12828947754

Committed 17 Jan 2025 12:20PM UTC coverage: 95.13% (-0.06%) from 95.193%

Build # 12828947754

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github

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web-flow

Commit Message

Drop Python 3.9 support (#377)

* Drop Python 3.9

* Apply ruff safe fixes

* Apply ruff unsafe fixes

* Apply manual ruff fixes

* Update docs

* Drop upper Python limit

* Use strict for all zips

* Add missing future annotations

* Revert "Implement temporary fix for CI (#376)"

This reverts commit fe6e6d9f5.

* Drop Python 3.9 from Mac CI

* Apply new ruff fixes

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84.62

/janus_core/processing/post_process.py

"""Module for post-processing trajectories."""

from __future__ import annotations

from collections.abc import Sequence
from itertools import combinations_with_replacement

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,
)
from janus_core.helpers.utils import slicelike_to_startstopstep


def compute_rdf(
    data: MaybeSequence[Atoms],
    ana: Analysis | None = None,
    /,
    *,
    filenames: MaybeSequence[PathLike] | None = None,
    by_elements: bool = False,
    rmax: float = 2.5,
    nbins: int = 50,
    elements: MaybeSequence[int | str] | None = None,
    index: SliceLike = (0, None, 1),
    volume: float | None = None,
) -> NDArray[float64] | dict[tuple[str, str] | NDArray[float64]]:
    """
    Compute the rdf of data.

    Parameters
    ----------
    data : MaybeSequence[Atoms]
        Dataset to compute RDF of.
    ana : Analysis | None
        ASE Analysis object for data reuse.
    filenames : MaybeSequence[PathLike] | None
        Filenames 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 : MaybeSequence[int | str] | None
        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 : float | None
        Volume of cell for normalisation. Only needs to be provided
        if aperiodic cell. Default is (2*rmax)**3.

    Returns
    -------
    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 = slicelike_to_startstopstep(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 filenames is not None:
            if isinstance(filenames, str) or not isinstance(filenames, Sequence):
                filenames = (filenames,)

            assert isinstance(filenames, Sequence)

            if len(filenames) != len(rdf):
                raise ValueError(
                    f"Different number of file names ({len(filenames)}) "
                    f"to number of samples ({len(rdf)})"
                )

            for (dists, rdfs), out_path in zip(rdf.values(), filenames, strict=True):
                with open(out_path, "w", encoding="utf-8") as out_file:
                    for dist, rdf_i in zip(dists, rdfs, strict=True):
                        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 filenames is not None:
            if isinstance(filenames, Sequence):
                if len(filenames) != 1:
                    raise ValueError(
                        f"Different number of file names ({len(filenames)}) "
                        "to number of samples (1)"
                    )
                filenames = filenames[0]

            with open(filenames, "w", encoding="utf-8") as out_file:
                for dist, rdf_i in zip(*rdf, strict=True):
                    print(dist, rdf_i, file=out_file)

    return rdf


def compute_vaf(
    data: Sequence[Atoms],
    filenames: MaybeSequence[PathLike] | None = None,
    *,
    use_velocities: bool = False,
    fft: bool = False,
    index: SliceLike = (0, None, 1),
    filter_atoms: MaybeSequence[MaybeSequence[int | None]] = ((None),),
    time_step: float = 1.0,
) -> tuple[NDArray[float64], list[NDArray[float64]]]:
    """
    Compute the velocity autocorrelation function (VAF) of `data`.

    Parameters
    ----------
    data : Sequence[Atoms]
        Dataset to compute VAF of.
    filenames : MaybeSequence[PathLike] | None
        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 | None]]
        Compute the VAF averaged over subsets of the system.
        Default is all atoms.
    time_step : float
        Time step for scaling lags to align with input data.
        Default is 1 (i.e. no scaling).

    Returns
    -------
    lags : numpy.ndarray
        Lags at which the VAFs have been computed.
    vafs : list[numpy.ndarray]
        Computed VAF(s).

