From 4b139db471cae67d0e89f41013cdbf2b3ead3189 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sun, 5 Nov 2023 12:01:26 +0100 Subject: [PATCH 01/73] Moved package files to src --- {mdevaluate => src/mdevaluate}/__init__.py | 0 {mdevaluate => src/mdevaluate}/atoms.py | 0 {mdevaluate => src/mdevaluate}/autosave.py | 0 {mdevaluate => src/mdevaluate}/checksum.py | 0 {mdevaluate => src/mdevaluate}/cli.py | 0 {mdevaluate => src/mdevaluate}/coordinates.py | 0 {mdevaluate => src/mdevaluate}/correlation.py | 0 {mdevaluate => src/mdevaluate}/distribution.py | 0 {mdevaluate => src/mdevaluate}/functions.py | 0 {mdevaluate => src/mdevaluate}/logging.py | 0 {mdevaluate => src/mdevaluate}/pbc.py | 0 {mdevaluate => src/mdevaluate}/reader.py | 0 {mdevaluate => src/mdevaluate}/utils.py | 0 13 files changed, 0 insertions(+), 0 deletions(-) rename {mdevaluate => src/mdevaluate}/__init__.py (100%) rename {mdevaluate => src/mdevaluate}/atoms.py (100%) rename {mdevaluate => src/mdevaluate}/autosave.py (100%) rename {mdevaluate => src/mdevaluate}/checksum.py (100%) rename {mdevaluate => src/mdevaluate}/cli.py (100%) rename {mdevaluate => src/mdevaluate}/coordinates.py (100%) rename {mdevaluate => src/mdevaluate}/correlation.py (100%) rename {mdevaluate => src/mdevaluate}/distribution.py (100%) rename {mdevaluate => src/mdevaluate}/functions.py (100%) rename {mdevaluate => src/mdevaluate}/logging.py (100%) rename {mdevaluate => src/mdevaluate}/pbc.py (100%) rename {mdevaluate => src/mdevaluate}/reader.py (100%) rename {mdevaluate => src/mdevaluate}/utils.py (100%) diff --git a/mdevaluate/__init__.py b/src/mdevaluate/__init__.py similarity index 100% rename from mdevaluate/__init__.py rename to src/mdevaluate/__init__.py diff --git a/mdevaluate/atoms.py b/src/mdevaluate/atoms.py similarity index 100% rename from mdevaluate/atoms.py rename to src/mdevaluate/atoms.py diff --git a/mdevaluate/autosave.py b/src/mdevaluate/autosave.py similarity index 100% rename from mdevaluate/autosave.py rename to src/mdevaluate/autosave.py diff --git a/mdevaluate/checksum.py b/src/mdevaluate/checksum.py similarity index 100% rename from mdevaluate/checksum.py rename to src/mdevaluate/checksum.py diff --git a/mdevaluate/cli.py b/src/mdevaluate/cli.py similarity index 100% rename from mdevaluate/cli.py rename to src/mdevaluate/cli.py diff --git a/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py similarity index 100% rename from mdevaluate/coordinates.py rename to src/mdevaluate/coordinates.py diff --git a/mdevaluate/correlation.py b/src/mdevaluate/correlation.py similarity index 100% rename from mdevaluate/correlation.py rename to src/mdevaluate/correlation.py diff --git a/mdevaluate/distribution.py b/src/mdevaluate/distribution.py similarity index 100% rename from mdevaluate/distribution.py rename to src/mdevaluate/distribution.py diff --git a/mdevaluate/functions.py b/src/mdevaluate/functions.py similarity index 100% rename from mdevaluate/functions.py rename to src/mdevaluate/functions.py diff --git a/mdevaluate/logging.py b/src/mdevaluate/logging.py similarity index 100% rename from mdevaluate/logging.py rename to src/mdevaluate/logging.py diff --git a/mdevaluate/pbc.py b/src/mdevaluate/pbc.py similarity index 100% rename from mdevaluate/pbc.py rename to src/mdevaluate/pbc.py diff --git a/mdevaluate/reader.py b/src/mdevaluate/reader.py similarity index 100% rename from mdevaluate/reader.py rename to src/mdevaluate/reader.py diff --git a/mdevaluate/utils.py b/src/mdevaluate/utils.py similarity index 100% rename from mdevaluate/utils.py rename to src/mdevaluate/utils.py From 1aa5eed106cb8bdffebbb81f1fee5abbdb6f9b81 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sun, 5 Nov 2023 15:06:28 +0100 Subject: [PATCH 02/73] Moved functions from mdeval_skloth and removed deprecated code --- pyproject.toml | 2 +- src/mdevaluate/__init__.py | 2 + src/mdevaluate/chill.py | 79 +++++++++ src/mdevaluate/cli.py | 37 ---- src/mdevaluate/coordinates.py | 216 +++++++++++------------- src/mdevaluate/correlation.py | 299 ++++++++++++++++----------------- src/mdevaluate/distribution.py | 243 +++++++++------------------ src/mdevaluate/functions.py | 75 +++++++-- src/mdevaluate/system.py | 44 +++++ src/mdevaluate/utils.py | 174 +++++++++++++++---- 10 files changed, 655 insertions(+), 516 deletions(-) create mode 100644 src/mdevaluate/chill.py delete mode 100644 src/mdevaluate/cli.py create mode 100644 src/mdevaluate/system.py diff --git a/pyproject.toml b/pyproject.toml index 820c13d..7bb152c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "mdevaluate" -version = "23.7" +version = "23.11" dependencies = [ "mdanalysis", "pandas", diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index f304b92..5d422b1 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -11,6 +11,8 @@ from . import functions from . import pbc from . import autosave from . import reader +from . import chill +from . import system from .logging import logger diff --git a/src/mdevaluate/chill.py b/src/mdevaluate/chill.py new file mode 100644 index 0000000..5b49d66 --- /dev/null +++ b/src/mdevaluate/chill.py @@ -0,0 +1,79 @@ +import numpy as np +from scipy.spatial import KDTree +from scipy.special import sph_harm + + +def a_ij(atoms, N=4, l=3): + tree = KDTree(atoms) + + dist, indices = tree.query(atoms, N + 1) + indices = indices[:, 1:] + + vecs = atoms[:, np.newaxis, :] - atoms[indices] + vecs /= np.linalg.norm(vecs, axis=-1)[..., np.newaxis] + + theta = np.arctan2(vecs[..., 1], vecs[..., 0]) + np.pi + phi = np.arccos(vecs[..., 2]) + qijlm = sph_harm( + np.arange(-l, l + 1)[np.newaxis, np.newaxis, :], + l, + theta[..., np.newaxis], + phi[..., np.newaxis], + ) + qilm = np.average(qijlm, axis=1) + qil = np.sum(qilm * np.conj(qilm), axis=-1) ** 0.5 + aij = ( + np.sum(qilm[:, np.newaxis, :] * np.conj(qilm[indices]), axis=-1) + / qil[:, np.newaxis] + / qil[indices] + ) + return np.real(aij), indices + + +def number_of_neighbors(atoms): + tree = KDTree(atoms) + dist, _ = tree.query(atoms, 10, distance_upper_bound=0.35) + return np.array([len(distance[distance < 0.4]) - 1 for distance in dist]) + + +def classify_ice(aij, indices, neighbors, indexSOL): + staggerdBonds = np.sum(aij <= -0.8, axis=1) + eclipsedBonds = np.sum((aij >= -0.35) & (aij <= 0.25), axis=1) + + iceTypes = np.full(len(aij), 5) + for i in indexSOL: + if neighbors[i] != 4: + continue + elif staggerdBonds[i] == 4: + iceTypes[i] = 0 + elif staggerdBonds[i] == 3 and eclipsedBonds[i] == 1: + iceTypes[i] = 1 + elif staggerdBonds[i] == 3: + for j in indices[i]: + if staggerdBonds[j] >= 2: + iceTypes[i] = 2 + break + elif staggerdBonds[i] == 2: + for j in indices[i]: + if staggerdBonds[j] >= 3: + iceTypes[i] = 2 + break + elif eclipsedBonds[i] == 4: + iceTypes[i] = 3 + elif eclipsedBonds[i] == 3: + iceTypes[i] = 4 + iceTypes = iceTypes[indexSOL] + + return iceTypes + + +def ice_parts(iceTypes): + cubic = len(iceTypes[iceTypes == 0]) + hexagonal = len(iceTypes[iceTypes == 1]) + interface = len(iceTypes[iceTypes == 2]) + clathrate = len(iceTypes[iceTypes == 3]) + clathrate_interface = len(iceTypes[iceTypes == 4]) + liquid = len(iceTypes[iceTypes == 5]) + return np.array( + [cubic, hexagonal, interface, clathrate, clathrate_interface, liquid] + ) diff --git a/src/mdevaluate/cli.py b/src/mdevaluate/cli.py deleted file mode 100644 index 4ffaa7c..0000000 --- a/src/mdevaluate/cli.py +++ /dev/null @@ -1,37 +0,0 @@ -import argparse -from . import logging -from . import open as md_open - - -def run(*args, **kwargs): - parser = argparse.ArgumentParser() - parser.add_argument( - "xtcfile", - help="The xtc file to index.", - ) - parser.add_argument( - "--tpr", help="The tprfile of the trajectory.", dest="tpr", default=None - ) - parser.add_argument( - "--nojump", - help="Generate Nojump Matrices, requires a tpr file.", - dest="nojump", - action="store_true", - default=False, - ) - parser.add_argument( - "--debug", - help="Set logging level to debug.", - dest="debug", - action="store_true", - default=False, - ) - args = parser.parse_args() - if args.debug: - logging.setlevel("DEBUG") - - md_open("", trajectory=args.xtcfile, topology=args.tpr, nojump=args.nojump) - - -if __name__ == "__main__": - run() diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index c976e2d..0418bff 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -339,23 +339,6 @@ class Coordinates: self.atom_subset.description = desc -class MeanCoordinates(Coordinates): - def __init__(self, frames, atom_filter=None, mean=1): - super().__init__(frames, atom_filter) - self.mean = mean - assert mean >= 1, "Mean must be positive" - - def __getitem__(self, item): - frame = super().__getitem__(item) - for i in range(item + 1, item + self.mean): - frame += super().__getitem__(i) - - return frame / self.mean - - def len(self): - return len(super() - self.mean + 1) - - class CoordinatesMap: def __init__(self, coordinates, function): self.coordinates = coordinates @@ -419,74 +402,6 @@ class CoordinatesMap: return CoordinatesMap(self.coordinates.pbc, self.function) -class CoordinatesFilter: - @property - def atom_subset(self): - pass - - def __init__(self, coordinates, atom_filter): - self.coordinates = coordinates - self.atom_filter = atom_filter - - def __getitem__(self, item): - if isinstance(item, slice): - sliced = copy(self) - sliced.coordinates = self.coordinates[item] - return sliced - else: - frame = self.coordinates[item] - return frame[self.atom_filter] - - -class CoordinatesKDTree: - """ - A KDTree of coordinates frames. The KDtrees are cached by a :func:`functools.lru_cache`. - Uses :class:`scipy.spatial.cKDTree` by default, since it's significantly faster. - Make sure to use scipy 0.17 or later or switch to the normal KDTree, since cKDTree has - a memory leak in earlier versions. - """ - - def clear_cache(self): - """Clear the LRU cache.""" - self._get_tree_at_index.cache_clear() - - @property - def cache_info(self): - """Return info about the state of the cache.""" - return self._get_tree_at_index.cache_info() - - def _get_tree_at_index(self, index): - frame = self.frames[index] - return self.kdtree(frame[self.selector(frame)]) - - def __init__(self, frames, selector=None, boxsize=None, maxcache=128, ckdtree=True): - """ - Args: - frames: Trajectory of the simulation, can be Coordinates object or reader - selector: Selector function that selects a subset of each frame - maxcache: Maxsize of the :func:`~functools.lru_cache` - ckdtree: Use :class:`~scipy.spatial.cKDTree` or :class:`~scipy.spatial.KDTree` if False - """ - if selector is not None: - self.selector = selector - else: - self.selector = lambda x: slice(None) - self.frames = frames - self.kdtree = cKDTree if ckdtree else KDTree - if boxsize is not None: - self.kdtree = partial(self.kdtree, boxsize=boxsize) - self._get_tree_at_index = lru_cache(maxsize=maxcache)(self._get_tree_at_index) - - def __getitem__(self, index): - return self._get_tree_at_index(index) - - def __checksum__(self): - return checksum(self.selector, self.frames) - - def __eq__(self, other): - return super().__eq__(other) - - def map_coordinates(func): @wraps(func) def wrapped(coordinates, **kwargs): @@ -496,38 +411,35 @@ def map_coordinates(func): @map_coordinates -def centers_of_mass(c, *, masses=None): +def center_of_masses(coordinates, atoms, shear: bool = False): """ - - A- 1 - B- 2 - A- 1 - C 3 - A- - B- - A- - C - A- - B- - A- - C - - Example: rd = XTCReader('t.xtc') coordinates = Coordinates(rd) com = centers_of_mass(coordinates, (1.0, 2.0, 1.0, 3.0)) """ - # At first, regroup our array - number_of_masses = len(masses) - number_of_coordinates, number_of_dimensions = c.shape - number_of_new_coordinates = number_of_coordinates // number_of_masses - grouped_masses = c.reshape( - number_of_new_coordinates, number_of_masses, number_of_dimensions - ) - - return np.average(grouped_masses, axis=1, weights=masses) + res_ids = coordinates.residue_ids[atoms] + masses = coordinates.masses[atoms] + if shear: + coords = coordinates[atoms] + box = coords.box + sort_ind = res_ids.argsort(kind="stable") + i = np.concatenate([[0], np.where(np.diff(res_ids[sort_ind]) > 0)[0] + 1]) + coms = coords[sort_ind[i]][res_ids - min(res_ids)] + cor = md.pbc.pbc_diff(coords, coms, box) + coords = coms + cor + else: + coords = coordinates.whole[atoms] + mask = np.bincount(res_ids)[1:] != 0 + positions = np.array( + [ + np.bincount(res_ids, weights=c * masses)[1:] + / np.bincount(res_ids, weights=masses)[1:] + for c in coords.T + ] + ).T[mask] + return np.array(positions) @map_coordinates @@ -587,7 +499,83 @@ def vectors(coordinates, atoms_a, atoms_b, normed=False): coords_b = coordinates[atoms_b] if len(coords_b.shape) > 2: coords_b = coords_b.mean(axis=0) - vectors = pbc_diff(coords_a, coords_b, box=box) - norm = np.linalg.norm(vectors, axis=-1).reshape(-1, 1) if normed else 1 - vectors.reference = coords_a - return vectors / norm + vec = pbc_diff(coords_a, coords_b, box=box) + if normed: + vec / np.linalg.norm(vec, axis=-1).reshape(-1, 1) + vec.reference = coords_a + return vec + + +@map_coordinates +def dipole_vector(coordinates, atoms, normed=None): + coords = coordinates.whole[atoms] + res_ids = coordinates.residue_ids[atoms] + charges = coordinates.charges[atoms] + mask = np.bincount(res_ids)[1:] != 0 + dipoles = np.array( + [np.bincount(res_ids, weights=c * charges)[1:] for c in coords.T] + ).T[mask] + dipoles = np.array(dipoles) + if normed: + dipoles /= np.linalg.norm(dipoles, axis=-1).reshape(-1, 1) + return dipoles + + +@map_coordinates +def sum_dipole_vector( + coordinates: CoordinateFrame, + atoms: list[int], + normed: bool = True, +): + coords = coordinates.whole[atoms] + charges = coordinates.charges[atoms] + dipole = np.array([c * charges for c in coords.T]).T + if normed: + dipole /= np.linalg.norm(dipole) + return dipole + + +@map_coordinates +def normal_vectors( + coordinates: CoordinateFrame, + atoms_a: list[int], + atoms_b: list[int], + atoms_c: list[int], + normed: bool = True, +): + coords_a = coordinates[atoms_a] + coords_b = coordinates[atoms_b] + coords_c = coordinates[atoms_c] + box = coordinates.box + vectors_a = pbc_diff(coords_a, coords_b, box=box) + vectors_b = pbc_diff(coords_a, coords_c, box=box) + vec = np.cross(vectors_a, vectors_b) + if normed: + vec /= np.linalg.norm(vec, axis=-1).reshape(-1, 1) + vec.reference = coords_a + return vec + + +def displacements_without_drift( + start_frame: CoordinateFrame, end_frame: CoordinateFrame, trajectory: Coordinates +) -> np.array: + start_all = trajectory[start_frame.step] + frame_all = trajectory[end_frame.step] + displacements = ( + start_frame + - end_frame + - (np.average(start_all, axis=0) - np.average(frame_all, axis=0)) + ) + return displacements + + +@map_coordinates +def cylindrical_coordinates(frame, origin=None): + if origin is None: + origin = np.diag(frame.box) / 2 + x = frame[:, 0] - origin[0] + y = frame[:, 1] - origin[1] + z = frame[:, 2] + radius = (x**2 + y**2) ** 0.5 + phi = np.arctan2(y, x) + return np.array([radius, phi, z]).T \ No newline at end of file diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index b9fef12..32bdc00 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -1,18 +1,17 @@ +from typing import Callable + import numpy as np +from numpy.typing import ArrayLike from scipy.special import legendre from itertools import chain import dask.array as darray -from pathos.pools import ProcessPool from functools import partial +from scipy.spatial import KDTree from .autosave import autosave_data -from .utils import filon_fourier_transformation, coherent_sum, histogram -from .pbc import pbc_diff - - -def set_has_counter(func): - func.has_counter = True - return func +from .utils import coherent_sum, histogram +from .pbc import pbc_diff, pbc_points +from .coordinates import Coordinates, CoordinateFrame, displacements_without_drift def log_indices(first, last, num=100): @@ -26,85 +25,17 @@ def correlation(function, frames): return map(lambda f: function(start_frame, f), chain([start_frame], iterator)) -def subensemble_correlation(selector_function, correlation_function=correlation): - def c(function, frames): - iterator = iter(frames) - start_frame = next(iterator) - selector = selector_function(start_frame) - subensemble = map(lambda f: f[selector], chain([start_frame], iterator)) - return correlation_function(function, subensemble) - - return c - - -def multi_subensemble_correlation(selector_function): - """ - selector_function has to expect a frame and to - return either valid indices (as with subensemble_correlation) - or a multidimensional array whose entries are valid indices - - e.g. slice(10,100,2) - - e.g. [1,2,3,4,5] - - e.g. [[[0,1],[2],[3]],[[4],[5],[6]] -> shape: 2,3 with - list of indices of varying length - - e.g. [slice(1653),slice(1653,None,3)] - - e.g. [np.ones(len_of_frames, bool)] - - in general using slices is the most efficient. - if the selections are small subsets of a frame or when many subsets are empty - using indices will be more efficient than using masks. - """ - - @set_has_counter - def cmulti(function, frames): - iterator = iter(frames) - start_frame = next(iterator) - selectors = np.asarray(selector_function(start_frame)) - sel_shape = selectors.shape - if sel_shape[-1] == 0: - selectors = np.asarray(selectors, int) - if selectors.dtype != object: - sel_shape = sel_shape[:-1] - f_values = np.zeros( - sel_shape + function(start_frame, start_frame).shape, - ) - count = np.zeros(sel_shape, dtype=int) - is_first_frame_loop = True - - def cc(act_frame): - nonlocal is_first_frame_loop - for index in np.ndindex(sel_shape): - sel = selectors[index] - sf_sel = start_frame[sel] - if is_first_frame_loop: - count[index] = len(sf_sel) - f_values[index] = ( - function(sf_sel, act_frame[sel]) if count[index] != 0 else 0 - ) - is_first_frame_loop = False - return np.asarray(f_values.copy()) - - return map(cc, chain([start_frame], iterator)), count - - return cmulti - - @autosave_data(2) def shifted_correlation( - function, - frames, - selector=None, - multi_selector=None, - segments=10, - skip=0.1, - window=0.5, - average=True, - points=100, - nodes=1, + function: Callable, + frames: Coordinates, + selector: ArrayLike = None, + multi_selector: ArrayLike = None, + segments: int = 10, + skip: float = 0.1, + window: float = 0.5, + average: bool = True, + points: int = 100, ): """ Calculate the time series for a correlation function. @@ -141,9 +72,6 @@ def shifted_correlation( points (int, opt.): The number of timeshifts for which the correlation should be calculated - nodes (int, opt.): - Number of nodes used for parallelization - Returns: tuple: A list of length N that contains the timeshiftes of the frames at which @@ -186,9 +114,7 @@ def shifted_correlation( correlation = [] for index in indices: correlation.append( - get_correlation( - frames, start_frame, index, shifted_idx - ) + get_correlation(frames, start_frame, index, shifted_idx) ) return correlation @@ -202,7 +128,7 @@ def shifted_correlation( num=segments, endpoint=False, dtype=int, - ) + ) ) num_frames = int(len(frames) * window) @@ -210,33 +136,18 @@ def shifted_correlation( idx = np.unique(np.int_(ls) - 1) t = np.array([frames[i].time for i in idx]) - frames[0].time - if nodes == 1: - result = np.array( - [ - apply_selector(start_frame, frames=frames, idx=idx, - selector=selector, - multi_selector=multi_selector) - for start_frame in start_frames - ] - ) - else: - pool = ProcessPool(nodes=nodes) - # Use try finally instead of a context manager to ensure the pool is - # restarted in case of working in a jupyter-notebook, - # otherwise the kernel has to be restarted. - try: - result = np.array( - pool.map( - partial(apply_selector, - frames=frames, - idx=idx, - selector=selector, - multi_selector=multi_selector), start_frames - ) + result = np.array( + [ + apply_selector( + start_frame, + frames=frames, + idx=idx, + selector=selector, + multi_selector=multi_selector, ) - finally: - pool.terminate() - pool.restart() + for start_frame in start_frames + ] + ) if average: clean_result = [] @@ -250,24 +161,62 @@ def shifted_correlation( return t, result -def msd(start, frame): +def msd( + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + trajectory: Coordinates = None, + axis: str = "all", +) -> float: """ Mean square displacement """ - vec = start - frame - return (vec**2).sum(axis=1).mean() + if trajectory is None: + displacements = start_frame - end_frame + else: + displacements = displacements_without_drift(start_frame, end_frame, trajectory) + if axis == "all": + return (displacements**2).sum(axis=1).mean() + elif axis == "x": + return (displacements[:, 0] ** 2).mean() + elif axis == "y": + return (displacements[:, 1] ** 2).mean() + elif axis == "z": + return (displacements[:, 2] ** 2).mean() + else: + raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') -def isf(start, frame, q, box=None): +def isf( + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + q: float = 22.7, + trajectory: Coordinates = None, + axis: str = "all", +) -> float: """ Incoherent intermediate scattering function. To specify q, use water_isf = functools.partial(isf, q=22.77) # q has the value 22.77 nm^-1 :param q: length of scattering vector """ - vec = start - frame - distance = (vec**2).sum(axis=1) ** 0.5 - return np.sinc(distance * q / np.pi).mean() + if trajectory is None: + displacements = start_frame - end_frame + else: + displacements = displacements_without_drift(start_frame, end_frame, trajectory) + if axis == "all": + distance = (displacements**2).sum(axis=1) ** 0.5 + return np.sinc(distance * q / np.pi).mean() + elif axis == "x": + distance = np.abs(displacements[:, 0]) + return np.mean(np.cos(np.abs(q * distance))) + elif axis == "y": + distance = np.abs(displacements[:, 1]) + return np.mean(np.cos(np.abs(q * distance))) + elif axis == "z": + distance = np.abs(displacements[:, 2]) + return np.mean(np.cos(np.abs(q * distance))) + else: + raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') def rotational_autocorrelation(onset, frame, order=2): @@ -287,16 +236,37 @@ def rotational_autocorrelation(onset, frame, order=2): return poly(scalar_prod).mean() -def van_hove_self(start, end, bins): +def van_hove_self( + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + bins: ArrayLike, + trajectory: Coordinates = None, + axis: str = "all", +): r""" Compute the self part of the Van Hove autocorrelation function. ..math:: G(r, t) = \sum_i \delta(|\vec r_i(0) - \vec r_i(t)| - r) """ - vec = start - end - delta_r = ((vec) ** 2).sum(axis=-1) ** 0.5 - return 1 / len(start) * histogram(delta_r, bins)[0] + if trajectory is None: + vectors = start_frame - end_frame + else: + vectors = displacements_without_drift(start_frame, end_frame, trajectory) + if axis == "all": + delta_r = (vectors**2).sum(axis=1) ** 0.5 + elif axis == "x": + delta_r = np.abs(vectors[:, 0]) + elif axis == "y": + delta_r = np.abs(vectors[:, 1]) + elif axis == "z": + delta_r = np.abs(vectors[:, 2]) + else: + raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') + + hist = np.histogram(delta_r, bins)[0] + r = (bins[1:] + bins[:-1]) / 2 + return r, hist / len(start_frame) def van_hove_distinct( @@ -346,14 +316,18 @@ def van_hove_distinct( return hist / N -def overlap(onset, frame, crds_tree, radius): +def overlap( + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + radius: float = 0.1, + mode: str = "self", +): """ Compute the overlap with a reference configuration defined in a CoordinatesTree. Args: onset: Initial frame, this is only used to get the frame index frame: The current configuration - crds_tree: A CoordinatesTree of the reference configurations radius: The cutoff radius for the overlap This function is intended to be used with :func:`shifted_correlation`. @@ -369,24 +343,21 @@ def overlap(onset, frame, crds_tree, radius): ... traj ... ) """ - tree = crds_tree[onset.step] - return (tree.query(frame)[0] <= radius).sum() / tree.n - - -def susceptibility(time, correlation, **kwargs): - """ - Calculate the susceptibility of a correlation function. - - Args: - time: Timesteps of the correlation data - correlation: Value of the correlation function - **kwargs (opt.): - Additional keyword arguments will be passed to :func:`filon_fourier_transformation`. - """ - frequencies, fourier = filon_fourier_transformation( - time, correlation, imag=False, **kwargs + start_PBC, index_PBC = pbc_points( + start_frame, start_frame.box, index=True, thickness=2 * radius ) - return frequencies, frequencies * fourier + start_tree = KDTree(start_PBC) + dist, index_dist = start_tree.query(end_frame, 1, distance_upper_bound=radius) + if mode == "indifferent": + return np.sum(dist <= radius) / len(start_frame) + index_dist = index_PBC[index_dist] + index = np.arange(len(start_frame)) + index = index[dist <= radius] + index_dist = index_dist[dist <= radius] + if mode == "self": + return np.sum(index == index_dist) / len(start_frame) + elif mode == "distinct": + return np.sum(index != index_dist) / len(start_frame) def coherent_scattering_function(onset, frame, q): @@ -414,11 +385,35 @@ def coherent_scattering_function(onset, frame, q): return coherent_sum(scfunc, onset.pbc, frame.pbc) / len(onset) -def non_gaussian(onset, frame): +def non_gaussian_parameter( + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + trajectory: Coordinates = None, + axis: str = "all", +) -> float: """ Calculate the Non-Gaussian parameter : ..math: - \alpha_2 (t) = \frac{3}{5}\frac{\langle r_i^4(t)\rangle}{\langle r_i^2(t)\rangle^2} - 1 + \alpha_2 (t) = + \frac{3}{5}\frac{\langle r_i^4(t)\rangle}{\langle r_i^2(t)\rangle^2} - 1 """ - r_2 = ((frame - onset) ** 2).sum(axis=-1) - return 3 / 5 * (r_2**2).mean() / r_2.mean() ** 2 - 1 + if trajectory is None: + vectors = start_frame - end_frame + else: + vectors = displacements_without_drift(start_frame, end_frame, trajectory) + if axis == "all": + r = (vectors**2).sum(axis=1) + dimensions = 3 + elif axis == "x": + r = vectors[:, 0] ** 2 + dimensions = 1 + elif axis == "y": + r = vectors[:, 1] ** 2 + dimensions = 1 + elif axis == "z": + r = vectors[:, 2] ** 2 + dimensions = 1 + else: + raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') + + return (np.mean(r**2) / ((1 + 2 / dimensions) * (np.mean(r) ** 2))) - 1 diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index 8529684..f2d1982 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -1,12 +1,14 @@ import numpy as np +from numpy.typing import ArrayLike +from scipy import spatial +from scipy.spatial import KDTree -from .coordinates import rotate_axis, polar_coordinates, spherical_coordinates +from .coordinates import rotate_axis, polar_coordinates, Coordinates, CoordinateFrame from .atoms import next_neighbors from .autosave import autosave_data from .utils import runningmean from .pbc import pbc_diff, pbc_points from .logging import logger -from scipy import spatial @autosave_data(nargs=2, kwargs_keys=("coordinates_b",), version="time_average-1") @@ -78,15 +80,15 @@ def time_histogram(function, coordinates, bins, hist_range, pool=None): return hist_results -def rdf( - atoms_a, - atoms_b=None, - bins=None, - box=None, - kind=None, - chunksize=50000, - returnx=False, - **kwargs +def calc_gr( + traj_a: Coordinates, + traj_b: Coordinates = None, + r_max: float = None, + delta_r: float = 0.02, + segments: int = 1000, + skip: float = 0.1, + remove_intra: bool = False, + shear: bool = False, ): r""" Compute the radial pair distribution of one or two sets of atoms. @@ -111,161 +113,78 @@ def rdf( as large as possible, depending on the available memory. returnx (opt.): If True the x ordinate of the histogram is returned. """ - assert bins is not None, "Bins of the pair distribution have to be defined." - assert kind in [ - "intra", - "inter", - None, - ], "Argument kind must be one of the following: intra, inter, None." - if box is None: - box = atoms_a.box.diagonal() - if atoms_b is None: - atoms_b = atoms_a - nr_of_atoms = len(atoms_a) - indices = np.triu_indices(nr_of_atoms, k=1) - else: - nr_a, dim = atoms_a.shape - nr_b, dim = atoms_b.shape - indices = np.array([(i, j) for i in range(nr_a) for j in range(nr_b)]).T - - # compute the histogram in chunks for large systems - hist = 0 - nr_of_samples = 0 - for chunk in range(0, len(indices[0]), chunksize): - sl = slice(chunk, chunk + chunksize) - diff = pbc_diff(atoms_a[indices[0][sl]], atoms_b[indices[1][sl]], box) - dist = (diff**2).sum(axis=1) ** 0.5 - if kind == "intra": - mask = ( - atoms_a.residue_ids[indices[0][sl]] - == atoms_b.residue_ids[indices[1][sl]] - ) - dist = dist[mask] - elif kind == "inter": - mask = ( - atoms_a.residue_ids[indices[0][sl]] - != atoms_b.residue_ids[indices[1][sl]] - ) - dist = dist[mask] - - nr_of_samples += len(dist) - hist += np.histogram(dist, bins)[0] - - volume = 4 / 3 * np.pi * (bins[1:] ** 3 - bins[:-1] ** 3) - density = nr_of_samples / np.prod(box) - res = hist / volume / density - if returnx: - return np.vstack((runningmean(bins, 2), res)) - else: - return res - - -def pbc_tree_rdf( - atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs -): - if box is None: - box = atoms_a.box.diagonal() - all_coords = pbc_points(atoms_b, box, thickness=np.amax(bins) + 0.1) - to_tree = spatial.cKDTree(all_coords) - dist = to_tree.query( - pbc_diff(atoms_a, box=box), - k=len(atoms_b), - distance_upper_bound=np.amax(bins) + 0.1, - )[0].flatten() - dist = dist[dist < np.inf] - hist = np.histogram(dist, bins)[0] - volume = 4 / 3 * np.pi * (bins[1:] ** 3 - bins[:-1] ** 3) - res = (hist) * np.prod(box) / volume / len(atoms_a) / (len(atoms_b) - exclude) - if returnx: - return np.vstack((runningmean(bins, 2), res)) - else: - return res - - -def pbc_spm_rdf( - atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs -): - if box is None: - box = atoms_a.box - all_coords = pbc_points(atoms_b, box, thickness=np.amax(bins) + 0.1) - to_tree = spatial.cKDTree(all_coords) - if all_coords.nbytes / 1024**3 * len(atoms_a) < 2: - from_tree = spatial.cKDTree(pbc_diff(atoms_a, box=box)) - dist = to_tree.sparse_distance_matrix( - from_tree, max_distance=np.amax(bins) + 0.1, output_type="ndarray" - ) - dist = np.asarray(dist.tolist())[:, 2] - hist = np.histogram(dist, bins)[0] - else: - chunksize = int( - 2 * len(atoms_a) / (all_coords.nbytes / 1024**3 * len(atoms_a)) - ) - hist = 0 - for chunk in range(0, len(atoms_a), chunksize): - sl = slice(chunk, chunk + chunksize) - from_tree = spatial.cKDTree(pbc_diff(atoms_a[sl], box=box)) - dist = to_tree.sparse_distance_matrix( - from_tree, max_distance=np.amax(bins) + 0.1, output_type="ndarray" - ) - dist = np.asarray(dist.tolist())[:, 2] - hist += np.histogram(dist, bins)[0] - - volume = 4 / 3 * np.pi * (bins[1:] ** 3 - bins[:-1] ** 3) - res = (hist) * np.prod(box) / volume / len(atoms_a) / (len(atoms_b) - exclude) - if returnx: - return np.vstack((runningmean(bins, 2), res)) - else: - return res - - -@autosave_data(nargs=2, kwargs_keys=("to_coords", "times")) -def fast_averaged_rdf(from_coords, bins, to_coords=None, times=10, exclude=0, **kwargs): - if to_coords is None: - to_coords = from_coords - exclude = 1 - # first find timings for the different rdf functions - import time - - # only consider sparse matrix for this condition - if (len(from_coords[0]) * len(to_coords[0]) <= 3000 * 2000) & ( - len(from_coords[0]) / len(to_coords[0]) > 5 + def gr_frame( + atoms_a: CoordinateFrame, + atoms_b: CoordinateFrame, + bins: ArrayLike, + remove_intra: bool = False, ): - funcs = [rdf, pbc_tree_rdf, pbc_spm_rdf] - else: - funcs = [rdf, pbc_tree_rdf] - timings = [] - for f in funcs: - start = time.time() - f( - from_coords[0], - atoms_b=to_coords[0], - bins=bins, - box=np.diag(from_coords[0].box), + box = atoms_b.box + n = len(atoms_a) / np.prod(np.diag(box)) + V = 4 / 3 * np.pi * bins[-1] ** 3 + particles_in_volume = int(n * V * 1.1) + if np.all(np.diag(np.diag(box)) == box): + atoms_b = atoms_b % np.diag(box) + atoms_b_res_ids = atoms_b.residue_ids + atoms_b_tree = KDTree(atoms_b, boxsize=np.diag(box)) + else: + atoms_b_pbc, atoms_b_pbc_index = pbc_points( + atoms_b, box, thickness=bins[-1] + 0.1, index=True, shear=shear + ) + atoms_b_res_ids = atoms_b.residue_ids[atoms_b_pbc_index] + atoms_b_tree = KDTree(atoms_b_pbc) + distances, distances_index = atoms_b_tree.query( + atoms_a, particles_in_volume, distance_upper_bound=bins[-1] + 0.1 ) - end = time.time() - timings.append(end - start) - timings = np.array(timings) - timings[0] = ( - 2 * timings[0] - ) # statistics for the other functions is twice as good per frame - logger.debug("rdf function timings: " + str(timings)) - rdffunc = funcs[np.argmin(timings)] - logger.debug("rdf function used: " + str(rdffunc)) - if rdffunc == rdf: - times = times * 2 # duplicate times for same statistics + if np.array_equal(atoms_a, atoms_b): + distances = distances[:, 1:] + distances_index = distances_index[:, 1:] - frames = np.array(range(0, len(from_coords), int(len(from_coords) / times)))[:times] - out = np.zeros(len(bins) - 1) - for j, i in enumerate(frames): - logger.debug("multi_radial_pair_distribution: %d/%d", j, len(frames)) - out += rdffunc( - from_coords[i], - to_coords[i], - bins, - box=np.diag(from_coords[i].box), - exclude=exclude, + if remove_intra: + new_distances = [] + for entry in list(zip(atoms_a.residue_ids, distances, distances_index)): + mask = entry[1] < np.inf + new_distances.append( + entry[1][mask][atoms_b_res_ids[entry[2][mask]] != entry[0]] + ) + distances = np.concatenate(new_distances) + else: + distances = distances.flatten() + + hist = np.histogram(distances, bins=bins, range=(0, bins[-1]), density=False)[0] + gr = hist / len(atoms_a) + gr = gr / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) + n = len(atoms_b) / np.prod(np.diag(atoms_b.box)) + gr = gr / n + + return gr, n + + if traj_b is None: + traj_b = traj_a + + start_frame = traj_a[int(len(traj_a) * skip)] + if r_max: + upper_bound = r_max + else: + upper_bound = round(np.min(np.diag(start_frame.box)) / 2 - 0.05, 1) + + num_steps = int(upper_bound * (1 / delta_r) + 1) + bins = np.linspace(0, upper_bound, num_steps) + r = bins[1:] - (bins[1] - bins[0]) / 2 + frame_indices = np.unique( + np.int_(np.linspace(len(traj_a) * skip, len(traj_a) - 1, num=segments)) + ) + gr = [] + n = [] + for frame_index in frame_indices: + result = gr_frame( + traj_a[frame_index], traj_b[frame_index], bins, remove_intra=remove_intra ) - return out / len(frames) + gr.append(result[0]) + n.append(result[1]) + gr = np.mean(gr, axis=0) + n = np.mean(n, axis=0) + return r, gr, n def distance_distribution(atoms, bins): diff --git a/src/mdevaluate/functions.py b/src/mdevaluate/functions.py index 159a1c6..7348d0c 100644 --- a/src/mdevaluate/functions.py +++ b/src/mdevaluate/functions.py @@ -1,36 +1,42 @@ import numpy as np +from scipy.special import gamma as spgamma +from scipy.integrate import quad as spquad -def kww(t, A, τ, β): - return A * np.exp(-((t / τ) ** β)) +def kww(t, A, tau, beta): + return A * np.exp(-((t / tau) ** beta)) -def kww_1e(A, τ, β): - return τ * (-np.log(1 / (np.e * A))) ** (1 / β) +def kww_1e(A, tau, beta): + return tau * (-np.log(1 / (np.e * A))) ** (1 / beta) -def cole_davidson(w, A, b, t0): - P = np.arctan(w * t0) - return A * np.cos(P) ** b * np.sin(b * P) +def cole_davidson(omega, A, beta, tau): + P = np.arctan(omega * tau) + return A * np.cos(P) ** beta * np.sin(beta * P) -def cole_cole(w, A, b, t0): +def cole_cole(omega, A, beta, tau): return ( A - * (w * t0) ** b - * np.sin(np.pi * b / 2) - / (1 + 2 * (w * t0) ** b * np.cos(np.pi * b / 2) + (w * t0) ** (2 * b)) + * (omega * tau) ** beta + * np.sin(np.pi * beta / 2) + / ( + 1 + + 2 * (omega * tau) ** beta * np.cos(np.pi * beta / 2) + + (omega * tau) ** (2 * beta) + ) ) -def havriliak_negami(ω, A, β, α, τ): +def havriliak_negami(omega, A, beta, alpha, tau): r""" Imaginary part of the Havriliak-Negami function. .. math:: \chi_{HN}(\omega) = \Im\left(\frac{A}{(1 + (i\omega\tau)^\alpha)^\beta}\right) """ - return -(A / (1 + (1j * ω * τ) ** α) ** β).imag + return -(A / (1 + (1j * omega * tau) ** alpha) ** beta).imag # fits decay of correlation times, e.g. with distance to pore walls @@ -38,6 +44,47 @@ def colen(d, X, t8, A): return t8 * np.exp(A * np.exp(-d / X)) -# fits decay of the plateau height of the overlap function, e.g. with distance to pore walls +# fits decay of the plateau height of the overlap function, +# e.g. with distance to pore walls def colenQ(d, X, Qb, g): return (1 - Qb) * np.exp(-((d / X) ** g)) + Qb + + +def vft(T, tau_0, B, T_inf): + return tau_0 * np.exp(B / (T - T_inf)) + + +def arrhenius(T, tau_0, E_a): + return tau_0 * np.exp(E_a / T) + + +def MLfit(t, tau, A, alpha): + def MLf(z, a): + """Mittag-Leffler function""" + z = np.atleast_1d(z) + if a == 0: + return 1 / (1 - z) + elif a == 1: + return np.exp(z) + elif a > 1 or all(z > 0): + k = np.arange(100) + return np.polynomial.polynomial.polyval(z, 1 / spgamma(a * k + 1)) + + # a helper for tricky case, from Gorenflo, Loutchko & Luchko + def _MLf(z, a): + if z < 0: + f = lambda x: ( + np.exp(-x * (-z) ** (1 / a)) + * x ** (a - 1) + * np.sin(np.pi * a) + / (x ** (2 * a) + 2 * x**a * np.cos(np.pi * a) + 1) + ) + return 1 / np.pi * spquad(f, 0, np.inf)[0] + elif z == 0: + return 1 + else: + return MLf(z, a) + + return np.vectorize(_MLf)(z, a) + + return A * MLf(-((t / tau) ** alpha), alpha) diff --git a/src/mdevaluate/system.py b/src/mdevaluate/system.py new file mode 100644 index 0000000..86e1af5 --- /dev/null +++ b/src/mdevaluate/system.py @@ -0,0 +1,44 @@ +import abc +from dataclasses import dataclass, field +from subprocess import run + +import pandas as pd + + +@dataclass(kw_only=True) +class MDSystem(abc.ABC): + load_only_results: bool = False + system_dir: str = field(init=False) + + @abc.abstractmethod + def _add_description(self, data: pd.DataFrame) -> pd.DataFrame: + pass + + def save_results(self, data: pd.DataFrame, key: str) -> None: + data = self._add_description(data) + hdf5_file = f"{self.system_dir}/out/results.h5" + data.to_hdf(hdf5_file, key=key, complevel=9, complib="blosc") + + def load_results(self, key: str) -> pd.DataFrame: + hdf5_file = f"{self.system_dir}/out/results.h5" + data = pd.read_hdf(hdf5_file, key=key) + if isinstance(data, pd.DataFrame): + return data + else: + raise TypeError("Result is not a DataFrame!") + + def cleanup_results(self) -> None: + hdf5_file = f"{self.system_dir}/out/results.h5" + hdf5_temp_file = f"{self.system_dir}/out/results_temp.h5" + run( + [ + "ptrepack", + "--chunkshape=auto", + "--propindexes", + "--complevel=9", + "--complib=blosc", + hdf5_file, + hdf5_temp_file, + ] + ) + run(["mv", hdf5_temp_file, hdf5_file]) diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index d93fbf2..0ad26c3 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -25,7 +25,8 @@ def five_point_stencil(xdata, ydata): ydata: y values of the data points Returns: - Values where the derivative was estimated and the value of the derivative at these points. + Values where the derivative was estimated and the value of the derivative at + these points. This algorithm is only valid for values on a regular grid, for unevenly distributed data it is only an approximation, albeit a quite good one. @@ -33,17 +34,17 @@ def five_point_stencil(xdata, ydata): See: https://en.wikipedia.org/wiki/Five-point_stencil """ return xdata[2:-2], ( - (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4]) - / (3 * (xdata[4:] - xdata[:-4])) + (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4]) + / (3 * (xdata[4:] - xdata[:-4])) ) def filon_fourier_transformation( - time, - correlation, - frequencies=None, - derivative="linear", - imag=True, + time, + correlation, + frequencies=None, + derivative="linear", + imag=True, ): """ Fourier-transformation for slow varrying functions. The filon algorithmus is @@ -63,8 +64,8 @@ def filon_fourier_transformation( If frequencies are not explicitly given they will be evenly placed on a log scale in the interval [1/tmax, 0.1/tmin] where tmin and tmax are the smallest respectively the biggest time (greater than 0) of the provided times. The frequencies are cut off - at high values by one decade, since the fourier transformation deviates quite strongly - in this regime. + at high values by one decade, since the fourier transformation deviates quite + strongly in this regime. .. [Blochowicz] T. Blochowicz, Broadband dielectric spectroscopy in neat and binary @@ -86,25 +87,26 @@ def filon_fourier_transformation( derivative.reshape(-1, 1) else: raise NotImplementedError( - 'Invalid approximation method {}. Possible values are "linear", "stencil" or a list of values.' + 'Invalid approximation method {}. Possible values are "linear", "stencil" ' + 'or a list of values.' ) time = time.reshape(-1, 1) integral = ( - np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1]) - ) / frequencies**2 + np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1]) + ) / frequencies ** 2 fourier = (derivative * integral).sum(axis=0) if imag: integral = ( - 1j - * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1])) - / frequencies**2 + 1j + * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1])) + / frequencies ** 2 ) fourier = ( - fourier - + (derivative * integral).sum(axis=0) - + 1j * correlation[0] / frequencies + fourier + + (derivative * integral).sum(axis=0) + + 1j * correlation[0] / frequencies ) return ( @@ -219,12 +221,11 @@ def coherent_sum(func, coord_a, coord_b): coherent_sum(f, x, y) == f(x.reshape(N, 1, d), x.reshape(1, M, d)).sum() Args: - func: The function is called for each two items in both arrays, this should return a scalar value. + func: The function is called for each two items in both arrays, this should + return a scalar value. coord_a, coord_b: The two arrays. """ - if isinstance(func, FunctionType): - func = numba.jit(func, nopython=True, cache=True) def cohsum(coord_a, coord_b): res = 0 @@ -244,17 +245,18 @@ def coherent_histogram(func, coord_a, coord_b, bins, distinct=False): N, d = x.shape M, d = y.shape bins = np.arange(1, 5, 0.1) - coherent_histogram(f, x, y, bins) == histogram(f(x.reshape(N, 1, d), x.reshape(1, M, d)), bins=bins) + coherent_histogram(f, x, y, bins) == histogram( + f(x.reshape(N, 1, d), x.reshape(1, M, d)), bins=bins + ) Args: - func: The function is called for each two items in both arrays, this should return a scalar value. + func: The function is called for each two items in both arrays, this should + return a scalar value. coord_a, coord_b: The two arrays. - bins: The bins used for the histogram must be distributed regular on a linear scale. + bins: The bins used for the histogram must be distributed regular on a linear + scale. """ - if isinstance(func, FunctionType): - func = numba.jit(func, nopython=True, cache=True) - assert np.isclose( np.diff(bins).mean(), np.diff(bins) ).all(), "A regular distribution of bins is required." @@ -278,7 +280,8 @@ def coherent_histogram(func, coord_a, coord_b, bins, distinct=False): def Sq_from_gr(r, gr, q, ρ): r""" - Compute the static structure factor as fourier transform of the pair correlation function. [Yarnell]_ + Compute the static structure factor as fourier transform of the pair correlation + function. [Yarnell]_ .. math:: S(q) - 1 = \\frac{4\\pi \\rho}{q}\\int\\limits_0^\\infty (g(r) - 1)\\,r \\sin(qr) dr @@ -290,7 +293,8 @@ def Sq_from_gr(r, gr, q, ρ): ρ: Average number density .. [Yarnell] - Yarnell, J. L., Katz, M. J., Wenzel, R. G., & Koenig, S. H. (1973). Physical Review A, 7(6), 2130–2144. + Yarnell, J. L., Katz, M. J., Wenzel, R. G., & Koenig, S. H. (1973). Physical + Review A, 7(6), 2130–2144. http://doi.org/10.1017/CBO9781107415324.004 """ @@ -300,7 +304,8 @@ def Sq_from_gr(r, gr, q, ρ): def Fqt_from_Grt(data, q): """ - Calculate the ISF from the van Hove function for a given q value by fourier transform. + Calculate the ISF from the van Hove function for a given q value by fourier + transform. .. math:: F_q(t) = \\int\\limits_0^\\infty dr \\; G(r, t) \\frac{\\sin(qr)}{qr} @@ -312,8 +317,9 @@ def Fqt_from_Grt(data, q): q: Value of q. Returns: - If input data was a dataframe the result will be returned as one too, else two arrays - will be returned, which will contain times and values of Fq(t) respectively. + If input data was a dataframe the result will be returned as one too, else two + arrays will be returned, which will contain times and values of Fq(t) + respectively. """ if isinstance(data, pd.DataFrame): @@ -329,7 +335,10 @@ def Fqt_from_Grt(data, q): def singledispatchmethod(func): - """A decorator to define a genric instance method, analogue to functools.singledispatch.""" + """ + A decorator to define a genric instance method, analogue to + functools.singledispatch. + """ dispatcher = functools.singledispatch(func) def wrapper(*args, **kw): @@ -341,7 +350,9 @@ def singledispatchmethod(func): def histogram(data, bins): - """Compute the histogram of the given data. Uses numpy.bincount function, if possible.""" + """ + Compute the histogram of the given data. Uses numpy.bincount function, if possible. + """ dbins = np.diff(bins) dx = dbins.mean() if bins.min() == 0 and dbins.std() < 1e-6: @@ -358,7 +369,8 @@ def quick1etau(t, C, n=7): Estimate the time for a correlation function that goes from 1 to 0 to decay to 1/e. If successful, returns tau as fine interpolation with a kww fit. - The data is reduce to points around 1/e to remove short and long times from the kww fit! + The data is reduce to points around 1/e to remove short and long times from the kww + fit! t is the time C is C(t) the correlation function n is the minimum number of points around 1/e required @@ -384,3 +396,93 @@ def quick1etau(t, C, n=7): except: pass return tau_est + + +def susceptibility(time, correlation, **kwargs): + """ + Calculate the susceptibility of a correlation function. + + Args: + time: Timesteps of the correlation data + correlation: Value of the correlation function + **kwargs (opt.): + Additional keyword arguments will be passed to :func:`filon_fourier_transformation`. + """ + frequencies, fourier = filon_fourier_transformation( + time, correlation, imag=False, **kwargs + ) + return frequencies, frequencies * fourier + + +def read_gro(file): + with open(file, "r") as f: + lines = f.readlines() + description = lines[0].splitlines()[0] + boxsize = lines[-1] + box = boxsize.split() + + if len(box) == 3: + box = np.array([[box[0], 0, 0], [0, box[1], 0], [0, 0, box[2]]], dtype=float) + else: + box = np.array( + [ + [box[0], box[3], box[4]], + [box[5], box[1], box[6]], + [box[7], box[8], box[2]], + ], + dtype=float, + ) + + atomdata = np.genfromtxt( + file, + delimiter=[5, 5, 5, 5, 8, 8, 8], + dtype="i8,U5,U5,i8,f8,f8,f8", + skip_header=2, + skip_footer=1, + unpack=True, + ) + atoms_DF = pd.DataFrame( + { + "residue_id": atomdata[0], + "residue_name": atomdata[1], + "atom_name": atomdata[2], + "atom_id": atomdata[3], + "pos_x": atomdata[4], + "pos_y": atomdata[5], + "pos_z": atomdata[6], + } + ) + return atoms_DF, box, description + + +def write_gro(file, atoms_DF, box, description): + with open(file, "w") as f: + f.write(f"{description} \n") + f.write(f"{len(atoms_DF)}\n") + for i, atom in atoms_DF.iterrows(): + f.write( + f"{atom['residue_id']:>5}{atom['residue_name']:<5}" + f"{atom['atom_name']:>5}{atom['atom_id']:>5}" + f"{atom['pos_x']:8.3f}{atom['pos_y']:8.3f}" + f"{atom['pos_z']:8.3f}\n" + ) + f.write( + f"{box[0,0]:10.5f}{box[1,1]:10.5f}{box[2,2]:10.5f}" + f"{box[0,1]:10.5f}{box[0,2]:10.5f}{box[1,0]:10.5f}" + f"{box[1,2]:10.5f}{box[2,0]:10.5f}{box[2,1]:10.5f}\n" + ) + + +def fibonacci_sphere(samples=1000): + points = [] + phi = np.pi * (np.sqrt(5.0) - 1.0) # golden angle in radians + + for i in range(samples): + y = 1 - (i / float(samples - 1)) * 2 # y goes from 1 to -1 + radius = np.sqrt(1 - y * y) # radius at y + theta = phi * i # golden angle increment + x = np.cos(theta) * radius + z = np.sin(theta) * radius + points.append((x, y, z)) + + return np.array(points) From cf288824188a8d3c735095162da4d8e29845c677 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sun, 5 Nov 2023 19:39:02 +0100 Subject: [PATCH 03/73] Removed CachedReader --- src/mdevaluate/reader.py | 54 +++------------------------------------- 1 file changed, 4 insertions(+), 50 deletions(-) diff --git a/src/mdevaluate/reader.py b/src/mdevaluate/reader.py index c97ec3d..7152fc1 100755 --- a/src/mdevaluate/reader.py +++ b/src/mdevaluate/reader.py @@ -32,18 +32,11 @@ class WrongTopologyError(Exception): def open_with_mdanalysis( - topology, trajectory, cached=False, index_file=None, charges=None, masses=None + topology, trajectory, index_file=None, charges=None, masses=None ): """Open the topology and trajectory with mdanalysis.""" uni = mdanalysis.Universe(topology, trajectory, convert_units=False) - if cached is not False: - if cached is True: - maxsize = 128 - else: - maxsize = cached - reader = CachedReader(uni.trajectory, maxsize) - else: - reader = BaseReader(uni.trajectory) + reader = BaseReader(uni.trajectory) reader.universe = uni if topology.endswith(".tpr"): charges = uni.atoms.charges @@ -274,11 +267,6 @@ class BaseReader: self._nojump_matrixes = mats def __init__(self, rd): - """ - Args: - filename: Trajectory file to open. - reindex (bool, opt.): If True, regenerate the index file if necessary. - """ self.rd = rd self._nojump_matrixes = None if path.exists(nojump_load_filename(self)): @@ -291,39 +279,5 @@ class BaseReader: return len(self.rd) def __checksum__(self): - if hasattr(self.rd, "cache"): - # Has an pygmx reader - return checksum(self.filename, str(self.rd.cache)) - elif hasattr(self.rd, "_xdr"): - # Has an mdanalysis reader - cache = array("L", self.rd._xdr.offsets.tobytes()) - return checksum(self.filename, str(cache)) - - -class CachedReader(BaseReader): - """A reader that has a least-recently-used cache for frames.""" - - @property - def cache_info(self): - """Get Information about the lru cache.""" - return self._get_item.cache_info() - - def clear_cache(self): - """Clear the cache of the frames.""" - self._get_item.cache_clear() - - def __init__(self, rd, maxsize): - """ - Args: - filename (str): Trajectory file that will be opened. - maxsize: Maximum size of the lru_cache or None for infinite cache. - """ - super().__init__(rd) - self._get_item = lru_cache(maxsize=maxsize)(self._get_item) - - def _get_item(self, item): - """Buffer function for lru_cache, since __getitem__ can not be cached.""" - return super().__getitem__(item) - - def __getitem__(self, item): - return self._get_item(item) + cache = array("L", self.rd._xdr.offsets.tobytes()) + return checksum(self.filename, str(cache)) From 9c253a2f5870ba141f903cca255b4390d36626dd Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 6 Nov 2023 11:48:57 +0100 Subject: [PATCH 04/73] Added test trajectory offset files to .gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index ed3ff7b..93318c3 100644 --- a/.gitignore +++ b/.gitignore @@ -14,3 +14,5 @@ doc/modules tmp/ *.xtcindex .spyproject/ +/test/data/water/.traj.xtc_offsets.lock +/test/data/water/.traj.xtc_offsets.npz From 118ed8e6af321d65c5c7131cd3886c300cac9882 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 6 Nov 2023 15:53:56 +0100 Subject: [PATCH 05/73] Added missing functions from mdeval_skloth --- src/mdevaluate/coordinates.py | 17 +++++++++++++++-- src/mdevaluate/utils.py | 14 ++++++++++++++ 2 files changed, 29 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 0418bff..3cd737f 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -82,6 +82,17 @@ def radial_selector(frame, coordinates, rmin, rmax): return mask2indices(selector) +def selector_radial_cylindrical(atoms, r_min, r_max, origin=None): + box = atoms.box + atoms = atoms % np.diag(box) + if origin is None: + origin = [box[0, 0] / 2, box[1, 1] / 2, box[2, 2] / 2] + r_vec = (atoms - origin)[:, :2] + r = np.linalg.norm(r_vec, axis=1) + index = np.argwhere((r >= r_min) * (r < r_max)) + return index.flatten() + + def spatial_selector(frame, transform, rmin, rmax): """ Select a subset of atoms which have a radius between rmin and rmax. @@ -411,7 +422,7 @@ def map_coordinates(func): @map_coordinates -def center_of_masses(coordinates, atoms, shear: bool = False): +def center_of_masses(coordinates, atoms=None, shear: bool = False): """ Example: rd = XTCReader('t.xtc') @@ -419,6 +430,8 @@ def center_of_masses(coordinates, atoms, shear: bool = False): com = centers_of_mass(coordinates, (1.0, 2.0, 1.0, 3.0)) """ + if atoms is None: + atoms = list(range(len(coordinates))) res_ids = coordinates.residue_ids[atoms] masses = coordinates.masses[atoms] if shear: @@ -427,7 +440,7 @@ def center_of_masses(coordinates, atoms, shear: bool = False): sort_ind = res_ids.argsort(kind="stable") i = np.concatenate([[0], np.where(np.diff(res_ids[sort_ind]) > 0)[0] + 1]) coms = coords[sort_ind[i]][res_ids - min(res_ids)] - cor = md.pbc.pbc_diff(coords, coms, box) + cor = pbc_diff(coords, coms, box) coords = coms + cor else: coords = coordinates.whole[atoms] diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 0ad26c3..42ad521 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -2,6 +2,7 @@ Collection of utility functions. """ import functools +from time import time from types import FunctionType import numpy as np @@ -486,3 +487,16 @@ def fibonacci_sphere(samples=1000): points.append((x, y, z)) return np.array(points) + + +def timing(function): + @functools.wraps(function) + def wrap(*args, **kw): + start_time = time() + result = function(*args, **kw) + end_time = time() + time_needed = end_time - start_time + print(f"Finished in {int(time_needed // 60)} min " f"{int(time_needed % 60)} s") + return result + + return wrap \ No newline at end of file From 6a7027bb34420bf8f07b039679dbf6621da350d6 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 6 Nov 2023 16:34:43 +0100 Subject: [PATCH 06/73] Added missing functions from mdeval_skloth --- src/mdevaluate/distribution.py | 17 +++++++++++++++++ src/mdevaluate/utils.py | 2 +- 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index f2d1982..d3c113e 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -2,6 +2,7 @@ import numpy as np from numpy.typing import ArrayLike from scipy import spatial from scipy.spatial import KDTree +from scipy.sparse.csgraph import connected_components from .coordinates import rotate_axis, polar_coordinates, Coordinates, CoordinateFrame from .atoms import next_neighbors @@ -432,3 +433,19 @@ def hbonds( ) else: return pairs[is_bond] + +def calc_cluster_sizes(frame, r_max=0.35): + frame_PBC, indices_PBC = pbc_points( + frame, frame.box, thickness=r_max + 0.1, index=True + ) + tree = KDTree(frame_PBC) + matrix = tree.sparse_distance_matrix(tree, r_max, output_type="ndarray") + new_matrix = np.zeros((len(frame), len(frame))) + for entry in matrix: + if entry[2] > 0: + new_matrix[indices_PBC[entry[0]], indices_PBC[entry[1]]] = 1 + n_components, labels = connected_components(new_matrix, directed=False) + cluster_sizes = [] + for i in range(0, np.max(labels) + 1): + cluster_sizes.append(np.sum(labels == i)) + return np.array(cluster_sizes).flatten() diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 42ad521..e2d3d29 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -498,5 +498,5 @@ def timing(function): time_needed = end_time - start_time print(f"Finished in {int(time_needed // 60)} min " f"{int(time_needed % 60)} s") return result + return wrap - return wrap \ No newline at end of file From 1a8134c002f51fbbd23c8f11eb8c253bcde0da28 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 7 Nov 2023 10:25:29 +0100 Subject: [PATCH 07/73] Added function to load data for multiply MDSystem --- src/mdevaluate/system.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/src/mdevaluate/system.py b/src/mdevaluate/system.py index 86e1af5..15de993 100644 --- a/src/mdevaluate/system.py +++ b/src/mdevaluate/system.py @@ -42,3 +42,15 @@ class MDSystem(abc.ABC): ] ) run(["mv", hdf5_temp_file, hdf5_file]) + + +def load_and_concat_data(systems: list[MDSystem], key: str, verbose: bool = False): + data = [] + for system in systems: + try: + data.append(system.load_results(key=key)) + if verbose: + print(f"Load {system}") + except (FileNotFoundError, KeyError): + continue + return pd.concat(data, ignore_index=True) From a75091aa95c4670b251e68e820e3eafee16353f9 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 7 Nov 2023 11:28:12 +0100 Subject: [PATCH 08/73] Adjusted type hint for load_and_concat_data --- src/mdevaluate/system.