From 8c34232ca2cb1d76d552951085da62ae192b6cd9 Mon Sep 17 00:00:00 2001 From: Jacob Wilkins Date: Thu, 30 May 2024 13:39:37 +0100 Subject: [PATCH] Add post-processing routines to MD --- docs/source/apidoc/janus_core.rst | 10 + janus_core/calculations/md.py | 107 ++- janus_core/cli/md.py | 21 +- janus_core/cli/types.py | 14 + janus_core/helpers/janus_types.py | 29 +- janus_core/helpers/post_process.py | 288 +++++++ tests/data/lj-traj.xyz | 1122 ++++++++++++++++++++++++++++ tests/test_md.py | 1 - tests/test_post_process.py | 194 +++++ 9 files changed, 1777 insertions(+), 9 deletions(-) create mode 100644 janus_core/helpers/post_process.py create mode 100644 tests/data/lj-traj.xyz create mode 100644 tests/test_post_process.py diff --git a/docs/source/apidoc/janus_core.rst b/docs/source/apidoc/janus_core.rst index d4060eb9..3016d686 100644 --- a/docs/source/apidoc/janus_core.rst +++ b/docs/source/apidoc/janus_core.rst @@ -164,6 +164,16 @@ janus\_core.helpers.descriptors module :undoc-members: :show-inheritance: +janus\_core.helpers.post_process module +--------------------------------------- + +.. automodule:: janus_core.helpers.post_process + :members: + :special-members: + :private-members: + :undoc-members: + :show-inheritance: + janus\_core.helpers.train module -------------------------------- diff --git a/janus_core/calculations/md.py b/janus_core/calculations/md.py index c480a4d3..9f8b0888 100644 --- a/janus_core/calculations/md.py +++ b/janus_core/calculations/md.py @@ -2,14 +2,16 @@ """Run molecular dynamics simulations.""" import datetime +from itertools import combinations_with_replacement from math import isclose from pathlib import Path import random -from typing import Any, Optional +from typing import Any, Optional, Union from warnings import warn from ase import Atoms, units -from ase.io import write +from ase.geometry.analysis import Analysis +from ase.io import read, write from ase.md.langevin import Langevin from ase.md.npt import NPT as ASE_NPT from ase.md.velocitydistribution import ( @@ -21,8 +23,9 @@ import numpy as np from janus_core.calculations.geom_opt import optimize -from janus_core.helpers.janus_types import Ensembles, PathLike +from janus_core.helpers.janus_types import Ensembles, PathLike, PostProcessKwargs from janus_core.helpers.log import config_logger +from janus_core.helpers.post_process import compute_rdf, compute_vaf from janus_core.helpers.utils import FileNameMixin DENS_FACT = (units.m / 1.0e2) ** 3 / units.mol @@ -97,6 +100,8 @@ class MolecularDynamics(FileNameMixin): # pylint: disable=too-many-instance-att heating. temp_time : Optional[float] Time between heating steps, in fs. Default is None, which disables heating. + post_process_kwargs : Optional[PostProcessKwargs] + Keyword arguments to control post-processing operations. log_kwargs : Optional[dict[str, Any]] Keyword arguments to pass to log config. Default is None. seed : Optional[int] @@ -157,6 +162,7 @@ def __init__( # pylint: disable=too-many-arguments,too-many-locals,too-many-sta temp_end: Optional[float] = None, temp_step: Optional[float] = None, temp_time: Optional[float] = None, + post_process_kwargs: Optional[PostProcessKwargs] = None, log_kwargs: Optional[dict[str, Any]] = None, seed: Optional[int] = None, ) -> None: @@ -231,6 +237,8 @@ def __init__( # pylint: disable=too-many-arguments,too-many-locals,too-many-sta disables heating. temp_time : Optional[float] Time between heating steps, in fs. Default is None, which disables heating. + post_process_kwargs : Optional[PostProcessKwargs] + Keyword arguments to control post-processing operations. log_kwargs : Optional[dict[str, Any]] Keyword arguments to pass to log config. Default is None. seed : Optional[int] @@ -262,6 +270,9 @@ def __init__( # pylint: disable=too-many-arguments,too-many-locals,too-many-sta