    Notes
    -----
    `filter_atoms` is given as a series of sequences of atoms, where
    each element in the series denotes a VAF subset to calculate and
    each sequence determines the atoms (by index) to be included in that VAF.

    E.g.

    .. code-block: Python

        # Species indices in cell
        na = (1, 3, 5, 7)
        cl = (2, 4, 6, 8)

        compute_vaf(..., filter_atoms=(na, cl))

    Would compute separate VAFs for each species.

    By default, one VAF will be computed for all atoms in the structure.
    """
    # 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 = slicelike_to_startstopstep(index)
    data = data[slice(*index)]

    if use_velocities:
        momenta = np.asarray([datum.get_velocities() 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[0] is not None 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], "full")[
                        n_steps - 1 :
                    ]
                    for i in range(3)
                ]
                for j in used_atoms
            ]
        ),
        axis=1,
    )

    vafs /= n_steps - np.arange(n_steps)

    lags = np.arange(n_steps) * time_step

    if fft:
        vafs = np.fft.fft(vafs, axis=0)
        lags = np.fft.fftfreq(n_steps, time_step)

    vafs = (
        lags,
        [
            np.average([vafs[used_atoms[i]] for i in atoms], axis=0)
            for atoms in filter_atoms
        ],
    )

    if filenames:
        for vaf, filename in zip(vafs[1], filenames, strict=True):
            with open(filename, "w", encoding="utf-8") as out_file:
                for lag, dat in zip(lags, vaf, strict=True):
                    print(lag, dat, file=out_file)