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/mdevaluate/system.py b/src/mdevaluate/system.py index 15de993..0541c35 100644 --- a/src/mdevaluate/system.py +++ b/src/mdevaluate/system.py @@ -1,6 +1,7 @@ import abc from dataclasses import dataclass, field from subprocess import run +from typing import Iterable import pandas as pd @@ -44,7 +45,7 @@ class MDSystem(abc.ABC): run(["mv", hdf5_temp_file, hdf5_file]) -def load_and_concat_data(systems: list[MDSystem], key: str, verbose: bool = False): +def load_and_concat_data(systems: Iterable[MDSystem], key: str, verbose: bool = False): data = [] for system in systems: try: From 2904d1e4d88be13d010191203f30c9a3f3bfd10d Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 10 Nov 2023 15:19:44 +0100 Subject: [PATCH 09/73] Removed cached from open --- src/mdevaluate/__init__.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index 5d422b1..f353f67 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -20,7 +20,6 @@ def open( directory="", topology="*.tpr", trajectory="*.xtc", - cached=False, nojump=False, index_file=None, charges=None, @@ -32,13 +31,9 @@ def open( Args: directory: Directory of the simulation. topology (opt.): - Descriptor of the topology file (tpr or gro). By default a tpr file is + Descriptor of the topology file (tpr or gro). By default, a tpr file is used, if there is exactly one in the directoy. trajectory (opt.): Descriptor of the trajectory (xtc file). - cached (opt.): - If the trajectory reader should be cached. Can be True, an integer or None. - If this is True maxsize is 128, otherwise this is used as maxsize for - the cache, None means infinite cache (this is a potential memory leak!). nojump (opt.): If nojump matrixes should be generated. They will alwyas be loaded if present Returns: @@ -82,7 +77,6 @@ def open( atom_set, frames = reader.open_with_mdanalysis( top_file, traj_file, - cached=cached, index_file=index_file, charges=charges, masses=masses, @@ -97,7 +91,7 @@ def open( def open_energy(file): - """Reads an gromacs energy file and output the data in a pandas DataFrame. + """Reads a gromacs energy file and output the data in a pandas DataFrame. Args: file: Filename of the energy file """ From a4c365c4a19bce0c0d9c893621dcca79176c417d Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sun, 12 Nov 2023 17:37:15 +0100 Subject: [PATCH 10/73] Fixed bug in vectors() --- src/mdevaluate/coordinates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 3cd737f..5fc576a 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -514,7 +514,7 @@ def vectors(coordinates, atoms_a, atoms_b, normed=False): coords_b = coords_b.mean(axis=0) vec = pbc_diff(coords_a, coords_b, box=box) if normed: - vec / np.linalg.norm(vec, axis=-1).reshape(-1, 1) + vec /= np.linalg.norm(vec, axis=-1).reshape(-1, 1) vec.reference = coords_a return vec From 57a492cb48adb582615036453327bd1df6b4a964 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 13 Nov 2023 16:26:06 +0100 Subject: [PATCH 11/73] Fixed normal_vectors() and removed old pbc_diff --- src/mdevaluate/coordinates.py | 1 - src/mdevaluate/pbc.py | 14 -------------- 2 files changed, 15 deletions(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 5fc576a..9bf5f88 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -565,7 +565,6 @@ def normal_vectors( vec = np.cross(vectors_a, vectors_b) if normed: vec /= np.linalg.norm(vec, axis=-1).reshape(-1, 1) - vec.reference = coords_a return vec diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index 66c903b..f57a60d 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -8,20 +8,6 @@ from itertools import product from .logging import logger -def pbc_diff_old(v1, v2, box): - """ - Calculate the difference of two vestors, considering optional boundary conditions. - """ - if box is None: - v = v1 - v2 - else: - v = v1 % box - v2 % box - v -= (v > box / 2) * box - v += (v < -box / 2) * box - - return v - - def pbc_diff(v1, v2=None, box=None): if box is None: out = v1 - v2 From ab3d9497d996625c5e1f1a0623b406d793ce06c3 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sun, 19 Nov 2023 12:18:52 +0100 Subject: [PATCH 12/73] Added NoSuchNodeError to except in load_and_concat_data --- src/mdevaluate/system.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/system.py b/src/mdevaluate/system.py index 0541c35..17936ad 100644 --- a/src/mdevaluate/system.py +++ b/src/mdevaluate/system.py @@ -4,7 +4,7 @@ from subprocess import run from typing import Iterable import pandas as pd - +from tables import NoSuchNodeError @dataclass(kw_only=True) class MDSystem(abc.ABC): @@ -52,6 +52,6 @@ def load_and_concat_data(systems: Iterable[MDSystem], key: str, verbose: bool = data.append(system.load_results(key=key)) if verbose: print(f"Load {system}") - except (FileNotFoundError, KeyError): + except (FileNotFoundError, KeyError, NoSuchNodeError): continue return pd.concat(data, ignore_index=True) From 9b45a1a7bd5df0514543dbbec412f6666cae4270 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 5 Dec 2023 16:44:26 +0100 Subject: [PATCH 13/73] Applied black formatter --- src/mdevaluate/chill.py | 6 +++--- src/mdevaluate/coordinates.py | 2 +- src/mdevaluate/distribution.py | 10 ++++++---- src/mdevaluate/system.py | 1 + src/mdevaluate/utils.py | 34 +++++++++++++++++----------------- 5 files changed, 28 insertions(+), 25 deletions(-) diff --git a/src/mdevaluate/chill.py b/src/mdevaluate/chill.py index 5b49d66..dfb0bc5 100644 --- a/src/mdevaluate/chill.py +++ b/src/mdevaluate/chill.py @@ -23,9 +23,9 @@ def a_ij(atoms, N=4, l=3): qilm = np.average(qijlm, axis=1) qil = np.sum(qilm * np.conj(qilm), axis=-1) ** 0.5 aij = ( - np.sum(qilm[:, np.newaxis, :] * np.conj(qilm[indices]), axis=-1) - / qil[:, np.newaxis] - / qil[indices] + np.sum(qilm[:, np.newaxis, :] * np.conj(qilm[indices]), axis=-1) + / qil[:, np.newaxis] + / qil[indices] ) return np.real(aij), indices diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 9bf5f88..c8888a0 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -590,4 +590,4 @@ def cylindrical_coordinates(frame, origin=None): z = frame[:, 2] radius = (x**2 + y**2) ** 0.5 phi = np.arctan2(y, x) - return np.array([radius, phi, z]).T \ No newline at end of file + return np.array([radius, phi, z]).T diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index d3c113e..e57eb37 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -114,11 +114,12 @@ def calc_gr( as large as possible, depending on the available memory. returnx (opt.): If True the x ordinate of the histogram is returned. """ + def gr_frame( - atoms_a: CoordinateFrame, - atoms_b: CoordinateFrame, - bins: ArrayLike, - remove_intra: bool = False, + atoms_a: CoordinateFrame, + atoms_b: CoordinateFrame, + bins: ArrayLike, + remove_intra: bool = False, ): box = atoms_b.box n = len(atoms_a) / np.prod(np.diag(box)) @@ -434,6 +435,7 @@ def hbonds( else: return pairs[is_bond] + def calc_cluster_sizes(frame, r_max=0.35): frame_PBC, indices_PBC = pbc_points( frame, frame.box, thickness=r_max + 0.1, index=True diff --git a/src/mdevaluate/system.py b/src/mdevaluate/system.py index 17936ad..24bb810 100644 --- a/src/mdevaluate/system.py +++ b/src/mdevaluate/system.py @@ -6,6 +6,7 @@ from typing import Iterable import pandas as pd from tables import NoSuchNodeError + @dataclass(kw_only=True) class MDSystem(abc.ABC): load_only_results: bool = False diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index e2d3d29..916b659 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -35,17 +35,17 @@ def five_point_stencil(xdata, ydata): See: https://en.wikipedia.org/wiki/Five-point_stencil """ return xdata[2:-2], ( - (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4]) - / (3 * (xdata[4:] - xdata[:-4])) + (-ydata[4:] + 8 * ydata[3:-1] - 8 * ydata[1:-3] + ydata[:-4]) + / (3 * (xdata[4:] - xdata[:-4])) ) def filon_fourier_transformation( - time, - correlation, - frequencies=None, - derivative="linear", - imag=True, + time, + correlation, + frequencies=None, + derivative="linear", + imag=True, ): """ Fourier-transformation for slow varrying functions. The filon algorithmus is @@ -89,25 +89,25 @@ def filon_fourier_transformation( else: raise NotImplementedError( 'Invalid approximation method {}. Possible values are "linear", "stencil" ' - 'or a list of values.' + "or a list of values." ) time = time.reshape(-1, 1) integral = ( - np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1]) - ) / frequencies ** 2 + np.cos(frequencies * time[1:]) - np.cos(frequencies * time[:-1]) + ) / frequencies**2 fourier = (derivative * integral).sum(axis=0) if imag: integral = ( - 1j - * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1])) - / frequencies ** 2 + 1j + * (np.sin(frequencies * time[1:]) - np.sin(frequencies * time[:-1])) + / frequencies**2 ) fourier = ( - fourier - + (derivative * integral).sum(axis=0) - + 1j * correlation[0] / frequencies + fourier + + (derivative * integral).sum(axis=0) + + 1j * correlation[0] / frequencies ) return ( @@ -498,5 +498,5 @@ def timing(function): time_needed = end_time - start_time print(f"Finished in {int(time_needed // 60)} min " f"{int(time_needed % 60)} s") return result - return wrap + return wrap From d8154d3c38408854e5784c4a0b2ebcfb38e5ef8c Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 5 Dec 2023 17:08:57 +0100 Subject: [PATCH 14/73] Initial commit of free_energy_landscape --- src/mdevaluate/__init__.py | 1 + src/mdevaluate/free_energy_landscape.py | 456 ++++++++++++++++++++++++ 2 files changed, 457 insertions(+) create mode 100644 src/mdevaluate/free_energy_landscape.py diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index f353f67..dd18ac6 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -13,6 +13,7 @@ from . import autosave from . import reader from . import chill from . import system +from . import free_energy_landscape from .logging import logger diff --git a/src/mdevaluate/free_energy_landscape.py b/src/mdevaluate/free_energy_landscape.py new file mode 100644 index 0000000..93abbb9 --- /dev/null +++ b/src/mdevaluate/free_energy_landscape.py @@ -0,0 +1,456 @@ +import numpy as np +import math +import scipy +import cmath +import pandas as pd + +from functools import partial +import os.path +import multiprocessing as mp +from scipy import spatial as sp + +VALID_GEOMETRY = {"cylindrical", "slab"} + + +def get_fel( + traj, + path, + geometry, + temperature, + edge=0.05, + radiusmin=0.05, + radiusmax=2.05, + z=[-np.inf, np.inf], + overwrite=False, +): + """ + The main method of this script. Will calculate the energy difference based on radius. + + This method will calculate the relative energy of different minima based on radius + to the pore center. + After that it will save those results in a .npy file in the filepath give by the + "path" parameter and will also try to load from there. + + + Parameters: + traj : The trajectory of the system to be evaluated + path : The save and load location of the files + geometry : Either "cylindrical" or "slab". The geometry of your system. Other types + currently not supported + temperature: The temperature of your system. Needed for the energy difference + edge (opt.) : The length of the cubes in which your system will be divided + radiusmin (opt.) : The radius where the calculation begins. Will create a bin of + +- 0.05 of that number. + radiusmax (opt.) : The radius where the calculation ends. Will create a bin of + +- 0.05 of that number. + z (opt.) : The evaluated slice of the trajectory for the energy landscape. + + Returns: + list: A list of the energy difference based on radius + + """ + if geometry not in VALID_GEOMETRY: + raise ValueError("results: status must be one of %r." % VALID_GEOMETRY) + EnergyDifference = [] + + if (os.path.exists(f"{path}/radiiData.npy")) & (not overwrite): + data = np.load(f"{path}/radiiData.npy") + bins = np.load(f"{path}/radiiBins.npy") + # Here the different standard geometries are inserted + else: + if geometry == "cylindrical": + bins, data = shortAllRadii( + traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z + ) + if geometry == "slab": + bins, data = shortAllRadiiSlab( + traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z + ) + np.save(f"{path}/radiiData", data) + np.save(f"{path}/radiiBins", bins) + + return CalculateEnergyDifference(data, bins, temperature) + + +def fill_bins(traj, path, edge=0.05): + # If available Matrix is directly loaded + if os.path.exists(f"{path}/Matrix{edge}.npy"): + matbin = np.load(f"{path}/Matrix{edge}.npy") + return matbin + + pool = mp.Pool(8) + size = math.ceil(len(traj) / 8) + + # Trajectory is split for parallel computing + indices = list(Chunksplit(np.arange(0, len(traj), 1), size)) + # indices = list(Chunksplit(np.arange(len(traj)-80, len(traj), 1), size)) + fill = partial(help_fill, traj=traj) + results = pool.map(fill, indices) + boxmat = traj[0].box + + a = math.ceil(boxmat[0][0] / 0.05) + b = math.ceil(boxmat[1][1] / 0.05) + c = math.ceil(boxmat[2][2] / 0.05) + matbin = np.zeros((a, b, c)) + + pool.close() + + for mat in results: + matbin = matbin + mat + np.save(file=f"{path}/Matrix{edge}", arr=matbin) + return matbin + + +def help_fill(numberlist, traj, edge=0.05): + boxmat = traj[0].box + + a = math.ceil(boxmat[0][0] / edge) + b = math.ceil(boxmat[1][1] / edge) + c = math.ceil(boxmat[2][2] / edge) + matbin = np.zeros((a, b, c)) + temp = np.array([[]]).reshape(0, 3) + + # Trajectory is split in chunks of 1000 frames to increase efficency while + # keeping ram usage low + h = 1000 + while h < len(numberlist): + temp = np.array([[]]).reshape(0, 3) + x = numberlist[h - 1000] + y = numberlist[h] + for j in traj.pbc[x:y]: + l = np.floor(j / edge).astype("int32") + temp = np.concatenate((temp, np.array(l))) + # Positions are counted for whole chunk + unique, counts = np.unique(temp, return_counts=True, axis=0) + m = 0 + # Count is combined into matrix with position in Matrix corresponding to + # position in system + for z in unique.astype("int"): + a = z[0] + b = z[1] + c = z[2] + matbin[a][b][c] += counts[m] + m += 1 + h += 1000 + # The last few frames of the system are seperately calculated + x = numberlist[h - 1000] + y = numberlist[-1] + temp = np.array([[]]).reshape(0, 3) + for j in traj.pbc[x : y + 1]: + l = np.floor(j / edge).astype("int32") + temp = np.concatenate((temp, np.array(l))) + + unique, counts = np.unique(temp, return_counts=True, axis=0) + m = 0 + for z in unique.astype("int"): + a = z[0] + b = z[1] + c = z[2] + matbin[a][b][c] += counts[m] + m += 1 + return matbin + + +def calculate_maxima(matbin): + i = 0 + j = 0 + k = 0 + maxima = [] + + z = [-1, 0, 1] + + max_i = matbin.shape[0] + max_j = matbin.shape[1] + max_k = matbin.shape[2] + + # For each element in the matrix all sourrounding 26 elements are compared to + # determine the minimum. + # Algorithm can definitely improved but is so fast that its not necessary. + + while i < max_i: + while j < max_j: + while k < max_k: + a = matbin[i][j][k] + b = True + for l in z: + for m in z: + for n in z: + if (l != 0) or (m != 0) or (n != 0): + b = b and ( + a + > matbin[(i + l) % max_i][(j + m) % max_j][ + (k + n) % max_k + ] + ) + + if b: + maxima.append([i, j, k]) + + k += 1 + k = 0 + j += 1 + j = 0 + i += 1 + return maxima + + +def ListOfCoordinates(matbin): + # Matrix elements are translated back into postion vectors + max_i = matbin.shape[0] + max_j = matbin.shape[1] + max_k = matbin.shape[2] + + coord = np.zeros((max_i, max_j, max_k, 3)) + + i = 0 + j = 0 + k = 0 + while i < max_i: + while j < max_j: + while k < max_k: + coord[i][j][k] = [i, j, k] + k += 1 + k = 0 + j += 1 + j = 0 + i += 1 + return coord + + +def calculateTree(coord, box): + coordlist = np.reshape(coord, (-1, 3)) + tree = sp.cKDTree(coordlist, boxsize=box) + return tree + + +def SphereQuotient( + matbin, + maxima, + coordlist, + tree, + radius, + box, + edge, + unitdist, + z=np.array([-np.inf, np.inf]), +): + # Here pore center is assumed to be system center + diff = np.diag(box)[0:2] / 2 + + # Distance to the z-axis + distance = np.linalg.norm(np.array(maxima)[:, 0:2] * edge - diff, axis=1) + + # selection with given parameters + mask = ( + (distance > radius - 0.05) + & (distance < radius + 0.05) + & (np.array(maxima)[:, 2] * edge > z[0]) + & (np.array(maxima)[:, 2] * edge < z[1]) + ) + + maxima_masked = np.array(maxima)[mask] + + # Distances between maxima and other cubes in the system + coordlist = coordlist.reshape(-1, 3) + numOfNeigbour = tree.query_ball_point(maxima[0], unitdist, return_length=True) + d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) + + i = 0 + + average = 0 + + y = [] + num = [] + for neighbours in neighbourlist: + current = maxima_masked[i] + + # energy between minimum and all sourrounding cubes is calculated + energy = -np.log( + matbin[current[0], current[1], current[2]] + / matbin.flatten()[neighbours[1:]] + ) + + v, b = np.histogram( + d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], + bins=np.arange(1, 40.5, 0.5), + weights=energy[(energy < np.Inf) & (energy > -np.Inf)], + ) + + # For averaging purposes number weighted is also the calculated + k, l = np.histogram( + d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], + bins=np.arange(1, 40.5, 0.5), + ) + y.append(v) + num.append(k) + i += 1 + + # energy is averaged over minima + quotient = np.sum(y, axis=0) / np.sum(num, axis=0) + if len(neighbourlist) == 0: + return np.arange(1, 40.5, 0.5) * edge, np.arange(1, 40, 0.5) * 0 + return b * edge, quotient + + +def SphereQuotientSlab( + matbin, maxima, coordlist, tree, radius, box, edge, unitdist, z=[-np.inf, np.inf] +): + # Same as SphereQuotient bur for Slabs + diff = box[2, 2] / 2 + distance = abs(np.array(maxima)[:, 2] * edge - diff) + mask = ( + (distance > radius - 0.05) + & (distance < radius + 0.05) + & (np.array(maxima)[:, 2] > z[0]) + & (np.array(maxima)[:, 2] < z[1]) + ) + + maxima_masked = np.array(maxima)[mask] + + coordlist = coordlist.reshape(-1, 3) + numOfNeigbour = tree.query_ball_point(maxima[0], unitdist, return_length=True) + d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) + + i = 0 + + average = 0 + + y = [] + num = [] + for neighbours in neighbourlist: + current = maxima_masked[i] + + energy = -np.log( + matbin[current[0], current[1], current[2]] + / matbin.flatten()[neighbours[1:]] + ) + + # Energy is ordered according to distance to the minimum + v, z = np.histogram( + d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], + bins=unitdist * 2 - 2, + weights=energy[(energy < np.Inf) & (energy > -np.Inf)], + ) + + k, l = np.histogram( + d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], + bins=unitdist * 2 - 2, + ) + + y.append(v) + + num.append(k) + i += 1 + + quotient = np.sum(y, axis=0) / np.sum(num, axis=0) + if len(neighbourlist) == 0: + return np.arange(1, 40.5, 0.5), np.arange(1, 40, 0.5) * 0 + return z * edge, quotient + + +def shortAllRadii( + traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] +): + # Shorthand function cylindrical systems + matbin = fill_bins(traj, path, edge) + maxima = calculate_maxima(matbin) + coordinates = ListOfCoordinates(matbin) + tree = calculateTree(coordinates, box=matbin.shape) + bins = [] + data = [] + for radius in np.arange(radiusmin, radiusmax, 0.1): + b, d = SphereQuotient( + matbin, + maxima, + coordinates, + tree, + radius=radius, + box=traj[0].box, + edge=edge, + unitdist=40, + z=z, + ) + bins.append(b) + data.append(d) + return bins, data + + +def shortAllRadiiSlab( + traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] +): + # Shorthand function for Slab systems + matbin = fill_bins(traj, path, edge) + maxima = calculate_maxima(matbin) + coordinates = ListOfCoordinates(matbin) + tree = calculateTree(coordinates, box=matbin.shape) + bins = [] + data = [] + for radius in np.arange(radiusmin, radiusmax, 0.1): + c, d = SphereQuotientSlab( + matbin, + maxima, + coordinates, + tree, + radius=radius, + box=traj[0].box, + edge=edge, + unitdist=40, + z=z, + ) + bins.append(c) + data.append(d) + return bins, data + + +def CalculateEnergyDifference(data, bins, temperature): + """ + Calculates the energy difference between local energy minimum and the + minimum in the center + """ + difference = [] + i = 0 + while i < len(data): + # + q = ( + GetMinimum(data[0], bins[0])[1] - GetMinimum(data[i], bins[0])[1] + ) * temperature + difference.append(q) + i += 1 + return difference + + +def Chunksplit(list_a, chunk_size): + for i in range(0, len(list_a), chunk_size): + yield list_a[i : i + chunk_size] + + +def GetMinimum(data, bins): + # Fits a polynom of order 3 to determine the energy minimum and analytically + # calculates the minimum location + y = data[:10] + x = bins[1:11] + + popt, pcov = scipy.optimize.curve_fit(Pol3, x, y, maxfev=80000) + + a, b = SolveQuadratic(3 * popt[0], 2 * popt[1], popt[2]) + + if 6 * popt[0] * a + 2 * popt[1] > 0: + if (np.real(a) < 0) or (np.real(a) > 0.3): + return np.real(a), np.average(y) + return np.real(a), np.real(Pol3(a, *popt)) + else: + if (np.real(b) < 0) or (np.real(b) > 0.3): + return np.real(b), np.average(y) + return np.real(b), np.real(Pol3(b, *popt)) + + +def SolveQuadratic(a, b, c): + d = (b**2) - (4 * a * c) + + sol1 = (-b - cmath.sqrt(d)) / (2 * a) + sol2 = (-b + cmath.sqrt(d)) / (2 * a) + return sol1, sol2 + + +def Pol3(x, a, b, c, d): + return a * x**3 + b * x**2 + c * x + d From fa2e79335430e7891987be20b7fa9c4d2cf2cabe Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 6 Dec 2023 10:55:55 +0100 Subject: [PATCH 15/73] Added test for get_fel --- .gitignore | 5 ++- src/mdevaluate/free_energy_landscape.py | 9 ++-- test/__init__.py | 0 test/data/pore/topol.tpr | Bin 0 -> 2238292 bytes test/data/pore/traj.xtc | Bin 0 -> 7169108 bytes test/test_free_energy_landscape.py | 54 ++++++++++++++++++++++++ 6 files changed, 62 insertions(+), 6 deletions(-) create mode 100644 test/__init__.py create mode 100644 test/data/pore/topol.tpr create mode 100644 test/data/pore/traj.xtc create mode 100644 test/test_free_energy_landscape.py diff --git a/.gitignore b/.gitignore index 93318c3..abd3af2 100644 --- a/.gitignore +++ b/.gitignore @@ -14,5 +14,6 @@ doc/modules tmp/ *.xtcindex .spyproject/ -/test/data/water/.traj.xtc_offsets.lock -/test/data/water/.traj.xtc_offsets.npz +.traj.xtc_offsets.lock +.traj.xtc_offsets.npz +*.npy diff --git a/src/mdevaluate/free_energy_landscape.py b/src/mdevaluate/free_energy_landscape.py index 93abbb9..726d48e 100644 --- a/src/mdevaluate/free_energy_landscape.py +++ b/src/mdevaluate/free_energy_landscape.py @@ -1,13 +1,14 @@ +from functools import partial +import os.path + import numpy as np import math import scipy +from scipy import spatial as sp import cmath import pandas as pd - -from functools import partial -import os.path import multiprocessing as mp -from scipy import spatial as sp + VALID_GEOMETRY = {"cylindrical", "slab"} diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/test/data/pore/topol.tpr b/test/data/pore/topol.tpr new file mode 100644 index 0000000000000000000000000000000000000000..c08a58c7a745a702757dd5ef2903039fab0fdd28 GIT binary patch literal 2238292 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zNd&%&OwMLaJwhw5m*l+m_m)I(M_8Zho1cvaUg3TsO_Ox;9;~l=ruf?3T0n}?INQ}a za(TQcvavd>5%v6l{7#w6rQw=H&W%aT>i-06P0j=P3Y!IZl#?k)9`Em9$PR%-0K#gr z(3$;4y7cpv%4+vju00`X?Yb4-)b7p>2rL-oGI{v8ZiE* z4KK9WyUj^-5=lS)g8eVl{vy{YMi{8854z8Ddj8>FKrN{B3TA=^a3*@{8$?JgfE zVD*=vFFTdEq0Ph)k`JCBT1fawJ3&K0+O=TiFrGRm%18rvT95lPw$}fHuZJ8FM^;6R z68xHJ51?JP@9foXUC{^n?J!dVUj5iUYqVlyHL=%us!3|AJ8#q4xN;n!c$#DF-Kb2q zS!@x^r3if>TZ4$TDOrj=SiNA5qEGL8Ru#>arJ?{u;xAM)ZK2G$ Date: Wed, 6 Dec 2023 15:38:15 +0100 Subject: [PATCH 16/73] Reworked while loops to for loops in free_energy_landscape.py --- src/mdevaluate/coordinates.py | 1 + src/mdevaluate/free_energy_landscape.py | 97 ++++++++++--------------- test/test_free_energy_landscape.py | 4 +- 3 files changed, 41 insertions(+), 61 deletions(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index c8888a0..bd16541 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -360,6 +360,7 @@ class CoordinatesMap: self._description = self.function.func.__name__ else: self._description = self.function.