c_trans = get_lat_trans_compr_matrix_O4(trans_perms) row, col = c_trans.nonzero() np.testing.assert_array_equal(decompr_idx, col) np.testing.assert_allclose(c_trans.data, [0.7071067811865475] * len(decompr_idx))