    return vafs

1	"""Module for post-processing trajectories."""
2
3	from __future__ import annotations	3✔
4
5	from collections.abc import Sequence	3✔
6	from itertools import combinations_with_replacement	3✔
7
8	from ase import Atoms	3✔
9	from ase.geometry.analysis import Analysis	3✔
10	import numpy as np	3✔
11	from numpy import float64	3✔
12	from numpy.typing import NDArray	3✔
13
14	from janus_core.helpers.janus_types import (	3✔
15	MaybeSequence,
16	PathLike,
17	SliceLike,
18	)
19	from janus_core.helpers.utils import slicelike_to_startstopstep	3✔
20
21
22	def compute_rdf(	3✔
23	data: MaybeSequence[Atoms],
24	ana: Analysis \| None = None,
25	/,
26	*,
27	filenames: MaybeSequence[PathLike] \| None = None,
28	by_elements: bool = False,
29	rmax: float = 2.5,
30	nbins: int = 50,
31	elements: MaybeSequence[int \| str] \| None = None,
32	index: SliceLike = (0, None, 1),
33	volume: float \| None = None,
34	) -> NDArray[float64] \| dict[tuple[str, str] \| NDArray[float64]]:
35	"""
36	Compute the rdf of data.
37
38	Parameters
39	----------
40	data : MaybeSequence[Atoms]
41	Dataset to compute RDF of.
42	ana : Analysis \| None
43	ASE Analysis object for data reuse.
44	filenames : MaybeSequence[PathLike] \| None
45	Filenames to output data to. Must match number of RDFs computed.
46	by_elements : bool
47	Split RDF into pairwise by elements group. Default is False.
48	N.B. mixed RDFs (e.g. C-H) include all self-RDFs (e.g. C-C),
49	to get the pure (C-H) RDF subtract the self-RDFs.
50	rmax : float
51	Maximum distance of RDF.
52	nbins : int
53	Number of bins to divide RDF.
54	elements : MaybeSequence[int \| str] \| None
55	Make partial RDFs. If `by_elements` is true will filter to
56	only display pairs in list.
57	index : SliceLike
58	Images to analyze as:
59	`index` if `int`,
60	`start`, `stop`, `step` if `tuple`,
61	`slice` if `slice` or `range`.
62	volume : float \| None
63	Volume of cell for normalisation. Only needs to be provided
64	if aperiodic cell. Default is (2rmax)*3.
65
66	Returns
67	-------
68	NDArray[float64] \| dict[tuple[str, str] \| NDArray[float64]]
69	If `by_elements` is true returns a `dict` of RDF by element pairs.
70	Otherwise returns RDF of total system filtered by elements.
71	"""
72	index = slicelike_to_startstopstep(index)	3✔
73
74	if not isinstance(data, Sequence):	3✔
75	data = [data]	3✔
76
77	if elements is not None and not isinstance(elements, Sequence):	3✔
78	elements = (elements,)	×
79
80	if ( # If aperiodic, assume volume of a cube encompassing rmax sphere.	3✔
81	not all(data[0].get_pbc()) and volume is None
82	):
83	volume = (2 * rmax) ** 3	3✔
84
85	if ana is None:	3✔
86	ana = Analysis(data)	3✔
87
88	if by_elements:	3✔
89	elements = (	3✔
90	tuple(sorted(set(data[0].get_chemical_symbols())))
91	if elements is None
92	else elements
93	)
94
95	rdf = {	3✔
96	element: ana.get_rdf(
97	rmax=rmax,
98	nbins=nbins,
99	elements=element,
100	imageIdx=slice(*index),
101	return_dists=True,
102	volume=volume,
103	)
104	for element in combinations_with_replacement(elements, 2)
105	}
106
107	# Compute RDF average
108	rdf = {	3✔
109	element: (rdf[0][1], np.average([rdf_i[0] for rdf_i in rdf], axis=0))
110	for element, rdf in rdf.items()
111	}
112
113	if filenames is not None:	3✔
114	if isinstance(filenames, str) or not isinstance(filenames, Sequence):	×
115	filenames = (filenames,)	×
116
117	assert isinstance(filenames, Sequence)	×
118
119	if len(filenames) != len(rdf):	×
120	raise ValueError(	×
121	f"Different number of file names ({len(filenames)}) "
122	f"to number of samples ({len(rdf)})"
123	)
124
NEW 125	for (dists, rdfs), out_path in zip(rdf.values(), filenames, strict=True):	×
126	with open(out_path, "w", encoding="utf-8") as out_file:	×
NEW 127	for dist, rdf_i in zip(dists, rdfs, strict=True):	×
128	print(dist, rdf_i, file=out_file)	×
129
130	else:
131	rdf = ana.get_rdf(	3✔
132	rmax=rmax,
133	nbins=nbins,
134	elements=elements,
135	imageIdx=slice(*index),
136	return_dists=True,
137	volume=volume,
138	)
139
140	assert isinstance(rdf, list)	3✔
141
142	# Compute RDF average
143	rdf = rdf[0][1], np.average([rdf_i[0] for rdf_i in rdf], axis=0)	3✔
144
145	if filenames is not None:	3✔
146	if isinstance(filenames, Sequence):	3✔
147	if len(filenames) != 1:	3✔
148	raise ValueError(	×