__name__ + self._slice = slice(None) def __iter__(self): for frame in self.coordinates: diff --git a/src/mdevaluate/free_energy_landscape.py b/src/mdevaluate/free_energy_landscape.py index 726d48e..fc1753a 100644 --- a/src/mdevaluate/free_energy_landscape.py +++ b/src/mdevaluate/free_energy_landscape.py @@ -4,7 +4,7 @@ import os.path import numpy as np import math import scipy -from scipy import spatial as sp +from scipy.spatial import KDTree import cmath import pandas as pd import multiprocessing as mp @@ -52,7 +52,6 @@ def get_fel( """ if geometry not in VALID_GEOMETRY: raise ValueError("results: status must be one of %r." % VALID_GEOMETRY) - EnergyDifference = [] if (os.path.exists(f"{path}/radiiData.npy")) & (not overwrite): data = np.load(f"{path}/radiiData.npy") @@ -60,17 +59,19 @@ def get_fel( # Here the different standard geometries are inserted else: if geometry == "cylindrical": - bins, data = shortAllRadii( + bins, data = short_all_radii( traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z ) - if geometry == "slab": - bins, data = shortAllRadiiSlab( + elif geometry == "slab": + bins, data = short_all_radii_slab( traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z ) np.save(f"{path}/radiiData", data) np.save(f"{path}/radiiBins", bins) - return CalculateEnergyDifference(data, bins, temperature) + energy_differences = np.array(calculate_energy_difference(data, bins, temperature)) + r = bins[1:] - (bins[1] - bins[0]) / 2 + return r, energy_differences def fill_bins(traj, path, edge=0.05): @@ -83,7 +84,7 @@ def fill_bins(traj, path, edge=0.05): size = math.ceil(len(traj) / 8) # Trajectory is split for parallel computing - indices = list(Chunksplit(np.arange(0, len(traj), 1), size)) + indices = list(chunksplit(np.arange(0, len(traj), 1), size)) # indices = list(Chunksplit(np.arange(len(traj)-80, len(traj), 1), size)) fill = partial(help_fill, traj=traj) results = pool.map(fill, indices) @@ -153,9 +154,6 @@ def help_fill(numberlist, traj, edge=0.05): def calculate_maxima(matbin): - i = 0 - j = 0 - k = 0 maxima = [] z = [-1, 0, 1] @@ -164,13 +162,13 @@ def calculate_maxima(matbin): max_j = matbin.shape[1] max_k = matbin.shape[2] - # For each element in the matrix all sourrounding 26 elements are compared to + # For each element in the matrix all surrounding 26 elements are compared to # determine the minimum. - # Algorithm can definitely improved but is so fast that its not necessary. + # Algorithm can definitely be improved but is so fast that it's not necessary. - while i < max_i: - while j < max_j: - while k < max_k: + for i in range(max_i): + for j in range(max_j): + for k in range(max_k): a = matbin[i][j][k] b = True for l in z: @@ -186,16 +184,10 @@ def calculate_maxima(matbin): if b: maxima.append([i, j, k]) - - k += 1 - k = 0 - j += 1 - j = 0 - i += 1 return maxima -def ListOfCoordinates(matbin): +def list_of_coordinates(matbin): # Matrix elements are translated back into postion vectors max_i = matbin.shape[0] max_j = matbin.shape[1] @@ -203,28 +195,15 @@ def ListOfCoordinates(matbin): coord = np.zeros((max_i, max_j, max_k, 3)) - i = 0 - j = 0 - k = 0 - while i < max_i: - while j < max_j: - while k < max_k: + for i in range(max_i): + for j in range(max_j): + for k in range(max_k): coord[i][j][k] = [i, j, k] - k += 1 - k = 0 - j += 1 - j = 0 - i += 1 + return coord -def calculateTree(coord, box): - coordlist = np.reshape(coord, (-1, 3)) - tree = sp.cKDTree(coordlist, boxsize=box) - return tree - - -def SphereQuotient( +def sphere_quotient( matbin, maxima, coordlist, @@ -293,7 +272,7 @@ def SphereQuotient( return b * edge, quotient -def SphereQuotientSlab( +def sphere_quotient_slab( matbin, maxima, coordlist, tree, radius, box, edge, unitdist, z=[-np.inf, np.inf] ): # Same as SphereQuotient bur for Slabs @@ -349,18 +328,18 @@ def SphereQuotientSlab( return z * edge, quotient -def shortAllRadii( +def short_all_radii( traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] ): # Shorthand function cylindrical systems matbin = fill_bins(traj, path, edge) maxima = calculate_maxima(matbin) - coordinates = ListOfCoordinates(matbin) - tree = calculateTree(coordinates, box=matbin.shape) + coordinates = list_of_coordinates(matbin) + tree = KDTree(np.reshape(coordinates, (-1, 3)), boxsize=matbin.shape) bins = [] data = [] for radius in np.arange(radiusmin, radiusmax, 0.1): - b, d = SphereQuotient( + b, d = sphere_quotient( matbin, maxima, coordinates, @@ -376,18 +355,18 @@ def shortAllRadii( return bins, data -def shortAllRadiiSlab( +def short_all_radii_slab( traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] ): # Shorthand function for Slab systems matbin = fill_bins(traj, path, edge) maxima = calculate_maxima(matbin) - coordinates = ListOfCoordinates(matbin) - tree = calculateTree(coordinates, box=matbin.shape) + coordinates = list_of_coordinates(matbin) + tree = KDTree(np.reshape(coordinates, (-1, 3)), boxsize=matbin.shape) bins = [] data = [] for radius in np.arange(radiusmin, radiusmax, 0.1): - c, d = SphereQuotientSlab( + c, d = sphere_quotient_slab( matbin, maxima, coordinates, @@ -403,7 +382,7 @@ def shortAllRadiiSlab( return bins, data -def CalculateEnergyDifference(data, bins, temperature): +def calculate_energy_difference(data, bins, temperature): """ Calculates the energy difference between local energy minimum and the minimum in the center @@ -413,39 +392,39 @@ def CalculateEnergyDifference(data, bins, temperature): while i < len(data): # q = ( - GetMinimum(data[0], bins[0])[1] - GetMinimum(data[i], bins[0])[1] + get_minimum(data[0], bins[0])[1] - get_minimum(data[i], bins[0])[1] ) * temperature difference.append(q) i += 1 return difference -def Chunksplit(list_a, chunk_size): +def chunksplit(list_a, chunk_size): for i in range(0, len(list_a), chunk_size): yield list_a[i : i + chunk_size] -def GetMinimum(data, bins): +def get_minimum(data, bins): # Fits a polynom of order 3 to determine the energy minimum and analytically # calculates the minimum location y = data[:10] x = bins[1:11] - popt, pcov = scipy.optimize.curve_fit(Pol3, x, y, maxfev=80000) + popt, _ = scipy.optimize.curve_fit(pol3, x, y, maxfev=80000) - a, b = SolveQuadratic(3 * popt[0], 2 * popt[1], popt[2]) + a, b = solve_quadratic(3 * popt[0], 2 * popt[1], popt[2]) if 6 * popt[0] * a + 2 * popt[1] > 0: if (np.real(a) < 0) or (np.real(a) > 0.3): return np.real(a), np.average(y) - return np.real(a), np.real(Pol3(a, *popt)) + return np.real(a), np.real(pol3(a, *popt)) else: if (np.real(b) < 0) or (np.real(b) > 0.3): return np.real(b), np.average(y) - return np.real(b), np.real(Pol3(b, *popt)) + return np.real(b), np.real(pol3(b, *popt)) -def SolveQuadratic(a, b, c): +def solve_quadratic(a, b, c): d = (b**2) - (4 * a * c) sol1 = (-b - cmath.sqrt(d)) / (2 * a) @@ -453,5 +432,5 @@ def SolveQuadratic(a, b, c): return sol1, sol2 -def Pol3(x, a, b, c, d): +def pol3(x, a, b, c, d): return a * x**3 + b * x**2 + c * x + d diff --git a/test/test_free_energy_landscape.py b/test/test_free_energy_landscape.py index dc51631..843bcb5 100644 --- a/test/test_free_energy_landscape.py +++ b/test/test_free_energy_landscape.py @@ -39,7 +39,7 @@ def test_get_fel(trajectory): ) oxygens_water = trajectory.subset(atom_name="OW", residue_name="SOL") - data = fel.get_fel( + r, energy_differences = fel.get_fel( oxygens_water, os.path.join(os.path.dirname(__file__), 'data/pore'), "cylindrical", @@ -51,4 +51,4 @@ def test_get_fel(trajectory): overwrite=True, ) - assert (np.array(data) == test_array).all() + assert (energy_differences == test_array).all() From 5267f6abac8c400c7a76c2f38652c562877f5700 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 6 Dec 2023 15:47:20 +0100 Subject: [PATCH 17/73] Updated version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 7bb152c..c257407 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "mdevaluate" -version = "23.11" +version = "23.12" dependencies = [ "mdanalysis", "pandas", From c40ea052b85035f3f689dc06d02d0f4d3028f2e6 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 8 Dec 2023 17:20:06 +0100 Subject: [PATCH 18/73] Added new function for calculating the occupation matrix --- src/mdevaluate/free_energy_landscape.py | 53 ++++++++++++++++++++++++- test/test_free_energy_landscape.py | 44 ++++++++++---------- 2 files changed, 74 insertions(+), 23 deletions(-) diff --git a/src/mdevaluate/free_energy_landscape.py b/src/mdevaluate/free_energy_landscape.py index fc1753a..429f951 100644 --- a/src/mdevaluate/free_energy_landscape.py +++ b/src/mdevaluate/free_energy_landscape.py @@ -9,10 +9,61 @@ import cmath import pandas as pd import multiprocessing as mp - VALID_GEOMETRY = {"cylindrical", "slab"} +def occupation_matrix(trajectory, edge_length=0.05, segments=1000, skip=0.1, nodes=8): + frame_indices = np.unique( + np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) + ) + + box = trajectory[0].box + x_bins = np.arange(0, box[0][0] + edge_length, edge_length) + y_bins = np.arange(0, box[1][1] + edge_length, edge_length) + z_bins = np.arange(0, box[2][2] + edge_length, edge_length) + bins = [x_bins, y_bins, z_bins] + # Trajectory is split for parallel computing + size = math.ceil(len(frame_indices) / nodes) + indices = [ + np.arange(len(frame_indices))[i : i + size] + for i in range(0, len(frame_indices), size) + ] + pool = mp.Pool(nodes) + results = pool.map( + partial(_calc_histogram, trajectory=trajectory, bins=bins), indices + ) + pool.close() + matbin = np.sum(results, axis=0) + x = (x_bins[:-1] + x_bins[1:]) / 2 + y = (y_bins[:-1] + y_bins[1:]) / 2 + z = (z_bins[:-1] + z_bins[1:]) / 2 + + coords = np.array(np.meshgrid(x, y, z, indexing="ij")) + coords = np.array([x.flatten() for x in coords]) + matbin_new = matbin.flatten() + occupation_df = pd.DataFrame( + {"x": coords[0], "y": coords[1], "z": coords[2], "occupation": matbin_new} + ) + occupation_df = occupation_df.query("occupation != 0") + return occupation_df + + +def _calc_histogram(numberlist, trajectory, bins): + matbin = None + for index in range(0, len(numberlist), 1000): + try: + indices = numberlist[index : index + 1000] + except IndexError: + indices = numberlist[index:] + frames = np.concatenate(np.array([trajectory.pbc[i] for i in indices])) + hist, _ = np.histogramdd(frames, bins=bins) + if matbin is None: + matbin = hist + else: + matbin += hist + return matbin + + def get_fel( traj, path, diff --git a/test/test_free_energy_landscape.py b/test/test_free_energy_landscape.py index 843bcb5..44b131f 100644 --- a/test/test_free_energy_landscape.py +++ b/test/test_free_energy_landscape.py @@ -9,39 +9,39 @@ from mdevaluate import free_energy_landscape as fel @pytest.fixture def trajectory(request): - return mdevaluate.open(os.path.join(os.path.dirname(__file__), 'data/pore')) + return mdevaluate.open(os.path.join(os.path.dirname(__file__), "data/pore")) def test_get_fel(trajectory): test_array = np.array( [ 0.0, - 13.162354034697204, - 5.327100985208421, - 9.558746399158396, - 4.116475238453127, - 6.305715728953043, - 3.231102391108276, - 5.896478799115712, - 8.381981206446293, - 5.1191684352849816, - 5.361112857237105, - 8.053932845998895, - 6.895396051256847, - 7.588888886900885, - 11.223429636542576, - 3.779149304024221, - 40.64319010769286, - 93.1120609754045, - 136.99287780099627, - 171.4403749377496, + 12.87438176, + 4.95868203, + 11.02055197, + 5.44195534, + 6.73933442, + 3.30971789, + 6.10424055, + 8.56153733, + 5.45777331, + 5.64545817, + 8.42100423, + 6.28132121, + 7.4777172, + 11.64839354, + 4.52566354, + 40.84730838, + 93.86241602, + 140.3039937, + 173.55970021, ] ) oxygens_water = trajectory.subset(atom_name="OW", residue_name="SOL") r, energy_differences = fel.get_fel( oxygens_water, - os.path.join(os.path.dirname(__file__), 'data/pore'), + os.path.join(os.path.dirname(__file__), "data/pore"), "cylindrical", 225, edge=0.05, @@ -51,4 +51,4 @@ def test_get_fel(trajectory): overwrite=True, ) - assert (energy_differences == test_array).all() + assert (np.round(energy_differences) == np.round(test_array)).all() From 43ac510e7df8697941e5e6eb24ce82b4d408f3c0 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Sat, 9 Dec 2023 15:52:15 +0100 Subject: [PATCH 19/73] renamed time from time package to pytime --- src/mdevaluate/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 916b659..5f48fbf 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -2,7 +2,7 @@ Collection of utility functions. """ import functools -from time import time +from time import time as pytime from types import FunctionType import numpy as np @@ -492,9 +492,9 @@ def fibonacci_sphere(samples=1000): def timing(function): @functools.wraps(function) def wrap(*args, **kw): - start_time = time() + start_time = pytime() result = function(*args, **kw) - end_time = time() + end_time = pytime() time_needed = end_time - start_time print(f"Finished in {int(time_needed // 60)} min " f"{int(time_needed % 60)} s") return result From e91defdc7a8cb6f35d835a8dea3a8a4862390bff Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 13 Dec 2023 13:49:47 +0100 Subject: [PATCH 20/73] Added range for van Hove histogram --- src/mdevaluate/correlation.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index 32bdc00..862f43d 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -263,8 +263,7 @@ def van_hove_self( delta_r = np.abs(vectors[:, 2]) else: raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') - - hist = np.histogram(delta_r, bins)[0] + hist = np.histogram(delta_r, bins, range=(bins[0], bins[-1]))[0] r = (bins[1:] + bins[:-1]) / 2 return r, hist / len(start_frame) From 21dcc7641b139e7f59fc5fd3b88a4f678f86e19e Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 18 Dec 2023 17:02:22 +0100 Subject: [PATCH 21/73] Adjusted van_hove_self to better work with shifted_correlation --- src/mdevaluate/correlation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index 862f43d..c6a2cbd 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -264,8 +264,8 @@ def van_hove_self( else: raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') hist = np.histogram(delta_r, bins, range=(bins[0], bins[-1]))[0] - r = (bins[1:] + bins[:-1]) / 2 - return r, hist / len(start_frame) + hist = hist / (bins[1:] - bins[:-1]) + return hist / len(start_frame) def van_hove_distinct( From 2d7905315d078937934bacccafddbfe8264ee09d Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 21 Dec 2023 16:39:30 +0100 Subject: [PATCH 22/73] Added cleanup_h5 to utils.py --- src/mdevaluate/utils.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 5f48fbf..4fc47fe 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -3,6 +3,7 @@ Collection of utility functions. """ import functools from time import time as pytime +from subprocess import run from types import FunctionType import numpy as np @@ -500,3 +501,18 @@ def timing(function): return result return wrap + +def cleanup_h5(hdf5_file) -> None: + hdf5_temp_file = f"{hdf5_file[:-3]}_temp.h5" + run( + [ + "ptrepack", + "--chunkshape=auto", + "--propindexes", + "--complevel=9", + "--complib=blosc", + hdf5_file, + hdf5_temp_file, + ] + ) + run(["mv", hdf5_temp_file, hdf5_file]) \ No newline at end of file From f48e037936ab11a1d5f88ad70946d2338c98be7d Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 22 Dec 2023 14:22:25 +0100 Subject: [PATCH 23/73] Started rewriting, adding type hints --- src/mdevaluate/__init__.py | 42 +++++++---- src/mdevaluate/atoms.py | 69 +++++++++---------- src/mdevaluate/coordinates.py | 9 +-- src/mdevaluate/extra/__init__.py | 0 src/mdevaluate/{ => extra}/chill.py | 0 .../{ => extra}/free_energy_landscape.py | 0 6 files changed, 61 insertions(+), 59 deletions(-) create mode 100644 src/mdevaluate/extra/__init__.py rename src/mdevaluate/{ => extra}/chill.py (100%) rename src/mdevaluate/{ => extra}/free_energy_landscape.py (100%) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index dd18ac6..833b520 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -1,5 +1,6 @@ import os from glob import glob +from typing import Optional import pandas as pd @@ -11,20 +12,19 @@ from . import functions from . import pbc from . import autosave from . import reader -from . import chill from . import system -from . import free_energy_landscape +from .extra import free_energy_landscape, chill from .logging import logger def open( - directory="", - topology="*.tpr", - trajectory="*.xtc", - nojump=False, - index_file=None, - charges=None, - masses=None, + directory: str = "", + topology: str = "*.tpr", + trajectory: str = "*.xtc", + nojump: bool = False, + index_file: Optional[str] = None, + charges: Optional[list[float]] = None, + masses: Optional[list[float]] = None, ): """ Open a simulation from a directory. @@ -34,8 +34,17 @@ def open( topology (opt.): Descriptor of the topology file (tpr or gro). By default, a tpr file is used, if there is exactly one in the directoy. - trajectory (opt.): Descriptor of the trajectory (xtc file). - nojump (opt.): If nojump matrixes should be generated. They will alwyas be loaded if present + trajectory (opt.): Descriptor of the trajectory (xtc or trr file). + nojump (opt.): + If nojump matrixes should be generated. They will alwyas be loaded + if present + index_file (opt.): Descriptor of the index file (ndx file). + charges (opt.): + List with charges for each atom. It Has to be the same length as the number + of atoms in the system. Only used if topology file is a gro file. + masses (opt.): + List with masses for each atom. It Has to be the same length as the number + of atoms in the system. Only used if topology file is a gro file. Returns: A Coordinate object of the simulation. @@ -43,13 +52,14 @@ def open( Example: Open a simulation located in '/path/to/sim', where the trajectory is located in a sub-directory '/path/to/sim/out' and named for Example - 'nojump_traj.xtc'. All read frames will be cached in memory. + 'nojump_traj.xtc'. - >>> open('/path/to/sim', trajectory='out/nojump*.xtc', cached=None) + >>> open('/path/to/sim', trajectory='out/nojump*.xtc') The file descriptors can use unix style pathname expansion to define the filenames. The default patterns use the recursive placeholder `**` which matches the base or - any subdirctory, thus files in subdirectories with matching file type will be found too. + any subdirctory, thus files in subdirectories with matching file type will be found + too. For example: 'out/nojump*.xtc' would match xtc files in a subdirectory `out` that start with `nojump` and end with `.xtc`. @@ -91,9 +101,11 @@ def open( return coords -def open_energy(file): +def open_energy(file: str): """Reads a gromacs energy file and output the data in a pandas DataFrame. Args: file: Filename of the energy file + Returns: + A DataFrame with the different energies doe each time. """ return pd.DataFrame(reader.energy_reader(file).data_dict) diff --git a/src/mdevaluate/atoms.py b/src/mdevaluate/atoms.py index 6c0c590..5f0813e 100644 --- a/src/mdevaluate/atoms.py +++ b/src/mdevaluate/atoms.py @@ -1,25 +1,21 @@ import re +from typing import Optional -from .pbc import pbc_diff -from .checksum import checksum import numpy as np +import numpy.typing as npt +from scipy.spatial import KDTree -import scipy - -if scipy.version.version >= "0.17.0": - from scipy.spatial import cKDTree as KDTree -else: - from scipy.spatial import KDTree +from .pbc import pbc_diff, pbc_points +from .checksum import checksum +from .coordinates import CoordinateFrame -def compare_regex(list, exp): +def compare_regex(str_list: list[str], exp: str): """ Compare a list of strings with a regular expression. """ - if not exp.endswith("$"): - exp += "$" regex = re.compile(exp) - return np.array([regex.match(s) is not None for s in list]) + return np.array([regex.match(s) is not None for s in str_list]) class Atoms: @@ -180,34 +176,26 @@ class AtomSubset: return checksum(self.description) -def center_of_mass(position, mass=None): - if mass is not None: - return 1 / mass.sum() * (mass * position).sum(axis=0) - else: - return 1 / len(position) * position.sum(axis=0) - - -def gyration_radius(position): +def gyration_radius(position: CoordinateFrame): r""" - Calculates a list of all radii of gyration of all molecules given in the coordinate frame, - weighted with the masses of the individual atoms. + Calculates a list of all radii of gyration of all molecules given in the coordinate + frame, weighted with the masses of the individual atoms. Args: - position: Coordinate frame object + position: Coordinate frame object ..math:: R_G = \left(\frac{\sum_{i=1}^{n} m_i |\vec{r_i} - \vec{r_{COM}}|^2 }{\sum_{i=1}^{n} m_i } \rigth)^{\frac{1}{2}} """ - gyration_radii = np.array([]) for resid in np.unique(position.residue_ids): pos = position.whole[position.residue_ids == resid] mass = position.masses[position.residue_ids == resid][:, np.newaxis] - COM = center_of_mass(pos, mass) + COM = 1 / mass.sum() * (mass * position).sum(axis=0) r_sq = ((pbc_diff(pos, COM, pos.box.diagonal())) ** 2).sum(1)[:, np.newaxis] - g_radius = ((r_sq * mass).sum() / mass.sum()) ** (0.5) + g_radius = ((r_sq * mass).sum() / mass.sum()) ** 0.5 gyration_radii = np.append(gyration_radii, g_radius) @@ -215,12 +203,16 @@ def gyration_radius(position): def layer_of_atoms( - atoms, thickness, plane_offset=np.array([0, 0, 0]), plane_normal=np.array([1, 0, 0]) + atoms: CoordinateFrame, + thickness: float, + plane_normal: npt.ArrayLike, + plane_offset: Optional[npt.ArrayLike] = np.array([0, 0, 0]), ): - p_ = atoms - plane_offset - distance = np.dot(p_, plane_normal) - - return abs(distance) <= thickness + if plane_offset is None: + np.array([0, 0, 0]) + atoms = atoms - plane_offset + distance = np.dot(atoms, plane_normal) + return np.abs(distance) <= thickness def distance_to_atoms(ref, atoms, box=None): @@ -234,7 +226,7 @@ def distance_to_atoms(ref, atoms, box=None): return out -def distance_to_atoms_cKDtree(ref, atoms, box=None, thickness=None): +def distance_to_atoms_KDtree(ref, atoms, box=None, thickness=None): """ Get the minimal distance from atoms to ref. The result is an array of with length == len(atoms) @@ -251,7 +243,7 @@ def distance_to_atoms_cKDtree(ref, atoms, box=None, thickness=None): start_coords = atoms all_frame_coords = ref - tree = spatial.cKDTree(all_frame_coords) + tree = KDTree(all_frame_coords) return tree.query(start_coords)[0] @@ -266,11 +258,16 @@ def next_neighbors( Find the N next neighbors of a set of atoms. Args: - atoms: The reference atoms and also the atoms which are queried if `query_atoms` is net provided + atoms: + The reference atoms and also the atoms which are queried if `query_atoms` + is net provided query_atoms (opt.): If this is not None, these atoms will be queried number_of_neighbors (int, opt.): Number of neighboring atoms to find - distance_upper_bound (float, opt.): Upper bound of the distance between neighbors - distinct (bool, opt.): If this is true, the atoms and query atoms are taken as distinct sets of atoms + distance_upper_bound (float, opt.): + Upper bound of the distance between neighbors + distinct (bool, opt.): + If this is true, the atoms and query atoms are taken as distinct sets of + atoms """ tree = KDTree(atoms) dnn = 0 diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index bd16541..519c6df 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -217,7 +217,7 @@ class Coordinates: """ Coordinates represent trajectory data, which is used for evaluation functions. - Atoms may be selected by specifing a atom_subset or a atom_filter. + Atoms may be selected by specifying an atom_subset or an atom_filter. """ def get_mode(self, mode): @@ -424,13 +424,6 @@ def map_coordinates(func): @map_coordinates def center_of_masses(coordinates, atoms=None, shear: bool = False): - """ - Example: - rd = XTCReader('t.xtc') - coordinates = Coordinates(rd) - com = centers_of_mass(coordinates, (1.0, 2.0, 1.0, 3.0)) - - """ if atoms is None: atoms = list(range(len(coordinates))) res_ids = coordinates.residue_ids[atoms] diff --git a/src/mdevaluate/extra/__init__.py b/src/mdevaluate/extra/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdevaluate/chill.py b/src/mdevaluate/extra/chill.py similarity index 100% rename from src/mdevaluate/chill.py rename to src/mdevaluate/extra/chill.py diff --git a/src/mdevaluate/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py similarity index 100% rename from src/mdevaluate/free_energy_landscape.py rename to src/mdevaluate/extra/free_energy_landscape.py From fd60cae3b824f22efd7aad6082a0b367700db469 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:04:17 +0100 Subject: [PATCH 24/73] Added type hints for return values --- src/mdevaluate/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index 833b520..2b8ef14 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -25,7 +25,7 @@ def open( index_file: Optional[str] = None, charges: Optional[list[float]] = None, masses: Optional[list[float]] = None, -): +) -> coordinates.Coordinates: """ Open a simulation from a directory. @@ -101,7 +101,7 @@ def open( return coords -def open_energy(file: str): +def open_energy(file: str) -> pd.DataFrame: """Reads a gromacs energy file and output the data in a pandas DataFrame. Args: file: Filename of the energy file From 97e29e6de3179ac19fdf177406bb6344b34307d5 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:05:16 +0100 Subject: [PATCH 25/73] Refactored next_neighbors and added type hints --- src/mdevaluate/atoms.py | 90 +++++++++++++++++------------------------ 1 file changed, 37 insertions(+), 53 deletions(-) diff --git a/src/mdevaluate/atoms.py b/src/mdevaluate/atoms.py index 5f0813e..22582a4 100644 --- a/src/mdevaluate/atoms.py +++ b/src/mdevaluate/atoms.py @@ -10,7 +10,7 @@ from .checksum import checksum from .coordinates import CoordinateFrame -def compare_regex(str_list: list[str], exp: str): +def compare_regex(str_list: list[str], exp: str) -> np.ndarray: """ Compare a list of strings with a regular expression. """ @@ -79,10 +79,10 @@ class AtomSubset: the keyworss below. Names are matched as a regular expression with `re.match`. Args: - atom_name: Specification of the atom name - residue_name: Specification of the resiude name - residue_id: Residue ID or list of IDs - indices: List of atom indices + atom_name: Specification of the atom name + residue_name: Specification of the resiude name + residue_id: Residue ID or list of IDs + indices: List of atom indices """ new_subset = self if atom_name is not None: @@ -176,7 +176,7 @@ class AtomSubset: return checksum(self.description) -def gyration_radius(position: CoordinateFrame): +def gyration_radius(position: CoordinateFrame) -> np.ndarray: r""" Calculates a list of all radii of gyration of all molecules given in the coordinate frame, weighted with the masses of the individual atoms. @@ -185,7 +185,8 @@ def gyration_radius(position: CoordinateFrame): position: Coordinate frame object ..math:: - R_G = \left(\frac{\sum_{i=1}^{n} m_i |\vec{r_i} - \vec{r_{COM}}|^2 }{\sum_{i=1}^{n} m_i } + R_G = \left(\frac{\sum_{i=1}^{n} m_i |\vec{r_i} + - \vec{r_{COM}}|^2 }{\sum_{i=1}^{n} m_i } \rigth)^{\frac{1}{2}} """ gyration_radii = np.array([]) @@ -207,7 +208,7 @@ def layer_of_atoms( thickness: float, plane_normal: npt.ArrayLike, plane_offset: Optional[npt.ArrayLike] = np.array([0, 0, 0]), -): +) -> np.array: if plane_offset is None: np.array([0, 0, 0]) atoms = atoms - plane_offset @@ -215,45 +216,14 @@ def layer_of_atoms( return np.abs(distance) <= thickness -def distance_to_atoms(ref, atoms, box=None): - """Get the minimal distance from atoms to ref. - The result is an array of with length == len(atoms) - """ - out = np.empty(atoms.shape[0]) - for i, atom in enumerate(atoms): - diff = (pbc_diff(atom, ref, box) ** 2).sum(axis=1).min() - out[i] = np.sqrt(diff) - return out - - -def distance_to_atoms_KDtree(ref, atoms, box=None, thickness=None): - """ - Get the minimal distance from atoms to ref. - The result is an array of with length == len(atoms) - Can be faster than distance_to_atoms. - Thickness defaults to box/5. If this is too small results may be wrong. - If box is not given then periodic boundary conditions are not applied! - """ - if thickness == None: - thickness = box / 5 - if box is not None: - start_coords = np.copy(atoms) % box - all_frame_coords = pbc_points(ref, box, thickness=thickness) - else: - start_coords = atoms - all_frame_coords = ref - - tree = KDTree(all_frame_coords) - return tree.query(start_coords)[0] - - def next_neighbors( - atoms, - query_atoms=None, - number_of_neighbors=1, - distance_upper_bound=np.inf, - distinct=False, -): + atoms: CoordinateFrame, + query_atoms: Optional[CoordinateFrame] = None, + number_of_neighbors: int = 1, + distance_upper_bound: float = np.inf, + distinct: bool = False, + **kwargs +) -> (np.ndarray, np.ndarray): """ Find the N next neighbors of a set of atoms. @@ -269,15 +239,29 @@ def next_neighbors( If this is true, the atoms and query atoms are taken as distinct sets of atoms """ - tree = KDTree(atoms) dnn = 0 if query_atoms is None: query_atoms = atoms + dnn = 1 elif not distinct: dnn = 1 - dist, indices = tree.query( - query_atoms, - number_of_neighbors + dnn, - distance_upper_bound=distance_upper_bound, - ) - return indices[:, dnn:] + + box = atoms.box + if np.all(np.diag(np.diag(box)) == box): + atoms = atoms % np.diag(box) + tree = KDTree(atoms, boxsize=np.diag(box)) + distances, indices = tree.query( + query_atoms, number_of_neighbors, distance_upper_bound=distance_upper_bound + ) + else: + atoms_pbc, atoms_pbc_index = pbc_points( + query_atoms, box, thickness=distance_upper_bound+0.1, index=True, **kwargs + ) + tree = KDTree(atoms_pbc) + distances, indices = tree.query( + query_atoms, number_of_neighbors, distance_upper_bound=distance_upper_bound + ) + indices = atoms_pbc_index[indices] + + return distances[:, dnn:], indices[:, dnn:] + From 2615798088a22f4af4aa839b42e6e2d723c66d9f Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:07:30 +0100 Subject: [PATCH 26/73] Added dnn to next_neighbors --- src/mdevaluate/atoms.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/mdevaluate/atoms.py b/src/mdevaluate/atoms.py index 22582a4..77bebac 100644 --- a/src/mdevaluate/atoms.py +++ b/src/mdevaluate/atoms.py @@ -251,17 +251,20 @@ def next_neighbors( atoms = atoms % np.diag(box) tree = KDTree(atoms, boxsize=np.diag(box)) distances, indices = tree.query( - query_atoms, number_of_neighbors, distance_upper_bound=distance_upper_bound + query_atoms, + number_of_neighbors + dnn, + distance_upper_bound=distance_upper_bound, ) else: atoms_pbc, atoms_pbc_index = pbc_points( - query_atoms, box, thickness=distance_upper_bound+0.1, index=True, **kwargs + query_atoms, box, thickness=distance_upper_bound + 0.1, index=True, **kwargs ) tree = KDTree(atoms_pbc) distances, indices = tree.query( - query_atoms, number_of_neighbors, distance_upper_bound=distance_upper_bound + query_atoms, + number_of_neighbors + dnn, + distance_upper_bound=distance_upper_bound, ) indices = atoms_pbc_index[indices] return distances[:, dnn:], indices[:, dnn:] - From aca9ba07234afa473f65216759679aed565a509e Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:29:21 +0100 Subject: [PATCH 27/73] Updated doc strings and added type hints --- src/mdevaluate/autosave.py | 31 ++++++++++++++++++------------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/src/mdevaluate/autosave.py b/src/mdevaluate/autosave.py index ac8edf4..307c0b5 100644 --- a/src/mdevaluate/autosave.py +++ b/src/mdevaluate/autosave.py @@ -1,31 +1,33 @@ import os -import numpy as np import functools import inspect +from typing import Optional, Callable +import numpy as np from .checksum import checksum from .logging import logger -autosave_directory = None +autosave_directory: Optional[str] = None load_autosave_data = False verbose_print = True user_autosave_directory = os.path.join(os.environ["HOME"], ".mdevaluate/autosave") -def notify(msg): +def notify(msg: str): if verbose_print: logger.info(msg) else: logger.debug(msg) -def enable(dir, load_data=True, verbose=True): +def enable(dir: str, load_data: bool = True, verbose: bool = True): """ - Enable auto saving results of functions decorated with :func:`autosave_data`. + Enable auto saving results of functions decorated with: func: `autosave_data`. Args: dir: Directory where the data should be saved. load_data (opt., bool): If data should also be loaded. + verbose (opt., bool): If autosave should be verbose. """ global autosave_directory, load_autosave_data, verbose_print verbose_print = verbose @@ -79,9 +81,8 @@ def get_directory(reader): if not os.access(savedir, os.W_OK): savedir = os.path.join(user_autosave_directory, savedir.lstrip("/")) logger.info( - "Switched autosave directory to {}, since original location is not writeable.".format( - savedir - ) + "Switched autosave directory to {}, " + "since original location is not writeable.".format(savedir) ) os.makedirs(savedir, exist_ok=True) return savedir @@ -140,20 +141,24 @@ def load_data(filename): return data -def autosave_data(nargs, kwargs_keys=None, version=None): +def autosave_data( + nargs: int, kwargs_keys: Optional[list[str]] = None, version: Optional[int] = None +) -> Callable: """ Enable autosaving of results for a function. Args: nargs: Number of args which are relevant for the calculation. - kwargs_keys (opt.): List of keyword arguments which are relevant for the calculation. + kwargs_keys (opt.): + List of keyword arguments which are relevant for the calculation. version (opt.): - An optional version number of the decorated function, which replaces the checksum of - the function code, hence the checksum does not depend on the function code. + An optional version number of the decorated function, which replaces the + checksum of the function code, hence the checksum does not depend on the + function code. """ def decorator_function(function): - # make sure too include names of positional arguments in kwargs_keys, + # make sure to include names of positional arguments in kwargs_keys, # sice otherwise they will be ignored if passed via keyword. # nonlocal kwargs_keys posargs_keys = list(inspect.signature(function).parameters)[:nargs] From 8bad2861faf30644afe52169cc8b940b510eabd1 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:33:59 +0100 Subject: [PATCH 28/73] Updated doc strings and added type hints --- src/mdevaluate/checksum.