self.temp_end = temp_end self.temp_step = temp_step self.temp_time = temp_time * units.fs if temp_time else None + self.post_process_kwargs = ( + post_process_kwargs if post_process_kwargs is not None else {} + ) self.log_kwargs = log_kwargs self.ensemble = ensemble self.seed = seed @@ -315,7 +326,7 @@ def __init__( # pylint: disable=too-many-arguments,too-many-locals,too-many-sta self.minimize_kwargs = minimize_kwargs if minimize_kwargs else {} self.restart_files = [] - self.dyn = None + self.dyn: Union[Langevin, VelocityVerlet, ASE_NPT] self.n_atoms = len(self.struct) self.stats_file = self._build_filename( @@ -542,6 +553,91 @@ def _write_final_state(self) -> None: columns=["symbols", "positions", "momenta", "masses"], ) + def _post_process(self) -> None: + """Compute properties after MD run.""" + # Nothing to do + if not any( + self.post_process_kwargs.get(kwarg, None) + for kwarg in ("rdf_compute", "vaf_compute") + ): + warn( + "Post-processing arguments present, but no computation requested. " + "Please set either 'rdf_compute' or 'vaf_compute' " + "to do post-processing." + ) + + data = read(self.traj_file, index=":") + + ana = Analysis(data) + + param_pref = self._parameter_prefix if self.file_prefix is None else "" + + if self.post_process_kwargs.get("rdf_compute", False): + base_name = self.post_process_kwargs.get("rdf_output_file", None) + rdf_args = { + name: self.post_process_kwargs.get(key, default) + for name, (key, default) in ( + ("rmax", ("rdf_rmax", 2.5)), + ("nbins", ("rdf_nbins", 50)), + ("elements", ("rdf_elements", None)), + ("by_elements", ("rdf_by_elements", False)), + ) + } + slice_ = ( + self.post_process_kwargs.get("rdf_start", len(data) - 1), + self.post_process_kwargs.get("rdf_stop", len(data)), + self.post_process_kwargs.get("rdf_step", 1), + ) + rdf_args["index"] = slice_ + + if rdf_args["by_elements"]: + elements = ( + tuple(sorted(set(data[0].get_chemical_symbols()))) + if rdf_args["elements"] is None + else rdf_args["elements"] + ) + + out_paths = [ + self._build_filename( + "rdf.dat", + param_pref, + "_".join(element), + prefix_override=base_name, + ) + for element in combinations_with_replacement(elements, 2) + ] + + else: + out_paths = [ + self._build_filename( + "rdf.dat", param_pref, prefix_override=base_name + ) + ] + + compute_rdf(data, ana, filename=out_paths, **rdf_args) + + if self.post_process_kwargs.get("vaf_compute", False): + + file_name = self.post_process_kwargs.get("vaf_output_file", None) + use_vel = self.post_process_kwargs.get("vaf_velocities", False) + fft = self.post_process_kwargs.get("vaf_fft", False) + + out_path = self._build_filename("vaf.dat", param_pref, filename=file_name) + slice_ = ( + self.post_process_kwargs.get("vaf_start", 0), + self.post_process_kwargs.get("vaf_stop", None), + self.post_process_kwargs.get("vaf_step", 1), + ) + + compute_vaf( + data, + out_path, + use_velocities=use_vel, + fft=fft, + index=slice_, + filter_atoms=self.post_process_kwargs.get("vaf_atoms", None), + ) + def _write_restart(self) -> None: """Write restart file and (optionally) rotate files saved.""" step = self.offset + self.dyn.nsteps @@ -594,6 +690,9 @@ def run(self) -> None: self.struct.info["real_time"] = datetime.datetime.now() self._run_dynamics() + if self.post_process_kwargs: + self._post_process() + def _run_dynamics(self) -> None: """Run dynamics and/or temperature ramp.""" # Store temperature for final MD diff --git a/janus_core/cli/md.py b/janus_core/cli/md.py index db799a54..7bb28566 100644 --- a/janus_core/cli/md.py +++ b/janus_core/cli/md.py @@ -15,6 +15,7 @@ EnsembleKwargs, LogPath, MinimizeKwargs, + PostProcessKwargs, ReadKwargs, StructPath, Summary, @@ -170,6 +171,7 @@ def md( temp_time: Annotated[ float, Option(help="Time between heating steps, in