149	f"Different number of file names ({len(filenames)}) "
150	"to number of samples (1)"
151	)
152	filenames = filenames[0]	3✔
153
154	with open(filenames, "w", encoding="utf-8") as out_file:	3✔
155	for dist, rdf_i in zip(*rdf, strict=True):	3✔
156	print(dist, rdf_i, file=out_file)	3✔
157
158	return rdf	3✔
159
160
161	def compute_vaf(	3✔
162	data: Sequence[Atoms],
163	filenames: MaybeSequence[PathLike] \| None = None,
164	*,
165	use_velocities: bool = False,
166	fft: bool = False,
167	index: SliceLike = (0, None, 1),
168	filter_atoms: MaybeSequence[MaybeSequence[int \| None]] = ((None),),
169	time_step: float = 1.0,
170	) -> tuple[NDArray[float64], list[NDArray[float64]]]:
171	"""
172	Compute the velocity autocorrelation function (VAF) of `data`.
173
174	Parameters
175	----------
176	data : Sequence[Atoms]
177	Dataset to compute VAF of.
178	filenames : MaybeSequence[PathLike] \| None
179	If present, dump resultant VAF to file.
180	use_velocities : bool
181	Compute VAF using velocities rather than momenta.
182	Default is False.
183	fft : bool
184	Compute the fourier transformed VAF.
185	Default is False.
186	index : SliceLike
187	Images to analyze as `start`, `stop`, `step`.
188	Default is all images.
189	filter_atoms : MaybeSequence[MaybeSequence[int \| None]]
190	Compute the VAF averaged over subsets of the system.
191	Default is all atoms.
192	time_step : float
193	Time step for scaling lags to align with input data.
194	Default is 1 (i.e. no scaling).
195
196	Returns
197	-------
198	lags : numpy.ndarray
199	Lags at which the VAFs have been computed.
200	vafs : list[numpy.ndarray]
201	Computed VAF(s).
202
203	Notes
204	-----
205	`filter_atoms` is given as a series of sequences of atoms, where
206	each element in the series denotes a VAF subset to calculate and
207	each sequence determines the atoms (by index) to be included in that VAF.
208
209	E.g.
210
211	.. code-block: Python
212
213	# Species indices in cell
214	na = (1, 3, 5, 7)
215	cl = (2, 4, 6, 8)
216
217	compute_vaf(..., filter_atoms=(na, cl))
218
219	Would compute separate VAFs for each species.
220
221	By default, one VAF will be computed for all atoms in the structure.
222	"""
223	# Ensure if passed scalars they are turned into correct dimensionality
224	if not isinstance(filter_atoms, Sequence):	3✔
225	filter_atoms = (filter_atoms,)	3✔
226	if not isinstance(filter_atoms[0], Sequence):	3✔
227	filter_atoms = (filter_atoms,)	3✔
228
229	if filenames and not isinstance(filenames, Sequence):	3✔
230	filenames = (filenames,)	3✔
231
232	if len(filenames) != len(filter_atoms):	3✔
233	raise ValueError(	×
234	f"Different number of file names ({len(filenames)}) "
235	f"to number of samples ({len(filter_atoms)})"
236	)
237
238	# Extract requested data
239	index = slicelike_to_startstopstep(index)	3✔
240	data = data[slice(*index)]	3✔
241
242	if use_velocities:	3✔
243	momenta = np.asarray([datum.get_velocities() for datum in data])	3✔
244	else:
245	momenta = np.asarray([datum.get_momenta() for datum in data])	3✔
246
247	n_steps = len(momenta)	3✔
248	n_atoms = len(momenta[0])	3✔
249
250	# If filter_atoms not specified use all atoms
251	filter_atoms = [	3✔
252	atom if atom[0] is not None else range(n_atoms) for atom in filter_atoms
253	]
254
255	used_atoms = {atom for atoms in filter_atoms for atom in atoms}	3✔
256	used_atoms = {j: i for i, j in enumerate(used_atoms)}	3✔
257
258	vafs = np.sum(	3✔
259	np.asarray(
260	[
261	[
262	np.correlate(momenta[:, j, i], momenta[:, j, i], "full")[
263	n_steps - 1 :
264	]
265	for i in range(3)
266	]
267	for j in used_atoms
268	]
269	),
270	axis=1,
271	)
272
273	vafs /= n_steps - np.arange(n_steps)	3✔
274
275	lags = np.arange(n_steps) * time_step	3✔
276
277	if fft:	3✔
278	vafs = np.fft.fft(vafs, axis=0)	3✔
279	lags = np.fft.fftfreq(n_steps, time_step)	3✔
280
281	vafs = (	3✔
282	lags,
283	[
284	np.average([vafs[used_atoms[i]] for i in atoms], axis=0)
285	for atoms in filter_atoms
286	],
287	)
288
289	if filenames:	3✔
290	for vaf, filename in zip(vafs[1], filenames, strict=True):	3✔
291	with open(filename, "w", encoding="utf-8") as out_file:	3✔
292	for lag, dat in zip(lags, vaf, strict=True):	3✔
293	print(lag, dat, file=out_file)	3✔
294
295	return vafs	3✔

stfc / janus-core / 12828947754

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