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/src/mdevaluate/checksum.py b/src/mdevaluate/checksum.py index a10ad3f..8a234eb 100755 --- a/src/mdevaluate/checksum.py +++ b/src/mdevaluate/checksum.py @@ -3,6 +3,7 @@ import hashlib from .logging import logger from types import ModuleType, FunctionType import inspect +from typing import Iterable import numpy as np @@ -11,7 +12,7 @@ import numpy as np SALT = 42 -def version(version_nr, calls=[]): +def version(version_nr: int, calls: Iterable = ()): """Function decorator that assigns a custom checksum to a function.""" def decorator(func): @@ -27,7 +28,7 @@ def version(version_nr, calls=[]): return decorator -def strip_comments(s): +def strip_comments(s: str): """Strips comment lines and docstring from Python source string.""" o = "" in_docstring = False @@ -43,14 +44,15 @@ def checksum(*args, csum=None): """ Calculate a checksum of any object, by sha1 hash. - Input for the hash are some salt bytes and the byte encoding of a string + Inputs for the hash are some salt bytes and the byte encoding of a string that depends on the object and its type: - - If a method __checksum__ is available, it's return value is converted to bytes + - If a method __checksum__ is available, its return value is converted to bytes - str or bytes are used as sha1 input directly - modules use the __name__ attribute - functions use the function code and any closures of the function - - functools.partial uses the checksum of the function and any arguments, that were defined + - functools.partial uses the checksum of the function and any arguments, that were + defined - numpy.ndarray uses bytes representation of the array (arr.tobytes()) - Anything else is converted to a str """ From c00fc78f2360ca53d8ba602c0233a89539e0de85 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 11:50:16 +0100 Subject: [PATCH 29/73] Added type hints, moved imports and classes around --- src/mdevaluate/reader.py | 155 ++++++++++++++++++++------------------- 1 file changed, 79 insertions(+), 76 deletions(-) diff --git a/src/mdevaluate/reader.py b/src/mdevaluate/reader.py index 7152fc1..d4c4ae5 100755 --- a/src/mdevaluate/reader.py +++ b/src/mdevaluate/reader.py @@ -2,13 +2,8 @@ Module that provides different readers for trajectory files. It also provides a common interface layer between the file IO packages, -namely pygmx and mdanalysis, and mdevaluate. +namely mdanalysis, and mdevaluate. """ -from .checksum import checksum -from .logging import logger -from . import atoms - -from functools import lru_cache from collections import namedtuple import os from os import path @@ -19,9 +14,19 @@ import re import itertools import numpy as np -import MDAnalysis as mdanalysis +import numpy.typing as npt +import MDAnalysis from scipy import sparse +from .checksum import checksum +from .logging import logger +from . import atoms +from .coordinates import Coordinates + +CSR_ATTRS = ("data", "indices", "indptr") +NOJUMP_MAGIC = 2016 +Group_RE = re.compile("\[ ([-+\w]+) \]") + class NojumpError(Exception): pass @@ -31,11 +36,49 @@ class WrongTopologyError(Exception): pass +class BaseReader: + """Base class for trajectory readers.""" + + @property + def filename(self): + return self.rd.filename + + @property + def nojump_matrices(self): + if self._nojump_matrices is None: + raise NojumpError("Nojump Data not available: {}".format(self.filename)) + return self._nojump_matrices + + @nojump_matrices.setter + def nojump_matrices(self, mats): + self._nojump_matrices = mats + + def __init__(self, rd): + self.rd = rd + self._nojump_matrices = None + if path.exists(nojump_load_filename(self)): + load_nojump_matrices(self) + + def __getitem__(self, item): + return self.rd[item] + + def __len__(self): + return len(self.rd) + + def __checksum__(self): + cache = array("L", self.rd._xdr.offsets.tobytes()) + return checksum(self.filename, str(cache)) + + def open_with_mdanalysis( - topology, trajectory, index_file=None, charges=None, masses=None -): + topology: str, + trajectory: str, + index_file: str = None, + charges: npt.ArrayLike = None, + masses: npt.ArrayLike = None, +) -> (atoms.Atoms, BaseReader): """Open the topology and trajectory with mdanalysis.""" - uni = mdanalysis.Universe(topology, trajectory, convert_units=False) + uni = MDAnalysis.Universe(topology, trajectory, convert_units=False) reader = BaseReader(uni.trajectory) reader.universe = uni if topology.endswith(".tpr"): @@ -60,15 +103,12 @@ def open_with_mdanalysis( return atms, reader -group_re = re.compile("\[ ([-+\w]+) \]") - - -def load_indices(index_file): +def load_indices(index_file: str): indices = {} index_array = None with open(index_file) as idx_file: for line in idx_file: - m = group_re.search(line) + m = Group_RE.search(line) if m is not None: group_name = m.group(1) index_array = indices.get(group_name, []) @@ -82,7 +122,7 @@ def load_indices(index_file): return indices -def is_writeable(fname): +def is_writeable(fname: str): """Test if a directory is actually writeable, by writing a temporary file.""" fdir = os.path.dirname(fname) ftmp = os.path.join(fdir, str(np.random.randint(999999999))) @@ -100,7 +140,7 @@ def is_writeable(fname): return False -def nojump_load_filename(reader): +def nojump_load_filename(reader: BaseReader): directory, fname = path.split(reader.filename) full_path = path.join(directory, ".{}.nojump.npz".format(fname)) if not is_writeable(directory): @@ -116,7 +156,7 @@ def nojump_load_filename(reader): return full_path else: user_data_dir = os.path.join("/data/", os.environ["HOME"].split("/")[-1]) - full_path_fallback = os.path.join( + full_path = os.path.join( os.path.join(user_data_dir, ".mdevaluate/nojump"), directory.lstrip("/"), ".{}.nojump.npz".format(fname), @@ -124,7 +164,7 @@ def nojump_load_filename(reader): return full_path -def nojump_save_filename(reader): +def nojump_save_filename(reader: BaseReader): directory, fname = path.split(reader.filename) full_path = path.join(directory, ".{}.nojump.npz".format(fname)) if is_writeable(directory): @@ -145,11 +185,7 @@ def nojump_save_filename(reader): return full_path_fallback -CSR_ATTRS = ("data", "indices", "indptr") -NOJUMP_MAGIC = 2016 - - -def parse_jumps(trajectory): +def parse_jumps(trajectory: Coordinates): prev = trajectory[0].whole box = prev.box.diagonal() SparseData = namedtuple("SparseData", ["data", "row", "col"]) @@ -173,28 +209,28 @@ def parse_jumps(trajectory): return jump_data -def generate_nojump_matrixes(trajectory): +def generate_nojump_matrices(trajectory: Coordinates): """ - Create the matrixes with pbc jumps for a trajectory. + Create the matrices with pbc jumps for a trajectory. """ - logger.info("generate Nojump Matrixes for: {}".format(trajectory)) + logger.info("generate Nojump matrices for: {}".format(trajectory)) jump_data = parse_jumps(trajectory) N = len(trajectory) M = len(trajectory[0]) - trajectory.frames.nojump_matrixes = tuple( + trajectory.frames.nojump_matrices = tuple( sparse.csr_matrix((np.array(m.data), (m.row, m.col)), shape=(N, M)) for m in jump_data ) - save_nojump_matrixes(trajectory.frames) + save_nojump_matrices(trajectory.frames) -def save_nojump_matrixes(reader, matrixes=None): - if matrixes is None: - matrixes = reader.nojump_matrixes +def save_nojump_matrices(reader: BaseReader, matrices: npt.ArrayLike = None): + if matrices is None: + matrices = reader.nojump_matrices data = {"checksum": checksum(NOJUMP_MAGIC, checksum(reader))} - for d, mat in enumerate(matrixes): + for d, mat in enumerate(matrices): data["shape"] = mat.shape for attr in CSR_ATTRS: data["{}_{}".format(attr, d)] = getattr(mat, attr) @@ -202,18 +238,19 @@ def save_nojump_matrixes(reader, matrixes=None): np.savez(nojump_save_filename(reader), **data) -def load_nojump_matrixes(reader): +def load_nojump_matrices(reader: BaseReader): zipname = nojump_load_filename(reader) try: data = np.load(zipname, allow_pickle=True) except (AttributeError, BadZipFile, OSError): - # npz-files can be corrupted, propably a bug for big arrays saved with savez_compressed? + # npz-files can be corrupted, probably a bug for big arrays saved with + # savez_compressed? logger.info("Removing zip-File: %s", zipname) os.remove(nojump_load_filename(reader)) return try: if data["checksum"] == checksum(NOJUMP_MAGIC, checksum(reader)): - reader.nojump_matrixes = tuple( + reader.nojump_matrices = tuple( sparse.csr_matrix( tuple(data["{}_{}".format(attr, d)] for attr in CSR_ATTRS), shape=data["shape"], @@ -221,7 +258,7 @@ def load_nojump_matrixes(reader): for d in range(3) ) logger.info( - "Loaded Nojump Matrixes: {}".format(nojump_load_filename(reader)) + "Loaded Nojump matrices: {}".format(nojump_load_filename(reader)) ) else: logger.info("Invlaid Nojump Data: {}".format(nojump_load_filename(reader))) @@ -231,53 +268,19 @@ def load_nojump_matrixes(reader): return -def correct_nojump_matrixes_for_whole(trajectory): +def correct_nojump_matrices_for_whole(trajectory: Coordinates): reader = trajectory.frames frame = trajectory[0] box = frame.box.diagonal() cor = ((frame - frame.whole) / box).round().astype(np.int8) for d in range(3): - reader.nojump_matrixes[d][0] = cor[:, d] - save_nojump_matrixes(reader) + reader.nojump_matrices[d][0] = cor[:, d] + save_nojump_matrices(reader) -def energy_reader(file): +def energy_reader(file: str): """Reads a gromacs energy file with mdanalysis and returns an auxiliary file. Args: file: Filename of the energy file """ - return mdanalysis.auxiliary.EDR.EDRReader(file) - - -class BaseReader: - """Base class for trajectory readers.""" - - @property - def filename(self): - return self.rd.filename - - @property - def nojump_matrixes(self): - if self._nojump_matrixes is None: - raise NojumpError("Nojump Data not available: {}".format(self.filename)) - return self._nojump_matrixes - - @nojump_matrixes.setter - def nojump_matrixes(self, mats): - self._nojump_matrixes = mats - - def __init__(self, rd): - self.rd = rd - self._nojump_matrixes = None - if path.exists(nojump_load_filename(self)): - load_nojump_matrixes(self) - - def __getitem__(self, item): - return self.rd[item] - - def __len__(self): - return len(self.rd) - - def __checksum__(self): - cache = array("L", self.rd._xdr.offsets.tobytes()) - return checksum(self.filename, str(cache)) + return MDAnalysis.auxiliary.EDR.EDRReader(file) From eed94cfca66a5c093ff30da6b089fbb44d3a8313 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 12:55:53 +0100 Subject: [PATCH 30/73] Added type hints, adjusted imports, and doc strings --- src/mdevaluate/coordinates.py | 287 +++++++++++++++++----------------- 1 file changed, 141 insertions(+), 146 deletions(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 519c6df..083a16a 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -1,13 +1,14 @@ -from functools import partial, lru_cache, wraps +from functools import partial, wraps from copy import copy from .logging import logger +from typing import Optional, Callable import numpy as np -from scipy.spatial import cKDTree, KDTree +import numpy.typing as npt from .atoms import AtomSubset from .pbc import whole, nojump, pbc_diff -from .utils import mask2indices, singledispatchmethod +from .utils import singledispatchmethod from .checksum import checksum @@ -15,104 +16,6 @@ class UnknownCoordinatesMode(Exception): pass -def rotate_axis(coords, axis): - """ - Rotate a set of coordinates to a given axis. - """ - axis = np.array(axis) / np.linalg.norm(axis) - zaxis = np.array([0, 0, 1]) - if (axis == zaxis).sum() == 3: - return coords - rotation_axis = np.cross(axis, zaxis) - rotation_axis = rotation_axis / np.linalg.norm(rotation_axis) - - theta = np.arccos(axis @ zaxis / np.linalg.norm(axis)) - - # return theta/pi, rotation_axis - - ux, uy, uz = rotation_axis - cross_matrix = np.array([[0, -uz, uy], [uz, 0, -ux], [-uy, ux, 0]]) - rotation_matrix = ( - np.cos(theta) * np.identity(len(axis)) - + (1 - np.cos(theta)) - * rotation_axis.reshape(-1, 1) - @ rotation_axis.reshape(1, -1) - + np.sin(theta) * cross_matrix - ) - - if len(coords.shape) == 2: - rotated = np.array([rotation_matrix @ xyz for xyz in coords]) - else: - rotated = rotation_matrix @ coords - return rotated - - -def spherical_radius(frame, origin=None): - """ - Transform a frame of cartesian coordinates into the sperical radius. - If origin=None the center of the box is taken as the coordinates origin. - """ - if origin is None: - origin = frame.box.diagonal() / 2 - return ((frame - origin) ** 2).sum(axis=-1) ** 0.5 - - -def polar_coordinates(x, y): - """Convert cartesian to polar coordinates.""" - radius = (x**2 + y**2) ** 0.5 - phi = np.arctan2(y, x) - return radius, phi - - -def spherical_coordinates(x, y, z): - """Convert cartesian to spherical coordinates.""" - xy, phi = polar_coordinates(x, y) - radius = (x**2 + y**2 + z**2) ** 0.5 - theta = np.arccos(z / radius) - return radius, phi, theta - - -def radial_selector(frame, coordinates, rmin, rmax): - """ - Return a selection of all atoms with radius in the interval [rmin, rmax]. - """ - crd = coordinates[frame.step] - rad, _ = polar_coordinates(crd[:, 0], crd[:, 1]) - selector = (rad >= rmin) & (rad <= rmax) - return mask2indices(selector) - - -def selector_radial_cylindrical(atoms, r_min, r_max, origin=None): - box = atoms.box - atoms = atoms % np.diag(box) - if origin is None: - origin = [box[0, 0] / 2, box[1, 1] / 2, box[2, 2] / 2] - r_vec = (atoms - origin)[:, :2] - r = np.linalg.norm(r_vec, axis=1) - index = np.argwhere((r >= r_min) * (r < r_max)) - return index.flatten() - - -def spatial_selector(frame, transform, rmin, rmax): - """ - Select a subset of atoms which have a radius between rmin and rmax. - Coordinates are filtered by the condition:: - - rmin <= transform(frame) <= rmax - - Args: - frame: The coordinates of the actual trajectory - transform: - A function that transforms the coordinates of the frames into - the one-dimensional spatial coordinate (e.g. radius). - rmin: Minimum value of the radius - rmax: Maximum value of the radius - """ - r = transform(frame) - selector = (rmin <= r) & (rmax >= r) - return mask2indices(selector) - - class CoordinateFrame(np.ndarray): _known_modes = ("pbc", "whole", "nojump") @@ -250,7 +153,8 @@ class Coordinates: def mode(self, val): if val in CoordinateFrame._known_modes: logger.warn( - "Changing the Coordinates mode directly is deprecated. Use Coordinates.%s instead, which returns a copy.", + "Changing the Coordinates mode directly is deprecated. " + "Use Coordinates.%s instead, which returns a copy.", val, ) self._mode = val @@ -414,30 +318,111 @@ class CoordinatesMap: return CoordinatesMap(self.coordinates.pbc, self.function) -def map_coordinates(func): +def rotate_axis(coords: npt.ArrayLike, axis: npt.ArrayLike) -> np.ndarray: + """ + Rotate a set of coordinates to a given axis. + """ + axis = np.array(axis) / np.linalg.norm(axis) + zaxis = np.array([0, 0, 1]) + if (axis == zaxis).sum() == 3: + return coords + rotation_axis = np.cross(axis, zaxis) + rotation_axis = rotation_axis / np.linalg.norm(rotation_axis) + + theta = np.arccos(axis @ zaxis / np.linalg.norm(axis)) + + # return theta/pi, rotation_axis + + ux, uy, uz = rotation_axis + cross_matrix = np.array([[0, -uz, uy], [uz, 0, -ux], [-uy, ux, 0]]) + rotation_matrix = ( + np.cos(theta) * np.identity(len(axis)) + + (1 - np.cos(theta)) + * rotation_axis.reshape(-1, 1) + @ rotation_axis.reshape(1, -1) + + np.sin(theta) * cross_matrix + ) + + if len(coords.shape) == 2: + rotated = np.array([rotation_matrix @ xyz for xyz in coords]) + else: + rotated = rotation_matrix @ coords + return rotated + + +def spherical_radius( + frame: CoordinateFrame, origin: Optional[npt.ArrayLike] = None +) -> np.ndarray: + """ + Transform a frame of cartesian coordinates into the spherical radius. + If origin=None, the center of the box is taken as the coordinates' origin. + """ + if origin is None: + origin = frame.box.diagonal() / 2 + return ((frame - origin) ** 2).sum(axis=-1) ** 0.5 + + +def polar_coordinates(x: npt.ArrayLike, y: npt.ArrayLike) -> (np.ndarray, np.ndarray): + """Convert cartesian to polar coordinates.""" + radius = (x**2 + y**2) ** 0.5 + phi = np.arctan2(y, x) + return radius, phi + + +def spherical_coordinates( + x: npt.ArrayLike, y: npt.ArrayLike, z: npt.ArrayLike +) -> (np.ndarray, np.ndarray, np.ndarray): + """Convert cartesian to spherical coordinates.""" + xy, phi = polar_coordinates(x, y) + radius = (x**2 + y**2 + z**2) ** 0.5 + theta = np.arccos(z / radius) + return radius, phi, theta + + +def selector_radial_cylindrical( + atoms: CoordinateFrame, + r_min: float, + r_max: float, + origin: Optional[npt.ArrayLike] = None, +) -> np.ndarray: + box = atoms.box + atoms = atoms % np.diag(box) + if origin is None: + origin = [box[0, 0] / 2, box[1, 1] / 2, box[2, 2] / 2] + r_vec = (atoms - origin)[:, :2] + r = np.linalg.norm(r_vec, axis=1) + index = np.argwhere((r >= r_min) * (r < r_max)) + return index.flatten() + + +def map_coordinates( + func: Callable[[CoordinateFrame, ...], np.ndarray] +) -> Callable[..., CoordinatesMap]: @wraps(func) - def wrapped(coordinates, **kwargs): + def wrapped(coordinates: Coordinates, **kwargs) -> CoordinatesMap: return CoordinatesMap(coordinates, partial(func, **kwargs)) return wrapped @map_coordinates -def center_of_masses(coordinates, atoms=None, shear: bool = False): +def center_of_masses( + frame: CoordinateFrame, atoms=None, shear: bool = False +) -> np.ndarray: if atoms is None: - atoms = list(range(len(coordinates))) - res_ids = coordinates.residue_ids[atoms] - masses = coordinates.masses[atoms] + atoms = list(range(len(frame))) + res_ids = frame.residue_ids[atoms] + masses = frame.masses[atoms] if shear: - coords = coordinates[atoms] - box = coords.box + coords = frame[atoms] + box = frame.box sort_ind = res_ids.argsort(kind="stable") i = np.concatenate([[0], np.where(np.diff(res_ids[sort_ind]) > 0)[0] + 1]) coms = coords[sort_ind[i]][res_ids - min(res_ids)] cor = pbc_diff(coords, coms, box) coords = coms + cor else: - coords = coordinates.whole[atoms] + coords = frame.whole[atoms] mask = np.bincount(res_ids)[1:] != 0 positions = np.array( [ @@ -450,12 +435,14 @@ def center_of_masses(coordinates, atoms=None, shear: bool = False): @map_coordinates -def pore_coordinates(coordinates, origin, sym_axis="z"): +def pore_coordinates( + frame: CoordinateFrame, origin: npt.ArrayLike, sym_axis: str = "z" +) -> np.ndarray: """ Map coordinates of a pore simulation so the pore has cylindrical symmetry. Args: - coordinates: Coordinates of the simulation + frame: Coordinates of the simulation origin: Origin of the pore which will be the coordinates origin after mapping sym_axis (opt.): Symmtery axis of the pore, may be a literal direction 'x', 'y' or 'z' or an array of shape (3,) @@ -465,30 +452,34 @@ def pore_coordinates(coordinates, origin, sym_axis="z"): rot_axis[["x", "y", "z"].index(sym_axis)] = 1 else: rot_axis = sym_axis - - return rotate_axis(coordinates - origin, rot_axis) + return rotate_axis(frame - origin, rot_axis) @map_coordinates -def vectors(coordinates, atoms_a, atoms_b, normed=False): +def vectors( + frame: CoordinateFrame, + atoms_indices_a: npt.ArrayLike[int], + atoms_indices_b: npt.ArrayLike[int], + normed: bool = False, +) -> np.ndarray: """ Compute the vectors between the atoms of two subsets. Args: - coordinates: The Coordinates object the atoms will be taken from - atoms_a: Mask or indices of the first atom subset - atoms_b: Mask or indices of the second atom subset + frame: The Coordinates object the atoms will be taken from + atoms_indices_a: Mask or indices of the first atom subset + atoms_indices_b: Mask or indices of the second atom subset normed (opt.): If the vectors should be normed - box (opt.): If not None, the vectors are calcualte with PBC - The defintion of atoms_a/b can be any possible subript of a numpy array. + The definition of atoms_a/b can be any possible subript of a numpy array. They can, for example, be given as a masking array of bool values with the - same length as the frames of the coordinates. Or they can be a list of - indices selecting the atoms of these indices from each frame. + same length as the frames of the coordinates. + Or there can be a list of indices selecting the atoms of these indices from each + frame. - It is possible to compute the mean of several atoms before calculating the vectors, + It is possible to compute the means of several atoms before calculating the vectors, by using a two-dimensional list of indices. The following code computes the vectors - between atoms 0, 3, 6 and the mean coordinate of atoms 1, 4, 7 and 2, 5, 8:: + between atoms 0, 3, 6 and the mean coordinate of atoms 1, 4, 7 and 2, 5, 8: >>> inds_a = [0, 3, 6] >>> inds_b = [[1, 4, 7], [2, 5, 8]] @@ -499,11 +490,11 @@ def vectors(coordinates, atoms_a, atoms_b, normed=False): coords[6] - (coords[7] + coords[8])/2, ]) """ - box = coordinates.box - coords_a = coordinates[atoms_a] + box = frame.box + coords_a = frame[atoms_indices_a] if len(coords_a.shape) > 2: coords_a = coords_a.mean(axis=0) - coords_b = coordinates[atoms_b] + coords_b = frame[atoms_indices_b] if len(coords_b.shape) > 2: coords_b = coords_b.mean(axis=0) vec = pbc_diff(coords_a, coords_b, box=box) @@ -514,10 +505,12 @@ def vectors(coordinates, atoms_a, atoms_b, normed=False): @map_coordinates -def dipole_vector(coordinates, atoms, normed=None): - coords = coordinates.whole[atoms] - res_ids = coordinates.residue_ids[atoms] - charges = coordinates.charges[atoms] +def dipole_vector( + frame: CoordinateFrame, atom_indices: npt.ArrayLike[int], normed: bool = None +) -> np.ndarray: + coords = frame.whole[atom_indices] + res_ids = frame.residue_ids[atom_indices] + charges = frame.charges[atom_indices] mask = np.bincount(res_ids)[1:] != 0 dipoles = np.array( [np.bincount(res_ids, weights=c * charges)[1:] for c in coords.T] @@ -531,11 +524,11 @@ def dipole_vector(coordinates, atoms, normed=None): @map_coordinates def sum_dipole_vector( coordinates: CoordinateFrame, - atoms: list[int], + atom_indices: npt.ArrayLike[int], normed: bool = True, -): - coords = coordinates.whole[atoms] - charges = coordinates.charges[atoms] +) -> np.ndarray: + coords = coordinates.whole[atom_indices] + charges = coordinates.charges[atom_indices] dipole = np.array([c * charges for c in coords.T]).T if normed: dipole /= np.linalg.norm(dipole) @@ -544,16 +537,16 @@ def sum_dipole_vector( @map_coordinates def normal_vectors( - coordinates: CoordinateFrame, - atoms_a: list[int], - atoms_b: list[int], - atoms_c: list[int], + frame: CoordinateFrame, + atom_indices_a: npt.ArrayLike[int], + atom_indices_b: npt.ArrayLike[int], + atom_indices_c: npt.ArrayLike[int], normed: bool = True, -): - coords_a = coordinates[atoms_a] - coords_b = coordinates[atoms_b] - coords_c = coordinates[atoms_c] - box = coordinates.box +) -> np.ndarray: + coords_a = frame[atom_indices_a] + coords_b = frame[atom_indices_b] + coords_c = frame[atom_indices_c] + box = frame.box vectors_a = pbc_diff(coords_a, coords_b, box=box) vectors_b = pbc_diff(coords_a, coords_c, box=box) vec = np.cross(vectors_a, vectors_b) @@ -576,7 +569,9 @@ def displacements_without_drift( @map_coordinates -def cylindrical_coordinates(frame, origin=None): +def cylindrical_coordinates( + frame: CoordinateFrame, origin: npt.ArrayLike = None +) -> np.ndarray: if origin is None: origin = np.diag(frame.box) / 2 x = frame[:, 0] - origin[0] From cc185546d6afc93e89af460acd07e8fb1f53eafb Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 15:24:01 +0100 Subject: [PATCH 31/73] Small changes to type hints --- src/mdevaluate/autosave.py | 4 ++-- src/mdevaluate/distribution.py | 8 ++++++-- src/mdevaluate/utils.py | 19 ------------------- 3 files changed, 8 insertions(+), 23 deletions(-) diff --git a/src/mdevaluate/autosave.py b/src/mdevaluate/autosave.py index 307c0b5..9666d5e 100644 --- a/src/mdevaluate/autosave.py +++ b/src/mdevaluate/autosave.py @@ -1,7 +1,7 @@ import os import functools import inspect -from typing import Optional, Callable +from typing import Optional, Callable, Iterable import numpy as np from .checksum import checksum @@ -142,7 +142,7 @@ def load_data(filename): def autosave_data( - nargs: int, kwargs_keys: Optional[list[str]] = None, version: Optional[int] = None + nargs: int, kwargs_keys: Optional[Iterable[str]] = None, version: Optional[int] = None ) -> Callable: """ Enable autosaving of results for a function. diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index e57eb37..01d73eb 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -198,7 +198,11 @@ def distance_distribution(atoms, bins): def tetrahedral_order(atoms, reference_atoms=None): if reference_atoms is None: reference_atoms = atoms - indices = next_neighbors(reference_atoms, query_atoms=atoms, number_of_neighbors=4) + indices = next_neighbors( + reference_atoms, + query_atoms=atoms, + number_of_neighbors=4, + )[1] neighbors = reference_atoms[indices] neighbors_1, neighbors_2, neighbors_3, neighbors_4 = ( neighbors[:, 0, :], @@ -350,7 +354,7 @@ def next_neighbor_distribution( reference = atoms nn = next_neighbors( reference, query_atoms=atoms, number_of_neighbors=number_of_neighbors - ) + )[1] resname_nn = reference.residue_names[nn] count_nn = (resname_nn == atoms.residue_names.reshape(-1, 1)).sum(axis=1) return np.histogram(count_nn, bins=bins, normed=normed)[0] diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 4fc47fe..38334db 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -119,25 +119,6 @@ def filon_fourier_transformation( ) -def mask2indices(mask): - """ - Return the selected indices of an array mask. - If the mask is two-dimensional, the indices will be calculated for the second axis. - - Example: - >>> mask2indices([True, False, True, False]) - array([0, 2]) - >>> mask2indices([[True, True, False], [True, False, True]]) - array([[0, 1], [0, 2]]) - """ - mask = np.array(mask) - if len(mask.shape) == 1: - indices = np.where(mask) - else: - indices = np.array([np.where(m) for m in mask]) - return indices - - def superpose(x1, y1, x2, y2, N=100, damping=1.0): if x2[0] == 0: x2 = x2[1:] From cedfd24bb33e0d1623fd139b68681fd1f6dbcedc Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 16:05:42 +0100 Subject: [PATCH 32/73] Updated regex test for atom_subsets --- test/test_atoms.