fs.") ] = None, + post_process_kwargs: PostProcessKwargs = None, log: LogPath = "md.log", seed: Annotated[ Optional[int], @@ -271,6 +273,8 @@ def md( temp_time : Optional[float] Time between heating steps, in fs. Default is None, which disables heating. + post_process_kwargs : Optional[PostProcessKwargs] + Kwargs to pass to post-processing. log : Optional[Path] Path to write logs to. Default is "md.log". seed : Optional[int] @@ -284,8 +288,20 @@ def md( # Check options from configuration file are all valid check_config(ctx) - [read_kwargs, calc_kwargs, minimize_kwargs, ensemble_kwargs] = parse_typer_dicts( - [read_kwargs, calc_kwargs, minimize_kwargs, ensemble_kwargs] + [ + read_kwargs, + calc_kwargs, + minimize_kwargs, + ensemble_kwargs, + post_process_kwargs, + ] = parse_typer_dicts( + [ + read_kwargs, + calc_kwargs, + minimize_kwargs, + ensemble_kwargs, + post_process_kwargs, + ] ) if not ensemble in get_args(Ensembles): @@ -338,6 +354,7 @@ def md( "temp_end": temp_end, "temp_step": temp_step, "temp_time": temp_time, + "post_process_kwargs": post_process_kwargs, "log_kwargs": log_kwargs, "seed": seed, "ensemble_kwargs": ensemble_kwargs, diff --git a/janus_core/cli/types.py b/janus_core/cli/types.py index df4938dd..c84440d3 100644 --- a/janus_core/cli/types.py +++ b/janus_core/cli/types.py @@ -152,6 +152,20 @@ def __str__(self): ), ] +PostProcessKwargs = Annotated[ + TyperDict, + Option( + parser=parse_dict_class, + help=( + """ + Keyword arguments to pass to post-processer. Must be passed as a dictionary + wrapped in quotes, e.g. "{'key' : value}". + """ + ), + metavar="DICT", + ), +] + LogPath = Annotated[Path, Option(help="Path to save logs to.")] Summary = Annotated[ diff --git a/janus_core/helpers/janus_types.py b/janus_core/helpers/janus_types.py index 4ab971e0..3eb928d4 100644 --- a/janus_core/helpers/janus_types.py +++ b/janus_core/helpers/janus_types.py @@ -17,7 +17,8 @@ MaybeList = Union[T, list[T]] MaybeSequence = Union[T, Sequence[T]] PathLike = Union[str, Path] - +StartStopStep = tuple[Optional[int], Optional[int], int] +SliceLike = Union[slice, range, int, StartStopStep] # ASE Arg types @@ -43,13 +44,37 @@ class ASEWriteArgs(TypedDict, total=False): class ASEOptArgs(TypedDict, total=False): - """Main arugments for ase optimisers.""" + """Main arguments for ase optimisers.""" restart: Optional[bool] logfile: Optional[PathLike] trajectory: Optional[str] +class PostProcessKwargs(TypedDict, total=False): + """Main arguments for MD post-processing.""" + + # RDF + rdf_compute: bool + rdf_rmax: float + rdf_nbins: int + rdf_elements: MaybeSequence[Union[str, int]] + rdf_by_elements: bool + rdf_start: int + rdf_stop: Optional[int] + rdf_step: int + rdf_output_file: Optional[str] + # VAF + vaf_compute: bool + vaf_velocities: bool + vaf_fft: bool + vaf_atoms: Sequence[Sequence[int]] + vaf_start: int + vaf_stop: Optional[int] + vaf_step: int + vaf_output_file: Optional[PathLike] + + # eos_names from ase.eos EoSNames = Literal[ "sj", diff --git a/janus_core/helpers/post_process.py b/janus_core/helpers/post_process.py new file mode 100644 index 00000000..f0e838a5 --- /dev/null +++ b/janus_core/helpers/post_process.py @@ -0,0 +1,288 @@ +"""Module for post-processing trajectories.""" + +from collections.abc import Sequence +from itertools import combinations_with_replacement +from typing import Optional, Union + +from ase import Atoms +from ase.geometry.analysis import Analysis +import numpy as np +from numpy import float64 +from numpy.typing import NDArray + +from janus_core.helpers.janus_types import ( + MaybeSequence, + PathLike, + SliceLike, + StartStopStep, +) + + +def _process_index(index: SliceLike) -> StartStopStep: + """ + Standarize `SliceLike`s into tuple of `start`, `stop`, `step`. + + Parameters + ---------- + index : SliceLike + `SliceLike` to