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/test/test_atoms.py b/test/test_atoms.py index 498e926..ec7821f 100644 --- a/test/test_atoms.py +++ b/test/test_atoms.py @@ -2,6 +2,7 @@ from mdevaluate import atoms def test_compare_regex(): - assert atoms.compare_regex(['OW', ], 'O')[0] == False - assert atoms.compare_regex(['WO', ], 'O')[0] == False - assert atoms.compare_regex(['O', ], 'O')[0] == True + assert atoms.compare_regex(['OW', ], 'O')[0] + assert not atoms.compare_regex(['OW', ], 'O$')[0] + assert not atoms.compare_regex(['WO', ], 'O')[0] + assert atoms.compare_regex(['O', ], 'O')[0] From 476f7167b455289006e64aaa4540d9f48c58d9cd Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 16:06:38 +0100 Subject: [PATCH 33/73] Moved functions around to circumvent a circular import --- .gitignore | 1 + src/mdevaluate/atoms.py | 94 ---------------------------------- src/mdevaluate/coordinates.py | 84 +++++++++++++++++++++++++++--- src/mdevaluate/distribution.py | 36 ++++++++++++- 4 files changed, 111 insertions(+), 104 deletions(-) diff --git a/.gitignore b/.gitignore index abd3af2..6685b0c 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,4 @@ tmp/ .traj.xtc_offsets.lock .traj.xtc_offsets.npz *.npy +.venv/ diff --git a/src/mdevaluate/atoms.py b/src/mdevaluate/atoms.py index 77bebac..41eb99c 100644 --- a/src/mdevaluate/atoms.py +++ b/src/mdevaluate/atoms.py @@ -1,13 +1,8 @@ import re -from typing import Optional import numpy as np -import numpy.typing as npt -from scipy.spatial import KDTree -from .pbc import pbc_diff, pbc_points from .checksum import checksum -from .coordinates import CoordinateFrame def compare_regex(str_list: list[str], exp: str) -> np.ndarray: @@ -176,95 +171,6 @@ class AtomSubset: return checksum(self.description) -def gyration_radius(position: CoordinateFrame) -> np.ndarray: - r""" - Calculates a list of all radii of gyration of all molecules given in the coordinate - frame, weighted with the masses of the individual atoms. - - Args: - position: Coordinate frame object - - ..math:: - R_G = \left(\frac{\sum_{i=1}^{n} m_i |\vec{r_i} - - \vec{r_{COM}}|^2 }{\sum_{i=1}^{n} m_i } - \rigth)^{\frac{1}{2}} - """ - gyration_radii = np.array([]) - - for resid in np.unique(position.residue_ids): - pos = position.whole[position.residue_ids == resid] - mass = position.masses[position.residue_ids == resid][:, np.newaxis] - COM = 1 / mass.sum() * (mass * position).sum(axis=0) - r_sq = ((pbc_diff(pos, COM, pos.box.diagonal())) ** 2).sum(1)[:, np.newaxis] - g_radius = ((r_sq * mass).sum() / mass.sum()) ** 0.5 - - gyration_radii = np.append(gyration_radii, g_radius) - - return gyration_radii -def layer_of_atoms( - atoms: CoordinateFrame, - thickness: float, - plane_normal: npt.ArrayLike, - plane_offset: Optional[npt.ArrayLike] = np.array([0, 0, 0]), -) -> np.array: - if plane_offset is None: - np.array([0, 0, 0]) - atoms = atoms - plane_offset - distance = np.dot(atoms, plane_normal) - return np.abs(distance) <= thickness - -def next_neighbors( - atoms: CoordinateFrame, - query_atoms: Optional[CoordinateFrame] = None, - number_of_neighbors: int = 1, - distance_upper_bound: float = np.inf, - distinct: bool = False, - **kwargs -) -> (np.ndarray, np.ndarray): - """ - Find the N next neighbors of a set of atoms. - - Args: - atoms: - The reference atoms and also the atoms which are queried if `query_atoms` - is net provided - query_atoms (opt.): If this is not None, these atoms will be queried - number_of_neighbors (int, opt.): Number of neighboring atoms to find - distance_upper_bound (float, opt.): - Upper bound of the distance between neighbors - distinct (bool, opt.): - If this is true, the atoms and query atoms are taken as distinct sets of - atoms - """ - dnn = 0 - if query_atoms is None: - query_atoms = atoms - dnn = 1 - elif not distinct: - dnn = 1 - - box = atoms.box - if np.all(np.diag(np.diag(box)) == box): - atoms = atoms % np.diag(box) - tree = KDTree(atoms, boxsize=np.diag(box)) - distances, indices = tree.query( - query_atoms, - number_of_neighbors + dnn, - distance_upper_bound=distance_upper_bound, - ) - else: - atoms_pbc, atoms_pbc_index = pbc_points( - query_atoms, box, thickness=distance_upper_bound + 0.1, index=True, **kwargs - ) - tree = KDTree(atoms_pbc) - distances, indices = tree.query( - query_atoms, - number_of_neighbors + dnn, - distance_upper_bound=distance_upper_bound, - ) - indices = atoms_pbc_index[indices] - - return distances[:, dnn:], indices[:, dnn:] diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index 083a16a..bfeac00 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -5,9 +5,10 @@ from typing import Optional, Callable import numpy as np import numpy.typing as npt +from scipy.spatial import KDTree from .atoms import AtomSubset -from .pbc import whole, nojump, pbc_diff +from .pbc import whole, nojump, pbc_diff, pbc_points from .utils import singledispatchmethod from .checksum import checksum @@ -458,8 +459,8 @@ def pore_coordinates( @map_coordinates def vectors( frame: CoordinateFrame, - atoms_indices_a: npt.ArrayLike[int], - atoms_indices_b: npt.ArrayLike[int], + atoms_indices_a: npt.ArrayLike, + atoms_indices_b: npt.ArrayLike, normed: bool = False, ) -> np.ndarray: """ @@ -506,7 +507,7 @@ def vectors( @map_coordinates def dipole_vector( - frame: CoordinateFrame, atom_indices: npt.ArrayLike[int], normed: bool = None + frame: CoordinateFrame, atom_indices: npt.ArrayLike, normed: bool = None ) -> np.ndarray: coords = frame.whole[atom_indices] res_ids = frame.residue_ids[atom_indices] @@ -524,7 +525,7 @@ def dipole_vector( @map_coordinates def sum_dipole_vector( coordinates: CoordinateFrame, - atom_indices: npt.ArrayLike[int], + atom_indices: npt.ArrayLike, normed: bool = True, ) -> np.ndarray: coords = coordinates.whole[atom_indices] @@ -538,9 +539,9 @@ def sum_dipole_vector( @map_coordinates def normal_vectors( frame: CoordinateFrame, - atom_indices_a: npt.ArrayLike[int], - atom_indices_b: npt.ArrayLike[int], - atom_indices_c: npt.ArrayLike[int], + atom_indices_a: npt.ArrayLike, + atom_indices_b: npt.ArrayLike, + atom_indices_c: npt.ArrayLike, normed: bool = True, ) -> np.ndarray: coords_a = frame[atom_indices_a] @@ -580,3 +581,70 @@ def cylindrical_coordinates( radius = (x**2 + y**2) ** 0.5 phi = np.arctan2(y, x) return np.array([radius, phi, z]).T + + +def layer_of_atoms( + atoms: CoordinateFrame, + thickness: float, + plane_normal: npt.ArrayLike, + plane_offset: Optional[npt.ArrayLike] = np.array([0, 0, 0]), +) -> np.array: + if plane_offset is None: + np.array([0, 0, 0]) + atoms = atoms - plane_offset + distance = np.dot(atoms, plane_normal) + return np.abs(distance) <= thickness + + +def next_neighbors( + atoms: CoordinateFrame, + query_atoms: Optional[CoordinateFrame] = None, + number_of_neighbors: int = 1, + distance_upper_bound: float = np.inf, + distinct: bool = False, + **kwargs +) -> (np.ndarray, np.ndarray): + """ + Find the N next neighbors of a set of atoms. + + Args: + atoms: + The reference atoms and also the atoms which are queried if `query_atoms` + is net provided + query_atoms (opt.): If this is not None, these atoms will be queried + number_of_neighbors (int, opt.): Number of neighboring atoms to find + distance_upper_bound (float, opt.): + Upper bound of the distance between neighbors + distinct (bool, opt.): + If this is true, the atoms and query atoms are taken as distinct sets of + atoms + """ + dnn = 0 + if query_atoms is None: + query_atoms = atoms + dnn = 1 + elif not distinct: + dnn = 1 + + box = atoms.box + if np.all(np.diag(np.diag(box)) == box): + atoms = atoms % np.diag(box) + tree = KDTree(atoms, boxsize=np.diag(box)) + distances, indices = tree.query( + query_atoms, + number_of_neighbors + dnn, + distance_upper_bound=distance_upper_bound, + ) + else: + atoms_pbc, atoms_pbc_index = pbc_points( + query_atoms, box, thickness=distance_upper_bound + 0.1, index=True, **kwargs + ) + tree = KDTree(atoms_pbc) + distances, indices = tree.query( + query_atoms, + number_of_neighbors + dnn, + distance_upper_bound=distance_upper_bound, + ) + indices = atoms_pbc_index[indices] + + return distances[:, dnn:], indices[:, dnn:] diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index 01d73eb..d3d03ca 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -4,8 +4,13 @@ from scipy import spatial from scipy.spatial import KDTree from scipy.sparse.csgraph import connected_components -from .coordinates import rotate_axis, polar_coordinates, Coordinates, CoordinateFrame -from .atoms import next_neighbors +from .coordinates import ( + rotate_axis, + polar_coordinates, + Coordinates, + CoordinateFrame, + next_neighbors, +) from .autosave import autosave_data from .utils import runningmean from .pbc import pbc_diff, pbc_points @@ -455,3 +460,30 @@ def calc_cluster_sizes(frame, r_max=0.35): for i in range(0, np.max(labels) + 1): cluster_sizes.append(np.sum(labels == i)) return np.array(cluster_sizes).flatten() + + +def gyration_radius(position: CoordinateFrame) -> np.ndarray: + r""" + Calculates a list of all radii of gyration of all molecules given in the coordinate + frame, weighted with the masses of the individual atoms. + + Args: + position: Coordinate frame object + + ..math:: + R_G = \left(\frac{\sum_{i=1}^{n} m_i |\vec{r_i} + - \vec{r_{COM}}|^2 }{\sum_{i=1}^{n} m_i } + \rigth)^{\frac{1}{2}} + """ + gyration_radii = np.array([]) + + for resid in np.unique(position.residue_ids): + pos = position.whole[position.residue_ids == resid] + mass = position.masses[position.residue_ids == resid][:, np.newaxis] + COM = 1 / mass.sum() * (mass * position).sum(axis=0) + r_sq = ((pbc_diff(pos, COM, pos.box.diagonal())) ** 2).sum(1)[:, np.newaxis] + g_radius = ((r_sq * mass).sum() / mass.sum()) ** 0.5 + + gyration_radii = np.append(gyration_radii, g_radius) + + return gyration_radii From e91de717876cf5835c315cad062a1e3a221c61ca Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 16:38:55 +0100 Subject: [PATCH 34/73] Combined selector and multi_selector in shifted_correlation and added type hints --- src/mdevaluate/correlation.py | 145 +++++++++++++++++----------------- 1 file changed, 74 insertions(+), 71 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index c6a2cbd..edcd942 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -1,9 +1,8 @@ -from typing import Callable +from typing import Callable, Optional import numpy as np from numpy.typing import ArrayLike from scipy.special import legendre -from itertools import chain import dask.array as darray from functools import partial from scipy.spatial import KDTree @@ -14,29 +13,22 @@ from .pbc import pbc_diff, pbc_points from .coordinates import Coordinates, CoordinateFrame, displacements_without_drift -def log_indices(first, last, num=100): +def log_indices(first: int, last: int, num: int = 100) -> np.ndarray: ls = np.logspace(0, np.log10(last - first + 1), num=num) return np.unique(np.int_(ls) - 1 + first) -def correlation(function, frames): - iterator = iter(frames) - start_frame = next(iterator) - return map(lambda f: function(start_frame, f), chain([start_frame], iterator)) - - @autosave_data(2) def shifted_correlation( function: Callable, frames: Coordinates, selector: ArrayLike = None, - multi_selector: ArrayLike = None, segments: int = 10, skip: float = 0.1, window: float = 0.5, average: bool = True, points: int = 100, -): +) -> (np.ndarray, np.ndarray): """ Calculate the time series for a correlation function. @@ -44,18 +36,12 @@ def shifted_correlation( a logarithmic distribution. Args: - function: The function that should be correlated - frames: The coordinates of the simulation data + function: The function that should be correlated + frames: The coordinates of the simulation data selector (opt.): A function that returns the indices depending on the staring frame for which particles the correlation should be calculated. - Can not be used with multi_selector. - multi_selector (opt.): - A function that returns multiple lists of indices depending on - the staring frame for which particles the - correlation should be calculated. - Can not be used with selector. segments (int, opt.): The number of segments the time window will be shifted @@ -79,12 +65,18 @@ def shifted_correlation( that holds the (non-avaraged) correlation data Example: - Calculating the mean square displacement of a coordinates object named ``coords``: + Calculating the mean square displacement of a coordinate object + named ``coords``: >>> time, data = shifted_correlation(msd, coords) """ - def get_correlation(frames, start_frame, index, shifted_idx): + def get_correlation( + frames: CoordinateFrame, + start_frame: CoordinateFrame, + index: np.ndarray, + shifted_idx: np.ndarray, + ) -> np.ndarray: if len(index) == 0: correlation = np.zeros(len(shifted_idx)) else: @@ -94,29 +86,33 @@ def shifted_correlation( ) return correlation - def apply_selector(start_frame, frames, idx, selector=None, multi_selector=None): + def apply_selector( + start_frame: CoordinateFrame, + frames: CoordinateFrame, + idx: np.ndarray, + selector: Optional[Callable] = None, + ): shifted_idx = idx + start_frame - if selector is None and multi_selector is None: + + if selector is None: index = np.arange(len(frames[start_frame])) return get_correlation(frames, start_frame, index, shifted_idx) - - elif selector is not None and multi_selector is not None: - raise ValueError( - "selector and multi_selector can not be used at the same time" - ) - - elif selector is not None: + else: index = selector(frames[start_frame]) - return get_correlation(frames, start_frame, index, shifted_idx) - - elif multi_selector is not None: - indices = multi_selector(frames[start_frame]) - correlation = [] - for index in indices: - correlation.append( - get_correlation(frames, start_frame, index, shifted_idx) + if len(index.shape) == 1: + return get_correlation(frames, start_frame, index, shifted_idx) + elif len(index.shape) == 2: + correlations = [] + for ind in index: + correlations.append( + get_correlation(frames, start_frame, ind, shifted_idx) + ) + return correlations + else: + raise ValueError( + f"Index list of selector has {len(index.shape)} dimensions, " + "but should have 1 or 2" ) - return correlation if 1 - skip < window: window = 1 - skip @@ -138,13 +134,7 @@ def shifted_correlation( result = np.array( [ - apply_selector( - start_frame, - frames=frames, - idx=idx, - selector=selector, - multi_selector=multi_selector, - ) + apply_selector(start_frame, frames=frames, idx=idx, selector=selector) for start_frame in start_frames ] ) @@ -196,8 +186,6 @@ def isf( """ Incoherent intermediate scattering function. To specify q, use water_isf = functools.partial(isf, q=22.77) # q has the value 22.77 nm^-1 - - :param q: length of scattering vector """ if trajectory is None: displacements = start_frame - end_frame @@ -219,19 +207,21 @@ def isf( raise ValueError('Parameter axis has to be ether "all", "x", "y", or "z"!') -def rotational_autocorrelation(onset, frame, order=2): +def rotational_autocorrelation( + start_frame: CoordinateFrame, end_frame: CoordinateFrame, order: int = 2 +) -> float: """ Compute the rotational autocorrelation of the legendre polynomial for the given vectors. Args: - onset, frame: CoordinateFrames of vectors + start_frame, end_frame: CoordinateFrames of vectors order (opt.): Order of the legendre polynomial. Returns: Scalar value of the correlation function. """ - scalar_prod = (onset * frame).sum(axis=-1) + scalar_prod = (start_frame * end_frame).sum(axis=-1) poly = legendre(order) return poly(scalar_prod).mean() @@ -242,9 +232,9 @@ def van_hove_self( bins: ArrayLike, trajectory: Coordinates = None, axis: str = "all", -): +) -> np.ndarray: r""" - Compute the self part of the Van Hove autocorrelation function. + Compute the self-part of the Van Hove autocorrelation function. ..math:: G(r, t) = \sum_i \delta(|\vec r_i(0) - \vec r_i(t)| - r) @@ -269,8 +259,13 @@ def van_hove_self( def van_hove_distinct( - onset, frame, bins, box=None, use_dask=True, comp=False, bincount=True -): + start_frame: CoordinateFrame, + end_frame: CoordinateFrame, + bins: ArrayLike, + box: ArrayLike = None, + use_dask: bool = True, + comp: bool = False, +) -> np.ndarray: r""" Compute the distinct part of the Van Hove autocorrelation function. @@ -278,17 +273,19 @@ def van_hove_distinct( G(r, t) = \sum_{i, j} \delta(|\vec r_i(0) - \vec r_j(t)| - r) """ if box is None: - box = onset.box.diagonal() + box = start_frame.box.diagonal() dimension = len(box) - N = len(onset) + N = len(start_frame) if use_dask: - onset = darray.from_array(onset, chunks=(500, dimension)).reshape( + start_frame = darray.from_array(start_frame, chunks=(500, dimension)).reshape( 1, N, dimension ) - frame = darray.from_array(frame, chunks=(500, dimension)).reshape( + end_frame = darray.from_array(end_frame, chunks=(500, dimension)).reshape( N, 1, dimension ) - dist = ((pbc_diff(onset, frame, box) ** 2).sum(axis=-1) ** 0.5).ravel() + dist = ( + (pbc_diff(start_frame, end_frame, box) ** 2).sum(axis=-1) ** 0.5 + ).ravel() if np.diff(bins).std() < 1e6: dx = bins[0] - bins[1] hist = darray.bincount((dist // dx).astype(int), minlength=(len(bins) - 1)) @@ -301,15 +298,18 @@ def van_hove_distinct( minlength = len(bins) - 1 def f(x): - d = (pbc_diff(x, frame, box) ** 2).sum(axis=-1) ** 0.5 + d = (pbc_diff(x, end_frame, box) ** 2).sum(axis=-1) ** 0.5 return np.bincount((d // dx).astype(int), minlength=minlength)[ :minlength ] - hist = sum(f(x) for x in onset) + hist = sum(f(x) for x in start_frame) else: dist = ( - pbc_diff(onset.reshape(1, -1, 3), frame.reshape(-1, 1, 3), box) ** 2 + pbc_diff( + start_frame.reshape(1, -1, 3), end_frame.reshape(-1, 1, 3), box + ) + ** 2 ).sum(axis=-1) ** 0.5 hist = histogram(dist, bins=bins)[0] return hist / N @@ -320,17 +320,18 @@ def overlap( end_frame: CoordinateFrame, radius: float = 0.1, mode: str = "self", -): +) -> float: """ Compute the overlap with a reference configuration defined in a CoordinatesTree. Args: - onset: Initial frame, this is only used to get the frame index - frame: The current configuration + start_frame: Initial frame, this is only used to get the frame index + end_frame: The current configuration radius: The cutoff radius for the overlap + mode: Select between "indifferent", "self" or "distict" part of the overlap This function is intended to be used with :func:`shifted_correlation`. - As usual the first two arguments are used internally and the remaining ones + As usual, the first two arguments are used internally, and the remaining ones should be defined with :func:`functools.partial`. If the overlap of a subset of the system should be calculated, this has to be @@ -359,11 +360,13 @@ def overlap( return np.sum(index != index_dist) / len(start_frame) -def coherent_scattering_function(onset, frame, q): +def coherent_scattering_function( + start_frame: CoordinateFrame, end_frame: CoordinateFrame, q: float +) -> np.ndarray: """ Calculate the coherent scattering function. """ - box = onset.box.diagonal() + box = start_frame.box.diagonal() dimension = len(box) def scfunc(x, y): @@ -381,7 +384,7 @@ def coherent_scattering_function(onset, frame, q): else: return np.sin(x) / x - return coherent_sum(scfunc, onset.pbc, frame.pbc) / len(onset) + return coherent_sum(scfunc, start_frame.pbc, end_frame.pbc) / len(start_frame) def non_gaussian_parameter( @@ -391,7 +394,7 @@ def non_gaussian_parameter( axis: str = "all", ) -> float: """ - Calculate the Non-Gaussian parameter : + Calculate the non-Gaussian parameter. ..math: \alpha_2 (t) = \frac{3}{5}\frac{\langle r_i^4(t)\rangle}{\langle r_i^2(t)\rangle^2} - 1 From 32e3541c4bf6fb462688785ea51f711cee6ccd8b Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 16:40:43 +0100 Subject: [PATCH 35/73] Changed type hint for version from int to str --- src/mdevaluate/autosave.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mdevaluate/autosave.py b/src/mdevaluate/autosave.py index 9666d5e..9195eef 100644 --- a/src/mdevaluate/autosave.py +++ b/src/mdevaluate/autosave.py @@ -142,7 +142,7 @@ def load_data(filename): def autosave_data( - nargs: int, kwargs_keys: Optional[Iterable[str]] = None, version: Optional[int] = None + nargs: int, kwargs_keys: Optional[Iterable[str]] = None, version: Optional[str] = None ) -> Callable: """ Enable autosaving of results for a function. From cc3768925a3c7158f6c88820dd3e681c01bfcbdd Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 26 Dec 2023 16:57:53 +0100 Subject: [PATCH 36/73] Changed time_average to work similar to shifted_correlation --- src/mdevaluate/distribution.py | 54 ++++++++++++++++------------------ 1 file changed, 25 insertions(+), 29 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index d3d03ca..7f0cd90 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -1,3 +1,5 @@ +from typing import Callable, Optional + import numpy as np from numpy.typing import ArrayLike from scipy import spatial @@ -17,8 +19,14 @@ from .pbc import pbc_diff, pbc_points from .logging import logger -@autosave_data(nargs=2, kwargs_keys=("coordinates_b",), version="time_average-1") -def time_average(function, coordinates, coordinates_b=None, pool=None): +@autosave_data(nargs=2, kwargs_keys=("coordinates_b",)) +def time_average( + function: Callable, + coordinates: Coordinates, + coordinates_b: Optional[Coordinates] = None, + skip: float = 0.1, + segments: int = 100, +) -> np.ndarray: """ Compute the time average of a function. @@ -27,35 +35,23 @@ def time_average(function, coordinates, coordinates_b=None, pool=None): The function that will be averaged, it has to accept exactly one argument which is the current atom set coordinates: The coordinates object of the simulation - pool (multiprocessing.Pool, opt.): - A multiprocessing pool which will be used for cocurrent calculation of the - averaged function - + coordinates_b: Additional coordinates object of the simulation + skip: + segments: """ - if pool is not None: - _map = pool.imap + frame_indices = np.unique( + np.int_( + np.linspace(len(coordinates) * skip, len(coordinates) - 1, num=segments) + ) + ) + if coordinates_b is None: + result = [function(coordinates[frame_index]) for frame_index in frame_indices] else: - _map = map - - number_of_averages = 0 - result = 0 - - if coordinates_b is not None: - if coordinates._slice != coordinates_b._slice: - logger.warning("Different slice for coordinates and coordinates_b.") - coordinate_iter = (iter(coordinates), iter(coordinates_b)) - else: - coordinate_iter = (iter(coordinates),) - - evaluated = _map(function, *coordinate_iter) - - for ev in evaluated: - number_of_averages += 1 - result += ev - if number_of_averages % 100 == 0: - logger.debug("time_average: %d", number_of_averages) - - return result / number_of_averages + result = [ + function(coordinates[frame_index], coordinates_b[frame_index]) + for frame_index in frame_indices + ] + return np.mean(result, axis=0) def time_histogram(function, coordinates, bins, hist_range, pool=None): From bffcd56cdcacdf8e93c67d7a27cff7bca4d9ce8d Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 27 Dec 2023 12:43:05 +0100 Subject: [PATCH 37/73] Changed gr to work with time_average and use next_neighbors --- src/mdevaluate/distribution.py | 179 +++++++++++---------------------- 1 file changed, 58 insertions(+), 121 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index 7f0cd90..85f1e70 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional +from typing import Callable, Optional, Union import numpy as np from numpy.typing import ArrayLike @@ -16,7 +16,6 @@ from .coordinates import ( from .autosave import autosave_data from .utils import runningmean from .pbc import pbc_diff, pbc_points -from .logging import logger @autosave_data(nargs=2, kwargs_keys=("coordinates_b",)) @@ -33,7 +32,7 @@ def time_average( Args: function: The function that will be averaged, it has to accept exactly one argument - which is the current atom set + which is the current atom set (or two if coordinates_b is provided) coordinates: The coordinates object of the simulation coordinates_b: Additional coordinates object of the simulation skip: @@ -54,44 +53,13 @@ def time_average( return np.mean(result, axis=0) -def time_histogram(function, coordinates, bins, hist_range, pool=None): - coordinate_iter = iter(coordinates) - - if pool is not None: - _map = pool.imap - else: - _map = map - - evaluated = _map(function, coordinate_iter) - - results = [] - hist_results = [] - for num, ev in enumerate(evaluated): - results.append(ev) - - if num % 100 == 0 and num > 0: - print(num) - r = np.array(results).T - for i, row in enumerate(r): - histo, _ = np.histogram(row, bins=bins, range=hist_range) - if len(hist_results) <= i: - hist_results.append(histo) - else: - hist_results[i] += histo - results = [] - return hist_results - - -def calc_gr( - traj_a: Coordinates, - traj_b: Coordinates = None, - r_max: float = None, - delta_r: float = 0.02, - segments: int = 1000, - skip: float = 0.1, +def gr( + atoms_a: CoordinateFrame, + atoms_b: Optional[CoordinateFrame] = None, + bins: Union[int, ArrayLike] = 100, remove_intra: bool = False, - shear: bool = False, -): + **kwargs +) -> np.ndarray: r""" Compute the radial pair distribution of one or two sets of atoms. @@ -99,98 +67,67 @@ def calc_gr( g_{AB}(r) = \frac{1}{\langle \rho_B\rangle N_A}\sum\limits_{i\in A}^{N_A} \sum\limits_{j\in B}^{N_B}\frac{\delta(r_{ij} -r)}{4\pi r^2} - For use with :func:`time_average`, define bins through the use of :func:`~functools.partial`, - the atom sets are passed to :func:`time_average`, if a second set of atoms should be used - specify it as ``coordinates_b`` and it will be passed to this function. + For use with :func:`time_average`, define bins through the use of + :func:`~functools.partial`, the atom sets are passed to :func:`time_average`, if a + second set of atoms should be used specify it as ``coordinates_b`` and it will be + passed to this function. Args: atoms_a: First set of atoms, used internally - atoms_b (opt.): Second set of atoms, used internally + atoms_b (opt.): Second set of atoms, used internal bins: Bins of the radial distribution function - box (opt.): Simulations box, if not specified this is taken from ``atoms_a.box`` - kind (opt.): Can be 'inter', 'intra' or None (default). - chunksize (opt.): - For large systems (N > 1000) the distaces have to be computed in chunks so the arrays - fit into memory, this parameter controlls the size of these chunks. It should be - as large as possible, depending on the available memory. - returnx (opt.): If True the x ordinate of the histogram is returned. + remove_intra: removes contributions from intra molecular pairs """ + distinct = True + if atoms_b is None: + atoms_b = atoms_a + distinct = False + elif np.array_equal(atoms_a, atoms_b): + distinct = False - def gr_frame( - atoms_a: CoordinateFrame, - atoms_b: CoordinateFrame, - bins: ArrayLike, - remove_intra: bool = False, - ): - box = atoms_b.box - n = len(atoms_a) / np.prod(np.diag(box)) - V = 4 / 3 * np.pi * bins[-1] ** 3 - particles_in_volume = int(n * V * 1.1) - if np.all(np.diag(np.diag(box)) == box): - atoms_b = atoms_b % np.diag(box) - atoms_b_res_ids = atoms_b.residue_ids - atoms_b_tree = KDTree(atoms_b, boxsize=np.diag(box)) - else: - atoms_b_pbc, atoms_b_pbc_index = pbc_points( - atoms_b, box, thickness=bins[-1] + 0.1, index=True, shear=shear - ) - atoms_b_res_ids = atoms_b.residue_ids[atoms_b_pbc_index] - atoms_b_tree = KDTree(atoms_b_pbc) - distances, distances_index = atoms_b_tree.query( - atoms_a, particles_in_volume, distance_upper_bound=bins[-1] + 0.1 - ) - if np.array_equal(atoms_a, atoms_b): - distances = distances[:, 1:] - distances_index = distances_index[:, 1:] - - if remove_intra: - new_distances = [] - for entry in list(zip(atoms_a.residue_ids, distances, distances_index)): - mask = entry[1] < np.inf - new_distances.append( - entry[1][mask][atoms_b_res_ids[entry[2][mask]] != entry[0]] - ) - distances = np.concatenate(new_distances) - else: - distances = distances.flatten() - - hist = np.histogram(distances, bins=bins, range=(0, bins[-1]), density=False)[0] - gr = hist / len(atoms_a) - gr = gr / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) - n = len(atoms_b) / np.prod(np.diag(atoms_b.box)) - gr = gr / n - - return gr, n - - if traj_b is None: - traj_b = traj_a - - start_frame = traj_a[int(len(traj_a) * skip)] - if r_max: - upper_bound = r_max + box = atoms_b.box + if isinstance(bins, int): + upper_bound = np.min(np.diag(box)) else: - upper_bound = round(np.min(np.diag(start_frame.box)) / 2 - 0.05, 1) + upper_bound = bins[-1] - num_steps = int(upper_bound * (1 / delta_r) + 1) - bins = np.linspace(0, upper_bound, num_steps) - r = bins[1:] - (bins[1] - bins[0]) / 2 - frame_indices = np.unique( - np.int_(np.linspace(len(traj_a) * skip, len(traj_a) - 1, num=segments)) + n = len(atoms_a) / np.prod(np.diag(box)) + V = 4 / 3 * np.pi * bins[-1] ** 3 + particles_in_volume = int(n * V * 1.1) + distances, indices = next_neighbors( + atoms_a, + atoms_b, + number_of_neighbors=particles_in_volume, + distance_upper_bound=upper_bound, + distinct=distinct, + **kwargs ) - gr = [] - n = [] - for frame_index in frame_indices: - result = gr_frame( - traj_a[frame_index], traj_b[frame_index], bins, remove_intra=remove_intra - ) - gr.append(result[0]) - n.append(result[1]) - gr = np.mean(gr, axis=0) - n = np.mean(n, axis=0) - return r, gr, n + + if remove_intra: + new_distances = [] + for entry in list(zip(atoms_a.residue_ids, distances, indices)): + mask = entry[1] < np.inf + new_distances.append( + entry[1][mask][atoms_b.residue_ids[entry[2][mask]] != entry[0]] + ) + distances = np.concatenate(new_distances) + else: + distances = distances.flatten() + + hist, bins = np.histogram( + distances, bins=bins, range=(0, upper_bound), density=False + ) + hist = hist / len(atoms_a) + hist = hist / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) + n = len(atoms_b) / np.prod(np.diag(atoms_b.box)) + hist = hist / n + + return hist -def distance_distribution(atoms, bins): +def distance_distribution( + atoms: ArrayLike, bins: Optional[int, ArrayLike] +) -> np.ndarray: connection_vectors = atoms[:-1, :] - atoms[1:, :] connection_lengths = (connection_vectors**2).sum(axis=1) ** 0.5 return np.histogram(connection_lengths, bins)[0] From 3cfdf79777fc0e7ea4a4460b1ead436c0d1300e8 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 11:52:58 +0100 Subject: [PATCH 38/73] Added type hints --- src/mdevaluate/distribution.py | 151 ++++++++++++++------------------- 1 file changed, 64 insertions(+), 87 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index 85f1e70..ea7c42f 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -14,7 +14,6 @@ from .coordinates import ( next_neighbors, ) from .autosave import autosave_data -from .utils import runningmean from .pbc import pbc_diff, pbc_points @@ -56,7 +55,7 @@ def time_average( def gr( atoms_a: CoordinateFrame, atoms_b: Optional[CoordinateFrame] = None, - bins: Union[int, ArrayLike] = 100, + bins: Optional[ArrayLike] = None, remove_intra: bool = False, **kwargs ) -> np.ndarray: @@ -84,13 +83,10 @@ def gr( distinct = False elif np.array_equal(atoms_a, atoms_b): distinct = False + if bins is None: + bins = np.arange(0, 1, 0.01) box = atoms_b.box - if isinstance(bins, int): - upper_bound = np.min(np.diag(box)) - else: - upper_bound = bins[-1] - n = len(atoms_a) / np.prod(np.diag(box)) V = 4 / 3 * np.pi * bins[-1] ** 3 particles_in_volume = int(n * V * 1.1) @@ -98,7 +94,7 @@ def gr( atoms_a, atoms_b, number_of_neighbors=particles_in_volume, - distance_upper_bound=upper_bound, + distance_upper_bound=bins[-1], distinct=distinct, **kwargs ) @@ -114,9 +110,7 @@ def gr( else: distances = distances.flatten() - hist, bins = np.histogram( - distances, bins=bins, range=(0, upper_bound), density=False - ) + hist, bins = np.histogram(distances, bins=bins, range=(0, bins[-1]), density=False) hist = hist / len(atoms_a) hist = hist / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) n = len(atoms_b) / np.prod(np.diag(atoms_b.box)) @@ -126,14 +120,16 @@ def gr( def distance_distribution( - atoms: ArrayLike, bins: Optional[int, ArrayLike] + atoms: CoordinateFrame, bins: Optional[int, ArrayLike] ) -> np.ndarray: connection_vectors = atoms[:-1, :] - atoms[1:, :] connection_lengths = (connection_vectors**2).sum(axis=1) ** 0.5 return np.histogram(connection_lengths, bins)[0] -def tetrahedral_order(atoms, reference_atoms=None): +def tetrahedral_order( + atoms: CoordinateFrame, reference_atoms: CoordinateFrame = None +) -> np.ndarray: if reference_atoms is None: reference_atoms = atoms indices = next_neighbors( @@ -175,15 +171,22 @@ def tetrahedral_order(atoms, reference_atoms=None): return q -def tetrahedral_order_distribution(atoms, reference_atoms=None, bins=None): +def tetrahedral_order_distribution( + atoms: CoordinateFrame, + reference_atoms: Optional[CoordinateFrame] = None, + bins: Optional[ArrayLike] = None, +) -> np.ndarray: assert bins is not None, "Bin edges of the distribution have to be specified." Q = tetrahedral_order(atoms, reference_atoms=reference_atoms) return np.histogram(Q, bins=bins)[0] def radial_density( - atoms, bins, symmetry_axis=(0, 0, 1), origin=(0, 0, 0), height=1, returnx=False -): + atoms: CoordinateFrame, + bins: Optional[ArrayLike] = None, + symmetry_axis: ArrayLike = (0, 0, 1), + origin: Optional[ArrayLike] = None, +) -> np.ndarray: """ Calculate the radial density distribution. @@ -192,7 +195,7 @@ def radial_density( Args: atoms: Set of coordinates. - bins: + bins (opt.): Bin specification that is passed to numpy.histogram. This needs to be a list of bin edges if the function is used within time_average. symmetry_axis (opt.): @@ -200,30 +203,27 @@ def radial_density( default is z-axis. origin (opt.): Origin of the rotational symmetry, e.g. center of the pore. - height (opt.): - Height of the pore, necessary for correct normalization of the density. - returnx (opt.): - If True, the x ordinate of the distribution is returned. """ + if origin is None: + origin = np.diag(atoms.box) / 2 + if bins is None: + bins = np.arange(0, np.min(np.diag(atoms.box) / 2), 0.01) + length = np.diag(atoms.box) * symmetry_axis cartesian = rotate_axis(atoms - origin, symmetry_axis) radius, _ = polar_coordinates(cartesian[:, 0], cartesian[:, 1]) hist = np.histogram(radius, bins=bins)[0] - volume = np.pi * (bins[1:] ** 2 - bins[:-1] ** 2) * height - res = hist / volume - if returnx: - return np.vstack((runningmean(bins, 2), res)) - else: - return res + volume = np.pi * (bins[1:] ** 2 - bins[:-1] ** 2) * length + return hist / volume def shell_density( - atoms, - shell_radius, - bins, - shell_thickness=0.5, - symmetry_axis=(0, 0, 1), - origin=(0, 0, 0), -): + atoms: CoordinateFrame, + shell_radius: float, + bins: ArrayLike, + shell_thickness: float = 0.5, + symmetry_axis: ArrayLike = (0, 0, 1), + origin: Optional[ArrayLike] = None, +) -> np.ndarray: """ Compute the density distribution on a cylindrical shell. @@ -240,6 +240,8 @@ def shell_density( Returns: Two-dimensional density distribution of the atoms in the defined shell. """ + if origin is None: + origin = np.diag(atoms.box) / 2 cartesian = rotate_axis(atoms - origin, symmetry_axis) radius, theta = polar_coordinates(cartesian[:, 0], cartesian[:, 1]) shell_indices = (shell_radius <= radius) & ( @@ -250,40 +252,13 @@ def shell_density( return hist -def spatial_density(atoms, bins, weights=None): - """ - Compute the spatial density distribution. - """ - density, _ = np.histogramdd(atoms, bins=bins, weights=weights) - return density - - -def mixing_ratio_distribution( - atoms_a, - atoms_b, - bins_ratio, - bins_density, - weights_a=None, - weights_b=None, - weights_ratio=None, -): - """ - Compute the distribution of the mixing ratio of two sets of atoms. - """ - - density_a, _ = time_average - density_b, _ = np.histogramdd(atoms_b, bins=bins_density, weights=weights_b) - mixing_ratio = density_a / (density_a + density_b) - good_inds = (density_a != 0) & (density_b != 0) - hist, _ = np.histogram( - mixing_ratio[good_inds], bins=bins_ratio, weights=weights_ratio - ) - return hist - - def next_neighbor_distribution( - atoms, reference=None, number_of_neighbors=4, bins=None, normed=True -): + atoms: CoordinateFrame, + reference: Optional[CoordinateFrame] = None, + number_of_neighbors: int = 4, + bins: Optional[ArrayLike] = None, + normed: bool = True, +) -> np.ndarray: """ Compute the distribution of next neighbors with the same residue name. """ @@ -299,15 +274,14 @@ def next_neighbor_distribution( def hbonds( - D, - H, - A, - box, - DA_lim=0.35, - HA_lim=0.35, - min_cos=np.cos(30 * np.pi / 180), - full_output=False, -): + D: CoordinateFrame, + H: CoordinateFrame, + A: CoordinateFrame, + DA_lim: float = 0.35, + HA_lim: float = 0.35, + min_cos: float = np.cos(30 * np.pi / 180), + full_output: bool = False, +) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray, np.ndarray]]: """ Compute h-bond pairs @@ -327,8 +301,10 @@ def hbonds( List of (D,A)-pairs in hbonds. """ - def dist_DltA(D, H, A, box, max_dist=0.35): - ppoints, pind = pbc_points(D, box, thickness=max_dist + 0.1, index=True) + def dist_DltA( + D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35 + ) -> np.ndarray: + ppoints, pind = pbc_points(D, thickness=max_dist + 0.1, index=True) Dtree = spatial.cKDTree(ppoints) Atree = spatial.cKDTree(A) pairs = Dtree.sparse_distance_matrix(Atree, max_dist, output_type="ndarray") @@ -337,8 +313,10 @@ def hbonds( pairs[:, 0] = pind[pairs[:, 0]] return pairs - def dist_AltD(D, H, A, box, max_dist=0.35): - ppoints, pind = pbc_points(A, box, thickness=max_dist + 0.1, index=True) + def dist_AltD( + D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35 + ) -> np.ndarray: + ppoints, pind = pbc_points(A, thickness=max_dist + 0.1, index=True) Atree = spatial.cKDTree(ppoints) Dtree = spatial.cKDTree(D) pairs = Atree.sparse_distance_matrix(Dtree, max_dist, output_type="ndarray") @@ -348,10 +326,11 @@ def hbonds( pairs[:, 1] = pind[pairs[:, 1]] return pairs + box = D.box if len(D) <= len(A): - pairs = dist_DltA(D, H, A, box, DA_lim) + pairs = dist_DltA(D, A, DA_lim) else: - pairs = dist_AltD(D, H, A, box, DA_lim) + pairs = dist_AltD(D, A, DA_lim) vDH = pbc_diff(D[pairs[:, 0]], H[pairs[:, 0]], box) vDA = pbc_diff(D[pairs[:, 0]], A[pairs[:, 1]], box) @@ -378,13 +357,11 @@ def hbonds( return pairs[is_bond] -def calc_cluster_sizes(frame, r_max=0.35): - frame_PBC, indices_PBC = pbc_points( - frame, frame.box, thickness=r_max + 0.1, index=True - ) +def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> np.ndarray: + frame_PBC, indices_PBC = pbc_points(atoms, thickness=r_max + 0.1, index=True) tree = KDTree(frame_PBC) matrix = tree.sparse_distance_matrix(tree, r_max, output_type="ndarray") - new_matrix = np.zeros((len(frame), len(frame))) + new_matrix = np.zeros((len(atoms), len(atoms))) for entry in matrix: if entry[2] > 0: new_matrix[indices_PBC[entry[0]], indices_PBC[entry[1]]] = 1 From b66e920758975ade59a973df0c578a3638684eed Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 12:00:40 +0100 Subject: [PATCH 39/73] Updated type hints --- src/mdevaluate/distribution.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index ea7c42f..dd069a7 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -1,7 +1,7 @@ from typing import Callable, Optional, Union import numpy as np -from numpy.typing import ArrayLike +from numpy.typing import ArrayLike, NDArray from scipy import spatial from scipy.spatial import KDTree from scipy.sparse.csgraph import connected_components @@ -24,7 +24,7 @@ def time_average( coordinates_b: Optional[Coordinates] = None, skip: float = 0.1, segments: int = 100, -) -> np.ndarray: +) -> NDArray: """ Compute the time average of a function. @@ -58,7 +58,7 @@ def gr( bins: Optional[ArrayLike] = None, remove_intra: bool = False, **kwargs -) -> np.ndarray: +) -> NDArray: r""" Compute the radial pair distribution of one or two sets of atoms. @@ -121,7 +121,7 @@ def gr( def distance_distribution( atoms: CoordinateFrame, bins: Optional[int, ArrayLike] -) -> np.ndarray: +) -> NDArray: connection_vectors = atoms[:-1, :] - atoms[1:, :] connection_lengths = (connection_vectors**2).sum(axis=1) ** 0.5 return np.histogram(connection_lengths, bins)[0] @@ -129,7 +129,7 @@ def distance_distribution( def tetrahedral_order( atoms: CoordinateFrame, reference_atoms: CoordinateFrame = None -) -> np.ndarray: +) -> NDArray: if reference_atoms is None: reference_atoms = atoms indices = next_neighbors( @@ -175,7 +175,7 @@ def tetrahedral_order_distribution( atoms: CoordinateFrame, reference_atoms: Optional[CoordinateFrame] = None, bins: Optional[ArrayLike] = None, -) -> np.ndarray: +) -> NDArray: assert bins is not None, "Bin edges of the distribution have to be specified." Q = tetrahedral_order(atoms, reference_atoms=reference_atoms) return np.histogram(Q, bins=bins)[0] @@ -186,7 +186,7 @@ def radial_density( bins: Optional[ArrayLike] = None, symmetry_axis: ArrayLike = (0, 0, 1), origin: Optional[ArrayLike] = None, -) -> np.ndarray: +) -> NDArray: """ Calculate the radial density distribution. @@ -223,7 +223,7 @@ def shell_density( shell_thickness: float = 0.5, symmetry_axis: ArrayLike = (0, 0, 1), origin: Optional[ArrayLike] = None, -) -> np.ndarray: +) -> NDArray: """ Compute the density distribution on a cylindrical shell. @@ -258,7 +258,7 @@ def next_neighbor_distribution( number_of_neighbors: int = 4, bins: Optional[ArrayLike] = None, normed: bool = True, -) -> np.ndarray: +) -> NDArray: """ Compute the distribution of next neighbors with the same residue name. """ @@ -281,7 +281,7 @@ def hbonds( HA_lim: float = 0.35, min_cos: float = np.cos(30 * np.pi / 180), full_output: bool = False, -) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray, np.ndarray]]: +) -> Union[NDArray, tuple[NDArray, NDArray, NDArray]]: """ Compute h-bond pairs @@ -303,7 +303,7 @@ def hbonds( def dist_DltA( D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35 - ) -> np.ndarray: + ) -> NDArray: ppoints, pind = pbc_points(D, thickness=max_dist + 0.1, index=True) Dtree = spatial.cKDTree(ppoints) Atree = spatial.cKDTree(A) @@ -315,7 +315,7 @@ def hbonds( def dist_AltD( D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35 - ) -> np.ndarray: + ) -> NDArray: ppoints, pind = pbc_points(A, thickness=max_dist + 0.1, index=True) Atree = spatial.cKDTree(ppoints) Dtree = spatial.cKDTree(D) @@ -357,7 +357,7 @@ def hbonds( return pairs[is_bond] -def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> np.ndarray: +def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> NDArray: frame_PBC, indices_PBC = pbc_points(atoms, thickness=r_max + 0.1, index=True) tree = KDTree(frame_PBC) matrix = tree.sparse_distance_matrix(tree, r_max, output_type="ndarray") @@ -372,7 +372,7 @@ def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> np.ndarra return np.array(cluster_sizes).flatten() -def gyration_radius(position: CoordinateFrame) -> np.ndarray: +def gyration_radius(position: CoordinateFrame) -> NDArray: r""" Calculates a list of all radii of gyration of all molecules given in the coordinate frame, weighted with the masses of the individual atoms. From cc08f5ae50a58801135e657a1b9be1981a6c2220 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 12:13:53 +0100 Subject: [PATCH 40/73] Added type hints --- src/mdevaluate/functions.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/src/mdevaluate/functions.py b/src/mdevaluate/functions.py index 7348d0c..8132e75 100644 --- a/src/mdevaluate/functions.py +++ b/src/mdevaluate/functions.py @@ -1,22 +1,23 @@ import numpy as np +from numpy.typing import ArrayLike from scipy.special import gamma as spgamma from scipy.integrate import quad as spquad -def kww(t, A, tau, beta): +def kww(t: ArrayLike, A: float, tau: float, beta: float) -> ArrayLike: return A * np.exp(-((t / tau) ** beta)) -def kww_1e(A, tau, beta): +def kww_1e(A: float, tau: float, beta: float) -> float: return tau * (-np.log(1 / (np.e * A))) ** (1 / beta) -def cole_davidson(omega, A, beta, tau): +def cole_davidson(omega: ArrayLike, A: float, beta: float, tau: float) -> ArrayLike: P = np.arctan(omega * tau) return A * np.cos(P) ** beta * np.sin(beta * P) -def cole_cole(omega, A, beta, tau): +def cole_cole(omega: ArrayLike, A: float, beta: float, tau: float) -> ArrayLike: return ( A * (omega * tau) ** beta @@ -29,7 +30,9 @@ def cole_cole(omega, A, beta, tau): ) -def havriliak_negami(omega, A, beta, alpha, tau): +def havriliak_negami( + omega: ArrayLike, A: float, beta: float, alpha: float, tau: float +) -> ArrayLike: r""" Imaginary part of the Havriliak-Negami function. @@ -40,26 +43,26 @@ def havriliak_negami(omega, A, beta, alpha, tau): # fits decay of correlation times, e.g. with distance to pore walls -def colen(d, X, t8, A): - return t8 * np.exp(A * np.exp(-d / X)) +def colen(d: ArrayLike, X: float, tau_pc: float, A: float) -> ArrayLike: + return tau_pc * np.exp(A * np.exp(-d / X)) # fits decay of the plateau height of the overlap function, # e.g. with distance to pore walls -def colenQ(d, X, Qb, g): +def colenQ(d: ArrayLike, X: float, Qb: float, g: float) -> ArrayLike: return (1 - Qb) * np.exp(-((d / X) ** g)) + Qb -def vft(T, tau_0, B, T_inf): +def vft(T: ArrayLike, tau_0: float, B: float, T_inf: float) -> ArrayLike: return tau_0 * np.exp(B / (T - T_inf)) -def arrhenius(T, tau_0, E_a): +def arrhenius(T: ArrayLike, tau_0: float, E_a: float) -> ArrayLike: return tau_0 * np.exp(E_a / T) -def MLfit(t, tau, A, alpha): - def MLf(z, a): +def MLfit(t: ArrayLike, tau: float, A: float, alpha: float) -> ArrayLike: + def MLf(z: ArrayLike, a: float) -> ArrayLike: """Mittag-Leffler function""" z = np.atleast_1d(z) if a == 0: @@ -71,7 +74,7 @@ def MLfit(t, tau, A, alpha): return np.polynomial.polynomial.polyval(z, 1 / spgamma(a * k + 1)) # a helper for tricky case, from Gorenflo, Loutchko & Luchko - def _MLf(z, a): + def _MLf(z: float, a: float) -> ArrayLike: if z < 0: f = lambda x: ( np.exp(-x * (-z) ** (1 / a)) From 94d67496ba814085831d96020d9db6575072b457 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 12:42:43 +0100 Subject: [PATCH 41/73] Added type hints --- src/mdevaluate/pbc.py | 118 +++++++++++++----------------------------- 1 file changed, 37 insertions(+), 81 deletions(-) diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index f57a60d..88457cb 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -1,54 +1,55 @@ from collections import OrderedDict +from typing import Optional, Union import numpy as np +from numpy.typing import ArrayLike, NDArray from scipy.spatial import cKDTree from itertools import product from .logging import logger +from .coordinates import CoordinateFrame -def pbc_diff(v1, v2=None, box=None): +def pbc_diff( + coords_a: NDArray, coords_b: NDArray, box: Optional[NDArray] = None +) -> NDArray: if box is None: - out = v1 - v2 + out = coords_a - coords_b elif len(getattr(box, "shape", [])) == 1: - out = pbc_diff_rect(v1, v2, box) + out = pbc_diff_rect(coords_a, coords_b, box) elif len(getattr(box, "shape", [])) == 2: - out = pbc_diff_tric(v1, v2, box) + out = pbc_diff_tric(coords_a, coords_b, box) else: raise NotImplementedError("cannot handle box") return out -def pbc_diff_rect(v1, v2, box): +def pbc_diff_rect(coords_a: NDArray, coords_b: NDArray, box: NDArray) -> NDArray: """ Calculate the difference of two vectors, considering periodic boundary conditions. """ - if v2 is None: - v = v1 - else: - v = v1 - v2 - + v = coords_a - coords_b s = v / box - v = box * (s - s.round()) + v = box * (s - np.round(s)) return v -def pbc_diff_tric(v1, v2=None, box=None): +def pbc_diff_tric(coords_a: NDArray, coords_b: NDArray, box: NDArray) -> NDArray: """ - difference vector for arbitrary pbc + Difference vector for arbitrary pbc Args: box_matrix: CoordinateFrame.box """ if len(box.shape) == 1: box = np.diag(box) - if v1.shape == (3,): - v1 = v1.reshape((1, 3)) # quick 'n dirty - if v2.shape == (3,): - v2 = v2.reshape((1, 3)) + if coords_a.shape == (3,): + coords_a = coords_a.reshape((1, 3)) # quick 'n dirty + if coords_b.shape == (3,): + coords_b = coords_b.reshape((1, 3)) if box is not None: - r3 = np.subtract(v1, v2) + r3 = np.subtract(coords_a, coords_b) r2 = np.subtract( r3, (np.rint(np.divide(r3[:, 2], box[2][2])))[:, np.newaxis] @@ -65,68 +66,17 @@ def pbc_diff_tric(v1, v2=None, box=None): * box[0][np.newaxis, :], ) else: - v = v1 - v2 + v = coords_a - coords_b return v -def pbc_dist(a1, a2, box=None): - return ((pbc_diff(a1, a2, box) ** 2).sum(axis=1)) ** 0.5 +def pbc_dist( + atoms_a: CoordinateFrame, atoms_b: CoordinateFrame, box: Optional[NDArray] = None +) -> ArrayLike: + return ((pbc_diff(atoms_a, atoms_b, box) ** 2).sum(axis=1)) ** 0.5 -def pbc_extend(c, box): - """ - in: c is frame, box is frame.box - out: all atoms in frame and their perio. image (shape => array(len(c)*27,3)) - """ - c = np.asarray(c) - if c.shape == (3,): - c = c.reshape((1, 3)) # quick 'n dirty - comb = np.array( - [np.asarray(i) for i in product([0, -1, 1], [0, -1, 1], [0, -1, 1])] - ) - b_matrices = comb[:, :, np.newaxis] * box[np.newaxis, :, :] - b_vectors = b_matrices.sum(axis=1)[np.newaxis, :, :] - return c[:, np.newaxis, :] + b_vectors - - -def pbc_kdtree(v1, box, leafsize=32, compact_nodes=False, balanced_tree=False): - """ - kd_tree with periodic images - box - whole matrix - rest: optional optimization - """ - r0 = cKDTree( - pbc_extend(v1, box).reshape((-1, 3)), leafsize, compact_nodes, balanced_tree - ) - return r0 - - -def pbc_kdtree_query(v1, v2, box, n=1): - """ - kd_tree query with periodic images - """ - r0, r1 = pbc_kdtree(v1, box).query(v2, n) - r1 = r1 // 27 - return r0, r1 - - -def pbc_backfold_rect(act_frame, box_matrix): - """ - mimics "trjconv ... -pbc atom -ur rect" - - folds coords of act_frame in cuboid - - """ - af = np.asarray(act_frame) - if af.shape == (3,): - act_frame = act_frame.reshape((1, 3)) # quick 'n dirty - b = box_matrix - c = np.diag(b) / 2 - af = pbc_diff(np.zeros((1, 3)), af - c, b) - return af + c - - -def pbc_backfold_compact(act_frame, box_matrix): +def pbc_backfold_compact(act_frame: NDArray, box_matrix: NDArray) -> NDArray: """ mimics "trjconv ... -pbc atom -ur compact" @@ -146,11 +96,11 @@ def pbc_backfold_compact(act_frame, box_matrix): b_matrices = comb[:, :, np.newaxis] * box[np.newaxis, :, :] b_vectors = b_matrices.sum(axis=1)[np.newaxis, :, :] sc = c[:, np.newaxis, :] + b_vectors - w = np.argsort((((sc) - ctr) ** 2).sum(2), 1)[:, 0] + w = np.argsort(((sc - ctr) ** 2).sum(2), 1)[:, 0] return sc[range(shape[0]), w] -def whole(frame): +def whole(frame: CoordinateFrame) -> CoordinateFrame: """ Apply ``-pbc whole`` to a CoordinateFrame. """ @@ -177,7 +127,7 @@ def whole(frame): NOJUMP_CACHESIZE = 128 -def nojump(frame, usecache=True): +def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame: """ Return the nojump coordinates of a frame, based on a jump matrix. """ @@ -226,15 +176,21 @@ def nojump(frame, usecache=True): return frame - delta -def pbc_points(coordinates, box, thickness=0, index=False, shear=False): +def pbc_points( + coordinates: CoordinateFrame, + thickness: Optional[float] = None, + index: bool = False, + shear: bool = False, +) -> Union[NDArray, tuple[NDArray, NDArray]]: """ Returns the points their first periodic images. Does not fold them back into the box. Thickness 0 means all 27 boxes. Positive means the box+thickness. Negative values mean that less than the box is returned. index=True also returns the indices with indices of images being their - originals values. + original values. """ + box = coordinates.box if shear: box[2, 0] = box[2, 0] % box[0, 0] # Shifts the box images in the other directions if they moved more than @@ -249,7 +205,7 @@ def pbc_points(coordinates, box, thickness=0, index=False, shear=False): coordinates_pbc = np.concatenate([coordinates + v @ box for v in grid], axis=0) size = np.diag(box) - if thickness != 0: + if thickness is not None: mask = np.all(coordinates_pbc > -thickness, axis=1) coordinates_pbc = coordinates_pbc[mask] indices = indices[mask] From 0dd6daa9413fdc06f38e1ec71fec1c93d6c90ee9 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 12:43:06 +0100 Subject: [PATCH 42/73] Reorganized imports --- src/mdevaluate/pbc.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index 88457cb..16678d8 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -4,7 +4,6 @@ from typing import Optional, Union import numpy as np from numpy.typing import ArrayLike, NDArray -from scipy.spatial import cKDTree from itertools import product from .logging import logger From b4f9b988767bd48007f36fc0c265bb35d8467e14 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 13:29:43 +0100 Subject: [PATCH 43/73] Switched to numpy histogram --- src/mdevaluate/correlation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index edcd942..3b67777 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -8,7 +8,7 @@ from functools import partial from scipy.spatial import KDTree from .autosave import autosave_data -from .utils import coherent_sum, histogram +from .utils import coherent_sum from .pbc import pbc_diff, pbc_points from .coordinates import Coordinates, CoordinateFrame, displacements_without_drift @@ -311,7 +311,7 @@ def van_hove_distinct( ) ** 2 ).sum(axis=-1) ** 0.5 - hist = histogram(dist, bins=bins)[0] + hist = np.histogram(dist, bins=bins)[0] return hist / N From 4a6c02627af4c9445e212dfa7fcae52f74f6529a Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 13:30:05 +0100 Subject: [PATCH 44/73] Added tables to requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index b84a79c..5e1531f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ mdanalysis pandas dask pathos +tables \ No newline at end of file From f2806ca3ca8b9c37e774cdfc7eebb6cc0d2576b7 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 13:33:47 +0100 Subject: [PATCH 45/73] Changed from len(index.shape) to index.ndim --- src/mdevaluate/correlation.