standardize. + + Returns + ------- + StartStopStep + Standardized `SliceLike` as `start`, `stop`, `step` triplet. + """ + + if isinstance(index, int): + if index == -1: + return (index, None, 1) + return (index, index + 1, 1) + + if isinstance(index, (slice, range)): + return (index.start, index.stop, index.step) + + return index + + +def compute_rdf( # pylint: disable=too-many-locals,too-many-branches + data: MaybeSequence[Atoms], + ana: Optional[Analysis] = None, + /, + *, + filename: Optional[MaybeSequence[PathLike]] = None, + by_elements: bool = False, + rmax: float = 2.5, + nbins: int = 50, + elements: Optional[MaybeSequence[Union[int, str]]] = None, + index: SliceLike = (0, None, 1), + volume: Optional[float] = None, +) -> Union[NDArray[float64], dict[tuple[str, str], NDArray[float64]]]: + """ + Compute the rdf of data. + + Parameters + ---------- + data : MaybeSequence[Atoms] + Dataset to compute RDF of. + ana : Optional[Analysis] + ASE Analysis object for data reuse. + filename : Optional[MaybeSequence[PathLike]] + Filename(s) to output data to. Must match number of RDFs computed. + by_elements : bool + Split RDF into pairwise by elements group. Default is False. + N.B. mixed RDFs (e.g. C-H) include all self-RDFs (e.g. C-C), + to get the pure (C-H) RDF subtract the self-RDFs. + rmax : float + Maximum distance of RDF. + nbins : int + Number of bins to divide RDF. + elements : Optional[MaybeSequence[Union[int, str]]] + Make partial RDFs. If `by_elements` is true will filter to + only display pairs in list. + index : SliceLike + Images to analyze as: + `index` if `int`, + `start`, `stop`, `step` if `tuple`, + `slice` if `slice` or `range`. + volume : Optional[float] + Volume of cell for normalisation. Only needs to be provided + if aperiodic cell. Default is (2*rmax)**3. + + Returns + ------- + Union[NDArray[float64], dict[tuple[str, str], NDArray[float64]]] + If `by_elements` is true returns a `dict` of RDF by element pairs. + Otherwise returns RDF of total system filtered by elements. + """ + index = _process_index(index) + + if not isinstance(data, Sequence): + data = [data] + + if elements is not None and not isinstance(elements, Sequence): + elements = (elements,) + + if ( # If aperiodic, assume volume of a cube encompassing rmax sphere. + not all(data[0].get_pbc()) and volume is None + ): + volume = (2 * rmax) ** 3 + + if ana is None: + ana = Analysis(data) + + if by_elements: + elements = ( + tuple(sorted(set(data[0].get_chemical_symbols()))) + if elements is None + else elements + ) + + rdf = { + element: ana.get_rdf( + rmax=rmax, + nbins=nbins, + elements=element, + imageIdx=slice(*index), + return_dists=True, + volume=volume, + ) + for element in combinations_with_replacement(elements, 2) + } + + # Compute RDF average + rdf = { + element: (rdf[0][1], np.average([rdf_i[0] for rdf_i in rdf], axis=0)) + for element, rdf in rdf.items() + } + + if filename is not None: + if not isinstance(filename, Sequence): + filename = (filename,) + + assert isinstance(filename, Sequence) + + if len(filename) != len(rdf): + raise ValueError( + f"Different number of file names ({len(filename)}) " + f"to number of samples ({len(rdf)})" + ) + + for (dists, rdfs), out_path in zip(rdf.values(), filename): + with open(out_path, "w", encoding="utf-8") as out_file: + for dist, rdf_i in zip(dists, rdfs): + print(dist, rdf_i, file=out_file) + + else: + rdf = ana.get_rdf( + rmax=rmax, + nbins=nbins, + elements=elements, + imageIdx=slice(*index), + return_dists=True, + volume=volume, + ) + + assert isinstance(rdf, list) + + # Compute RDF average + rdf = rdf[0][1], np.average([rdf_i[0] for rdf_i in rdf], axis=0) + + if filename is not None: + if isinstance(filename, Sequence): + if len(filename) != 1: + raise ValueError( + f"Different number of file names ({len(filename)}) " + "to number of samples (1)" + ) + filename = filename[0] + + with open(filename, "w", encoding="utf-8") as out_file: + for dist, rdf_i in zip(*rdf): + print(dist, rdf_i, file=out_file) + + return rdf + + +def compute_vaf( + data: Sequence[Atoms], + filenames: Optional[MaybeSequence[PathLike]] = None, + *, + use_velocities: bool = False, + fft: bool = False, + index: SliceLike = (0, None, 1), + filter_atoms: MaybeSequence[MaybeSequence[int]] = ((),), +) -> NDArray[float64]: + """ + Compute the velocity autocorrelation function (VAF) of `data`. + + Parameters + ---------- + data : Sequence[Atoms] + Dataset to compute VAF of. + filenames : Optional[MaybeSequence[PathLike]] + If present, dump resultant VAF to file. + use_velocities : bool + Compute VAF using velocities rather than momenta. + Default is False. + fft : bool + Compute the fourier transformed VAF. + Default is False. + index : SliceLike + Images to analyze as `start`, `stop`, `step`. + Default is all images. + filter_atoms : MaybeSequence[MaybeSequence[int]] + Compute the VAF averaged over subsets of the system. + Default is all atoms. + + Returns + ------- + MaybeSequence[NDArray[float64]] + Computed VAF(s). + """ + + # Ensure if passed scalars they are turned into correct dimensionality + if not isinstance(filter_atoms, Sequence): + filter_atoms = (filter_atoms,) + if not isinstance(filter_atoms[0], Sequence): + filter_atoms = (filter_atoms,) + + if filenames and not isinstance(filenames, Sequence): + filenames = (filenames,) + + if len(filenames) != len(filter_atoms): + raise ValueError( + f"Different number of file names ({len(filenames)}) " + f"to number of samples ({len(filter_atoms)})" + ) + + # Extract requested data + index = _process_index(index) + data = data[slice(*index)] + + if use_velocities: + momenta = np.asarray( + [datum.get_momenta() / datum.get_masses() for datum in data] + ) + else: + momenta = np.asarray([datum.get_momenta() for datum in data]) + + n_steps = len(momenta) + n_atoms = len(momenta[0]) + + # If filter_atoms not specified use all atoms + filter_atoms = [ + atom if atom and atom[0] else range(n_atoms) for atom in filter_atoms + ] + + used_atoms = {atom for atoms in filter_atoms for atom in atoms} + used_atoms = {j: i for i, j in enumerate(used_atoms)} + + vafs = np.sum( + np.asarray( + [ + [ + np.correlate(momenta[:, j, i], momenta[:, j, i], "same") + for i in range(3) + ] + for j in used_atoms + ] + ), + axis=1, + ) + + vafs /= n_steps - np.arange(n_steps) + + if fft: + vafs = np.fft.fft(vafs, axis=0) + + vafs = [ + np.average([vafs[used_atoms[i]] for i in atoms], axis=0) + for atoms in filter_atoms + ] + + if filenames: + for filename, vaf in zip(filenames, vafs): + with open(filename, "w", encoding="utf-8") as out_file: + print(*vaf, file=out_file, sep="\n") + + return vafs diff --git a/tests/data/lj-traj.xyz b/tests/data/lj-traj.xyz new file mode 100644 index 00000000..13af87e7 --- /dev/null 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-0.00358628 diff --git a/tests/test_md.py b/tests/test_md.py index 7059e8d7..af577377 100644 --- a/tests/test_md.py +++ b/tests/test_md.py @@ -70,7 +70,6 @@ def test_npt(): try: npt.run() - restart_atoms_1 = read(restart_path_1) assert isinstance(restart_atoms_1, Atoms) restart_atoms_2 = read(restart_path_2) diff --git a/tests/test_post_process.py b/tests/test_post_process.py new file mode 100644 index 00000000..dbdfdbc6 --- /dev/null +++ b/tests/test_post_process.py @@ -0,0 +1,194 @@ +"""Test post processing.""" + +from pathlib import Path + +from ase.io import read +import numpy as np +from typer.testing import CliRunner + +from janus_core.calculations.md import NVE +from janus_core.calculations.single_point import SinglePoint +from janus_core.cli.janus import app +from janus_core.helpers import post_process + +DATA_PATH = Path(__file__).parent / "data" +MODEL_PATH = Path(__file__).parent / "models" / "mace_mp_small.model" +runner = CliRunner() + + +def test_md_pp(tmp_path): + """Test post-processing as part of MD cycle.""" + file_prefix = tmp_path / "Cl4Na4-nve-T300.0" + traj_path = tmp_path / "Cl4Na4-nve-T300.0-traj.xyz" + stats_path = tmp_path / "Cl4Na4-nve-T300.0-stats.dat" + rdf_path = tmp_path / "Cl4Na4-nve-T300.0-rdf.dat" + vaf_path = tmp_path / "Cl4Na4-nve-T300.0-vaf.dat" + + single_point = SinglePoint( + struct_path=DATA_PATH / "NaCl.cif", + architecture="mace", + calc_kwargs={"model": MODEL_PATH}, + ) + + nve = NVE( + struct=single_point.struct, + temp=300.0, + steps=10, + traj_every=2, + stats_every=15, + file_prefix=file_prefix, + post_process_kwargs={ + "rdf_compute": True, + "rdf_rmax": 2.5, + "vaf_compute": True, + }, + ) + + try: + nve.run() + + assert rdf_path.exists() + rdf = np.loadtxt(rdf_path) + assert len(rdf) == 50 + + # Cell too small to really compute RDF + assert np.all(rdf[:, 1] == 0) + + assert vaf_path.exists() + + finally: + traj_path.unlink(missing_ok=True) + stats_path.unlink(missing_ok=True) + rdf_path.unlink(missing_ok=True) + vaf_path.unlink(missing_ok=True) + + +def test_md_pp_cli(tmp_path): + """Test all MD simulations are able to run.""" + file_prefix = tmp_path / "nve-T300" + log_path = tmp_path / "test.log" + summary_path = tmp_path / "summary.yml" + rdf_path = tmp_path / "nve-T300-rdf.dat" + vaf_path = tmp_path / "nve-T300-vaf.dat" + + result = runner.invoke( + app, + [ + "md", + "--ensemble", + "nve", + "--struct", + DATA_PATH / "NaCl.cif", + "--file-prefix", + file_prefix, + "--steps", + 10, + "--traj-every", + 2, + "--log", + log_path, + "--summary", + summary_path, + "--post-process-kwargs", + """{'vaf_compute': True, + 'rdf_compute': True, + 'rdf_rmax': 2.5}""", + ], + ) + + assert result.exit_code == 0 + + assert rdf_path.exists() + rdf = np.loadtxt(rdf_path) + assert len(rdf) == 50 + + # Cell too small to really compute RDF + assert np.all(rdf[:, 1] == 0) + + assert vaf_path.exists() + + +def test_rdf(): + """Test computation of RDF""" + data = read(DATA_PATH / "benzene.xyz") + rdf = post_process.compute_rdf(data, index=0, rmax=5.0, nbins=100) + + assert isinstance(rdf, tuple) + assert isinstance(rdf[0], np.ndarray) + + expected_peaks = np.asarray( + ( + 1.075, + 1.375, + 2.175, + 2.425, + 2.475, + 2.775, + 3.425, + 3.875, + 4.275, + 4.975, + ) + ) + peaks = np.where(rdf[1] > 0.0) + assert (np.isclose(expected_peaks, rdf[0][peaks])).all() + + +def test_rdf_by_elements(): + """Test the by_elements method of compute rdf""" + data = read(DATA_PATH / "benzene.xyz") + + rdfs = post_process.compute_rdf( + data, + index=0, + rmax=5.0, + nbins=100, + by_elements=True, + ) + + assert isinstance(rdfs, dict) + assert isinstance(rdfs[("C", "C")], tuple) + assert isinstance(rdfs[("C", "C")][0], np.ndarray) + + expected_peaks = { + ("C", "C"): (1.375, 2.425, 2.775), + ("C", "H"): ( + 1.075, + 1.375, + 2.175, + 2.425, + 2.475, + 2.775, + 3.425, + 3.875, + 4.275, + 4.975, + ), + ("H", "H"): (2.475, 4.275, 4.975), + } + + for element, rdf in rdfs.items(): + peaks = np.where(rdf[1] > 0.0) + assert (np.isclose(expected_peaks[element], rdf[0][peaks])).all() + + +def test_vaf(): + """Test vaf will run""" + data = read(DATA_PATH / "lj-traj.xyz", index=":") + vaf = post_process.compute_vaf(data) + + assert isinstance(vaf, list) + assert len(vaf) == 1 + assert isinstance(vaf[0], np.ndarray) + + vaf = post_process.compute_vaf(data, fft=True) + + assert isinstance(vaf, list) + assert len(vaf) == 1 + assert isinstance(vaf[0], np.ndarray) + + vaf = post_process.compute_vaf(data, filter_atoms=((3, 4), (1, 2, 3))) + + assert isinstance(vaf, list) + assert len(vaf) == 2 + assert isinstance(vaf[0], np.ndarray)