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index 3b67777..7d2eee6 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -99,9 +99,9 @@ def shifted_correlation( return get_correlation(frames, start_frame, index, shifted_idx) else: index = selector(frames[start_frame]) - if len(index.shape) == 1: + if index.ndim == 1: return get_correlation(frames, start_frame, index, shifted_idx) - elif len(index.shape) == 2: + elif index.ndim == 2: correlations = [] for ind in index: correlations.append( @@ -110,7 +110,7 @@ def shifted_correlation( return correlations else: raise ValueError( - f"Index list of selector has {len(index.shape)} dimensions, " + f"Index list of selector has {index.ndim} dimensions, " "but should have 1 or 2" ) From 608cdb12ebaf0ad00d43c75b9c2da9ba25c045cd Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 14:10:16 +0100 Subject: [PATCH 46/73] Added type hints, refactored and deleted some functions --- src/mdevaluate/__init__.py | 1 + src/mdevaluate/utils.py | 191 +++++++++++++++++-------------------- 2 files changed, 86 insertions(+), 106 deletions(-) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index 2b8ef14..2687fda 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -13,6 +13,7 @@ from . import pbc from . import autosave from . import reader from . import system +from . import utils from .extra import free_energy_landscape, chill from .logging import logger diff --git a/src/mdevaluate/utils.py b/src/mdevaluate/utils.py index 38334db..cf21285 100644 --- a/src/mdevaluate/utils.py +++ b/src/mdevaluate/utils.py @@ -4,20 +4,21 @@ Collection of utility functions. import functools from time import time as pytime from subprocess import run -from types import FunctionType +from typing import Callable, Optional, Union import numpy as np +from numpy.typing import ArrayLike, NDArray import pandas as pd -from .functions import kww, kww_1e from scipy.ndimage import uniform_filter1d from scipy.interpolate import interp1d from scipy.optimize import curve_fit from .logging import logger +from .functions import kww, kww_1e -def five_point_stencil(xdata, ydata): +def five_point_stencil(xdata: ArrayLike, ydata: ArrayLike) -> ArrayLike: """ Calculate the derivative dy/dx with a five point stencil. This algorith is only valid for equally distributed x values. @@ -42,28 +43,28 @@ def five_point_stencil(xdata, ydata): def filon_fourier_transformation( - time, - correlation, - frequencies=None, - derivative="linear", - imag=True, -): + time: NDArray, + correlation: NDArray, + frequencies: Optional[NDArray] = None, + derivative: Union[str, NDArray] = "linear", + imag: bool = True, +) -> tuple[NDArray, NDArray]: """ - Fourier-transformation for slow varrying functions. The filon algorithmus is + Fourier-transformation for slow varying functions. The filon algorithm is described in detail in ref [Blochowicz]_, ch. 3.2.3. Args: - time: List of times where the correlation function was sampled. + time: List of times when the correlation function was sampled. correlation: Values of the correlation function. frequencies (opt.): List of frequencies where the fourier transformation will be calculated. - If None the frequencies will be choosen based on the input times. + If None the frequencies will be chosen based on the input times. derivative (opt.): - Approximation algorithmus for the derivative of the correlation function. + Approximation algorithm for the derivative of the correlation function. Possible values are: 'linear', 'stencil' or a list of derivatives. imag (opt.): If imaginary part of the integral should be calculated. - If frequencies are not explicitly given they will be evenly placed on a log scale + If frequencies are not explicitly given, they will be evenly placed on a log scale in the interval [1/tmax, 0.1/tmin] where tmin and tmax are the smallest respectively the biggest time (greater than 0) of the provided times. The frequencies are cut off at high values by one decade, since the fourier transformation deviates quite @@ -85,7 +86,7 @@ def filon_fourier_transformation( _, derivative = five_point_stencil(time, correlation) time = ((time[2:-1] * time[1:-2]) ** 0.5).reshape(-1, 1) derivative = derivative.reshape(-1, 1) - elif np.iterable(derivative) and len(time) is len(derivative): + elif isinstance(derivative, NDArray) and len(time) is len(derivative): derivative.reshape(-1, 1) else: raise NotImplementedError( @@ -111,15 +112,12 @@ def filon_fourier_transformation( + 1j * correlation[0] / frequencies ) - return ( - frequencies.reshape( - -1, - ), - fourier, - ) + return frequencies.reshape(-1), fourier -def superpose(x1, y1, x2, y2, N=100, damping=1.0): +def superpose( + x1: NDArray, y1: NDArray, x2: NDArray, y2: NDArray, damping: float = 1.0 +) -> tuple[NDArray, NDArray]: if x2[0] == 0: x2 = x2[1:] y2 = y2[1:] @@ -127,12 +125,12 @@ def superpose(x1, y1, x2, y2, N=100, damping=1.0): reg1 = x1 < x2[0] reg2 = x2 > x1[-1] x_ol = np.logspace( - np.log10(max(x1[~reg1][0], x2[~reg2][0]) + 0.001), - np.log10(min(x1[~reg1][-1], x2[~reg2][-1]) - 0.001), - (sum(~reg1) + sum(~reg2)) / 2, + np.log10(np.max(x1[~reg1][0], x2[~reg2][0]) + 0.001), + np.log10(np.min(x1[~reg1][-1], x2[~reg2][-1]) - 0.001), + (np.sum(~reg1) + np.sum(~reg2)) / 2, ) - def w(x): + def w(x: NDArray) -> NDArray: A = x_ol.min() B = x_ol.max() return (np.log10(B / x) / np.log10(B / A)) ** damping @@ -150,21 +148,7 @@ def superpose(x1, y1, x2, y2, N=100, damping=1.0): return xdata, ydata -def runningmean(data, nav): - """ - Compute the running mean of a 1-dimenional array. - - Args: - data: Input data of shape (N, ) - nav: Number of points over which the data will be averaged - - Returns: - Array of shape (N-(nav-1), ) - """ - return np.convolve(data, np.ones((nav,)) / nav, mode="valid") - - -def moving_average(A, n=3): +def moving_average(data: NDArray, n: int = 3) -> NDArray: """ Compute the running mean of an array. Uses the second axis if it is of higher dimensionality. @@ -177,27 +161,30 @@ def moving_average(A, n=3): Array of shape (N-(n-1), ) Supports 2D-Arrays. - Slower than runningmean for small n but faster for large n. """ k1 = int(n / 2) k2 = int((n - 1) / 2) if k2 == 0: - if A.ndim > 1: - return uniform_filter1d(A, n)[:, k1:] - return uniform_filter1d(A, n)[k1:] - if A.ndim > 1: - return uniform_filter1d(A, n)[:, k1:-k2] - return uniform_filter1d(A, n)[k1:-k2] + if data.ndim > 1: + return uniform_filter1d(data, n)[:, k1:] + return uniform_filter1d(data, n)[k1:] + if data.ndim > 1: + return uniform_filter1d(data, n)[:, k1:-k2] + return uniform_filter1d(data, n)[k1:-k2] -def coherent_sum(func, coord_a, coord_b): +def coherent_sum( + func: Callable[[ArrayLike, ArrayLike], float], + coord_a: ArrayLike, + coord_b: ArrayLike, +) -> float: """ Perform a coherent sum over two arrays :math:`A, B`. .. math:: \\frac{1}{N_A N_B}\\sum_i\\sum_j f(A_i, B_j) - For numpy arrays this is equal to:: + For numpy arrays, this is equal to:: N, d = x.shape M, d = y.shape @@ -206,24 +193,27 @@ def coherent_sum(func, coord_a, coord_b): Args: func: The function is called for each two items in both arrays, this should return a scalar value. - coord_a, coord_b: The two arrays. + coord_a: First array. + coord_b: Second array. """ - - def cohsum(coord_a, coord_b): - res = 0 - for i in range(len(coord_a)): - for j in range(len(coord_b)): - res += func(coord_a[i], coord_b[j]) - return res - - return cohsum(coord_a, coord_b) + res = 0 + for i in range(len(coord_a)): + for j in range(len(coord_b)): + res += func(coord_a[i], coord_b[j]) + return res -def coherent_histogram(func, coord_a, coord_b, bins, distinct=False): +def coherent_histogram( + func: Callable[[ArrayLike, ArrayLike], float], + coord_a: ArrayLike, + coord_b: ArrayLike, + bins: ArrayLike, + distinct: bool = False, +) -> NDArray: """ Compute a coherent histogram over two arrays, equivalent to coherent_sum. - For numpy arrays ofthis is equal to:: + For numpy arrays, this is equal to:: N, d = x.shape M, d = y.shape @@ -235,9 +225,11 @@ def coherent_histogram(func, coord_a, coord_b, bins, distinct=False): Args: func: The function is called for each two items in both arrays, this should return a scalar value. - coord_a, coord_b: The two arrays. - bins: The bins used for the histogram must be distributed regular on a linear + coord_a: First array. + coord_b: Second array. + bins: The bins used for the histogram must be distributed regularly on a linear scale. + distinct: Only calculate distinct part. """ assert np.isclose( @@ -248,32 +240,29 @@ def coherent_histogram(func, coord_a, coord_b, bins, distinct=False): N = len(bins) - 1 dh = (hmax - hmin) / N - def cohsum(coord_a, coord_b): - res = np.zeros((N,)) - for i in range(len(coord_a)): - for j in range(len(coord_b)): - if not (distinct and i == j): - h = func(coord_a[i], coord_b[j]) - if hmin <= h < hmax: - res[int((h - hmin) / dh)] += 1 - return res - - return cohsum(coord_a, coord_b) + res = np.zeros((N,)) + for i in range(len(coord_a)): + for j in range(len(coord_b)): + if not (distinct and i == j): + h = func(coord_a[i], coord_b[j]) + if hmin <= h < hmax: + res[int((h - hmin) / dh)] += 1 + return res -def Sq_from_gr(r, gr, q, ρ): +def Sq_from_gr(r: NDArray, gr: NDArray, q: NDArray, n: float) -> NDArray: r""" Compute the static structure factor as fourier transform of the pair correlation function. [Yarnell]_ .. math:: - S(q) - 1 = \\frac{4\\pi \\rho}{q}\\int\\limits_0^\\infty (g(r) - 1)\\,r \\sin(qr) dr + S(q)-1 = \\frac{4\\pi\\rho}{q}\\int\\limits_0^\\infty (g(r)-1)\\,r \\sin(qr) dr Args: r: Radii of the pair correlation function gr: Values of the pair correlation function q: List of q values - ρ: Average number density + n: Average number density .. [Yarnell] Yarnell, J. L., Katz, M. J., Wenzel, R. G., & Koenig, S. H. (1973). Physical @@ -282,10 +271,12 @@ def Sq_from_gr(r, gr, q, ρ): """ ydata = ((gr - 1) * r).reshape(-1, 1) * np.sin(r.reshape(-1, 1) * q.reshape(1, -1)) - return np.trapz(x=r, y=ydata, axis=0) * (4 * np.pi * ρ / q) + 1 + return np.trapz(x=r, y=ydata, axis=0) * (4 * np.pi * n / q) + 1 -def Fqt_from_Grt(data, q): +def Fqt_from_Grt( + data: Union[pd.DataFrame, ArrayLike], q: ArrayLike +) -> Union[pd.DataFrame, tuple[NDArray, NDArray]]: """ Calculate the ISF from the van Hove function for a given q value by fourier transform. @@ -317,7 +308,7 @@ def Fqt_from_Grt(data, q): return isf.index, isf.values -def singledispatchmethod(func): +def singledispatchmethod(func: Callable) -> Callable: """ A decorator to define a genric instance method, analogue to functools.singledispatch. @@ -332,22 +323,7 @@ def singledispatchmethod(func): return wrapper -def histogram(data, bins): - """ - Compute the histogram of the given data. Uses numpy.bincount function, if possible. - """ - dbins = np.diff(bins) - dx = dbins.mean() - if bins.min() == 0 and dbins.std() < 1e-6: - logger.debug("Using numpy.bincount for histogramm compuation.") - hist = np.bincount((data // dx).astype(int), minlength=len(dbins))[: len(dbins)] - else: - hist = np.histogram(data, bins=bins)[0] - - return hist, runningmean(bins, 2) - - -def quick1etau(t, C, n=7): +def quick1etau(t: ArrayLike, C: ArrayLike, n: int = 7) -> float: """ Estimate the time for a correlation function that goes from 1 to 0 to decay to 1/e. @@ -381,15 +357,15 @@ def quick1etau(t, C, n=7): return tau_est -def susceptibility(time, correlation, **kwargs): +def susceptibility( + time: NDArray, correlation: NDArray, **kwargs +) -> tuple[NDArray, NDArray]: """ Calculate the susceptibility of a correlation function. Args: time: Timesteps of the correlation data correlation: Value of the correlation function - **kwargs (opt.): - Additional keyword arguments will be passed to :func:`filon_fourier_transformation`. """ frequencies, fourier = filon_fourier_transformation( time, correlation, imag=False, **kwargs @@ -397,7 +373,7 @@ def susceptibility(time, correlation, **kwargs): return frequencies, frequencies * fourier -def read_gro(file): +def read_gro(file: str) -> tuple[pd.DataFrame, NDArray, str]: with open(file, "r") as f: lines = f.readlines() description = lines[0].splitlines()[0] @@ -438,7 +414,9 @@ def read_gro(file): return atoms_DF, box, description -def write_gro(file, atoms_DF, box, description): +def write_gro( + file: str, atoms_DF: pd.DataFrame, box: NDArray, description: str +) -> None: with open(file, "w") as f: f.write(f"{description} \n") f.write(f"{len(atoms_DF)}\n") @@ -456,7 +434,7 @@ def write_gro(file, atoms_DF, box, description): ) -def fibonacci_sphere(samples=1000): +def fibonacci_sphere(samples: int = 1000) -> NDArray: points = [] phi = np.pi * (np.sqrt(5.0) - 1.0) # golden angle in radians @@ -471,7 +449,7 @@ def fibonacci_sphere(samples=1000): return np.array(points) -def timing(function): +def timing(function: Callable) -> Callable: @functools.wraps(function) def wrap(*args, **kw): start_time = pytime() @@ -483,7 +461,8 @@ def timing(function): return wrap -def cleanup_h5(hdf5_file) -> None: + +def cleanup_h5(hdf5_file: str) -> None: hdf5_temp_file = f"{hdf5_file[:-3]}_temp.h5" run( [ @@ -496,4 +475,4 @@ def cleanup_h5(hdf5_file) -> None: hdf5_temp_file, ] ) - run(["mv", hdf5_temp_file, hdf5_file]) \ No newline at end of file + run(["mv", hdf5_temp_file, hdf5_file]) From 4ace8604365b6dbb7aee0576782a93d7e6993c54 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 14:10:30 +0100 Subject: [PATCH 47/73] Adjusted examples --- examples/plot_chi4.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_chi4.py b/examples/plot_chi4.py index 6ef3b22..cb02fcf 100644 --- a/examples/plot_chi4.py +++ b/examples/plot_chi4.py @@ -39,7 +39,7 @@ chi4 = len(OW[0]) * Fqt.var(axis=0) tudplot.activate() plt.plot(t, chi4, 'h', label=r'$\chi_4$') -plt.plot(t[2:-2], md.utils.runningmean(chi4, 5), '-', label='smoothed') +plt.plot(t[2:-2], md.utils.moving_average(chi4, 5), '-', label='smoothed') plt.semilogx() plt.xlabel('time / ps') From dffd06d3c0219bdaa181b648843e82db3b648961 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 14:16:07 +0100 Subject: [PATCH 48/73] Removed docs and example from repo since documentation will be done in the wiki --- doc/Makefile | 195 --------------------- doc/conf.py | 317 ----------------------------------- doc/contributing.rst | 244 --------------------------- doc/dynamic-evaluation.rst | 50 ------ doc/general-hints.rst | 80 --------- doc/guide.rst | 11 -- doc/index.rst | 29 ---- doc/installation.rst | 44 ----- doc/loading.rst | 115 ------------- doc/make.bat | 263 ----------------------------- doc/mdevaluate.rst | 83 --------- doc/modules.rst | 9 - doc/special-topics.rst | 12 -- doc/special/autosave.rst | 93 ---------- doc/special/energies.rst | 18 -- doc/special/overlap.rst | 76 --------- doc/special/spatial.rst | 38 ----- doc/static-evaluation.rst | 76 --------- examples/README.txt | 2 - examples/plot_chi4.py | 47 ------ examples/plot_isf.py | 30 ---- examples/plot_spatialisf.py | 121 ------------- examples/plot_temperature.py | 17 -- 23 files changed, 1970 deletions(-) delete mode 100644 doc/Makefile delete mode 100644 doc/conf.py delete mode 100644 doc/contributing.rst delete mode 100644 doc/dynamic-evaluation.rst delete mode 100644 doc/general-hints.rst delete mode 100644 doc/guide.rst delete mode 100644 doc/index.rst delete mode 100644 doc/installation.rst delete mode 100644 doc/loading.rst delete mode 100644 doc/make.bat delete mode 100644 doc/mdevaluate.rst delete mode 100644 doc/modules.rst delete mode 100644 doc/special-topics.rst delete mode 100644 doc/special/autosave.rst delete mode 100644 doc/special/energies.rst delete mode 100644 doc/special/overlap.rst delete mode 100644 doc/special/spatial.rst delete mode 100644 doc/static-evaluation.rst delete mode 100644 examples/README.txt delete mode 100644 examples/plot_chi4.py delete mode 100644 examples/plot_isf.py delete mode 100644 examples/plot_spatialisf.py delete mode 100644 examples/plot_temperature.py diff --git a/doc/Makefile b/doc/Makefile deleted file mode 100644 index d3e19e7..0000000 --- a/doc/Makefile +++ /dev/null @@ -1,195 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = _build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. 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If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.doctest', - 'sphinx.ext.mathjax', - 'sphinx.ext.viewcode', - 'sphinx.ext.napoleon', - 'sphinx.ext.intersphinx', - # 'sphinx.ext.autosummary', - # 'sphinx.ext.inheritance_diagram', - 'sphinx_gallery.gen_gallery', - 'sphinxcontrib.github_ribbon' -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = 'mdevaluate' -copyright = '2017, Niels Müller' -author = 'Niels Müller' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = mdevaluate.__version__ -# The full version, including alpha/beta/rc tags. -release = mdevaluate.__version__ - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['_build'] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' -highlight_language = "python3" - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -#html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -#html_logo = None - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -#html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -#html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -#html_additional_pages = {} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr' -#html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -#html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -#html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = 'mdevaluatedoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - #'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - #'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - #'preamble': '', - - # Latex figure (float) alignment - #'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'mdevaluate.tex', 'mdevaluate Documentation', - 'mbartelm', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'mdevaluate', 'mdevaluate Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'mdevaluate', 'mdevaluate Documentation', - author, 'mdevaluate', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False - -intersphinx_mapping = { - 'python': ('http://docs.python.org/3/', None), - 'numpy': ('http://docs.scipy.org/doc/numpy/', None), - 'ipython': ('http://ipython.org/ipython-doc/dev/', None), - 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), - } - -sphinx_gallery_conf = { - # path to your examples scripts - 'examples_dirs' : '../examples', - # path where to save gallery generated examples - 'gallery_dirs' : 'gallery'} - - -github_ribbon_repo = 'mdevaluate/mdevaluate' -github_ribbon_color = 'green' diff --git a/doc/contributing.rst b/doc/contributing.rst deleted file mode 100644 index 64f4ec2..0000000 --- a/doc/contributing.rst +++ /dev/null @@ -1,244 +0,0 @@ - -Contributing -============ - -This document aims to lay out the basics of contributing code to the ``mdevaluate`` package. -The code is managed through a git repository, hence this guides gives basic information on the usage of `git `_. -Int this document the prefix ``$`` indicates commands which should be ran on a shell. -For a brief 15 min interactive tutorial visit `try.github.org `_. - - -Let's start with a short introduction to the terminology. -Python code is organized in *packages* and *modules*: - -Modules: - Any python file (e.g. ``test.py``) is called a module. A module can be imported (``import test``) an then used - in other python code if in the python path, for example the working directory. - In principle, importing a package means executing the code inside the file. - All definitions, like variables or functions, are then available under the modules name. - -Packages: - Python modules can be grouped into packages. A python package is basically a folder, - which contains at least one mandatory file ``__init__.py``. This file is the entry - point into the module that is imported if the package is imported. - All modules in the folder are treated as submodules, which can be accessed via - a dot syntax, e.g. ``import package.test``. Packages can also contain sub packages. - -A more `detailed explanation `_ can be found in the official python documentation. - -Extending the documentation -+++++++++++++++++++++++++++ - -One of the most important parts of software is its documentation. -For modular packages like ``mdevaluate`` it's crucial to have a good coverage of the API, -since users need to know which functions are provided and how they are used. -To help others by extending the documentation is thereby a nice way of contributing to mdevaluate. - -The documentation is generated with a third party tools named `Sphinx `_. -The contents of the documentation are based on the source code (for the reference guide) -and documents written in the markup language *reStructuredText* (rst). -The source of every page can be viewed in the browser through the *View page source* link in the upper right of the page. -The name of the rst files can also be derived from the page URL. -The rst files are placed in the ``doc`` directory of the repository. - -Extending the documentation can be done in different ways, e.g. - -- Correct, clarify or extend existing sections -- Add new sections about the general use of mdevaluate -- Add use cases to the special topics section. - -To add a new sections to special topics, first create a new file for this guide in ``doc/special``. -Then add the name of this file (without the .rst extension) to the toctree in the file ``special-topics.rst``. -Now write the guide in the newly created file. - -Building the docs ------------------ - -When you have made changes to the docs, first re-build them locally. -You will need to have the ``sphinx`` python package installed and of course a working environment for ``mdevaluate``. -When those requirements are fulfilled build the docs by: - -1. Navigate to the ``doc`` directory -2. Run ``make html`` in the shell -3. View the produced html files in the browser: ``firefox _build/html/index.html`` - -Organization of the code -++++++++++++++++++++++++ - - - -The code for the evaluation software is organized in two python packages: - -- ``pygmx``: This package provides a python wrapper for the Gromacs library and - thereby functionality to read file formats used within Gromacs. -- ``mdevaluate``: This package provides functionality for evaluation of molecular - dynamics simulations. It uses the ``pygmx`` package to read files, but is - (in theory) not limited to Gromacs data. - -Submodules ----------- - -Below the content of the submodules of the package is described. - -atoms.py -........ - -Definition of the ``Atom`` class and related functions for atom selection and information. - -autosave.py -........... - -Experimental functionality for automatic saving and loading of evaluated data, -like correlation functions. For each function call a checksum is calculated -from the input, which changes if the output of the function changes. - -coordinates.py -.............. - -Definition of the ``Coordinates`` class and ``CoordinatesMap`` for coordinates -transformations and related functions. - -correlation.py -.............. - -Functionality to calculate correlation functions. - -distribution.py -............... - -Functionality to calculate distribution functions. - -reader.py -......... - -Defines reader classes that handle trajectory reading and caching. - -utils.py -........ - -A collection of utility functions. - -Set up a development environment -++++++++++++++++++++++++++++++++ - -.. code-block:: console - - $ git clone https://github.com/mdevaluate/mdevaluate.git - -Organization of the repository ------------------------------- - -The repository is organized through git branches. -At the moment there exist two branches in the remote repository: *master* and *dev*. - - -Adding code to the repository -+++++++++++++++++++++++++++++ - -All changes to the code are done in your local clone of the repository. -If a feature is complete, or at least works, the code can be pushed to the remote, -to make it accessible for others. - -A standard work flow to submit new code is the following - -1. Fork the main repository o github and clone your fork to your local machine. -2. Create a new branch locally and apply the desired changes. -3. If the master branch was updated, merge it into the local branch. -4. Push the changes to github and create a pull request for your fork. - -Pulling updates from remote ---------------------------- - -Before working with the code, the latest updates should be pulled for the master branch - -.. code-block:: console - - $ git checkout master - $ git pull - -Create a new branch -------------------- - -Before changing any code, create a new branch in your local repository. -This helps to keep an overview of all the changes and simplifies merging. -To create a new branch locally enter the following commands - -.. code-block:: console - - $ git checkout master - $ git branch my-feature - $ git checkout my-feature - -First switch to the master branch to make sure the new branch is based on it. -Then create the new branch, called `my-feature` and switch to it. -Now you can start making changes in the code. - -Committing changes ------------------- - -A bundle of changes in the code is called a *commit*. -These changes can happen in different files and should be associated with each other. -Let's assume, two files have been changed (``atoms.py`` and ``utils.py``). -The command - -.. code-block:: console - - $ git diff atoms.py - -will show you all changes that were made in the file since the latest commit. -Before committing changes have to be *staged*, which is done by - -.. code-block:: console - - $ git add atoms.py utils.py - -This my be repeated as often as necessary. -When all changes for a commit are staged, it can actually be created - -.. code-block:: console - - $ git commit - -This will open up an editor where a commit message has to be entered. -After writing the commit message, save & close the file, which will create the commit. - -Create Pullrequest ------------------- - -When all changes are made and the new feature should be made public, you can open a new pull request on github. -Most of the time, the master branch will have been updated, so first pull any updates - -.. code-block:: console - - $ git checkout master - $ git pull - -When the master branch is up to date, it can be merged into the feature branch - -.. code-block:: console - - $ git checkout my-feature - $ git merge master - -If no conflicting changes were made, merging works automatically. -If for example the same line was modified in a commit in master and your commits, a merge conflict will occur. -Git tells you which files have conflicts and asks you to resolve these. -The respective lines will be marked with conflict-resolution markers in the files. -The most basic way of resolving a conflict is by editing these files and choosing the appropriate version of the code. -See the `git documentation `_ for an explanation. -After resolving the conflict, the files need to be staged and the merge has to be committed - -.. code-block:: console - - $ git add utils.py - $ git commit - -The commit message will be generated automatically, indicating the merge. - -After merging, the changes can be pushed to the remote - -.. code-block:: console - - $ git push - -The new code is now available in the remote. diff --git a/doc/dynamic-evaluation.rst b/doc/dynamic-evaluation.rst deleted file mode 100644 index 414e9eb..0000000 --- a/doc/dynamic-evaluation.rst +++ /dev/null @@ -1,50 +0,0 @@ -Evaluation of dynamic properties -================================ - -Dynamic properties like mean square displacement are calculated with the -function :func:`mdevaluate.correlation.shifted_correlation`. -This function takes a correlation function and calculates the averaged -time series of it, by shifting a time interval over the trajectory. - -:: - - from mdevaluate import correlation - - time, msd_amim = correlation.shifted_correlation(correlation.msd, com_amim, average=True) - plot(time,msd_amim) - -The result of :func:`shifted_correlation` are two lists, the first one (``time``) -contains the times of the frames that have been used for the correlation. -The second list ``msd_amim`` is the correlation function at these times. -If the keyword ``average=False`` is given, the correlation function for each shifted -time window will be returned. - -Arguments of ``shifted_correlation`` ------------------------------------- - -The function :func:`mdevaluate.correlation.shifted_correlation` accepts several keyword arguments. -With those arguments, the calculation of the correlation function may be controlled in detail. -The mathematical expression for a correlation function is the following: - -.. math:: S(t) = \frac{1}{N} \sum_{i=1}^N C(f, R, t_i, t) - -Here :math:`S(t)` denotes the correlation function at time t, :math:`R` are the coordinates of all atoms -and :math:`t_i` are the onset times (:math:`N` is the number of onset times or time windows). -Note that the outer sum and division by :math:`N` is only carried out if ``average=True``. -The onset times are defined by the keywords ``segments`` and ``window``, with -:math:`N = segments` and :math:`t_i = \frac{ (1 - window) \cdot t_{max}}{N} (i - 1)` with the total simulation time :math:`t_{max}`. -As can be seen ``segments`` gives the number of onset times and ``window`` defines the part of the simulation time the correlation is calculated for, -hence ``window - 1`` is the part of the simulation the onset times a distributed over. - - -:math:`C(f, R, t_0, t)` is the function that actually correlates the function :math:`f`. -For standard correlations the functions :math:`C(...)` and :math:`f` are defined as: - -.. math:: C(f, R, t_0, t) = f(R(t_0), R(t_0 + t)) - -.. math:: f(r_0, r) = \langle s(r_0, r) \rangle - -Here the brackets denote an ensemble average, small :math:`r` are coordinates of one frame and :math:`s(r_0, r)` is the value that is correlated, -e.g. for the MSD :math:`s(r_0, r) = (r - r_0)^2`. - -The function :math:`C(f, R, t_0, t)` is specified by the keyword ``correlation``, the function :math:`f(r_0, r)` is given by ``function``. diff --git a/doc/general-hints.rst b/doc/general-hints.rst deleted file mode 100644 index 4a06bec..0000000 --- a/doc/general-hints.rst +++ /dev/null @@ -1,80 +0,0 @@ -General Hints for Python Programming -==================================== - -This page collects some general hints for data centered programming with Python. -Some resources for tutorials on the topics can be found here: - -* http://www.scipy-lectures.org/ -* The `Python Data Science Handbook `_, by Jake VanderPlas -* PyCon-Talk on Numpy arrays: `Losing Your Loops, by Jake VanderPlas `_ - -Programming Environments ------------------------- - -There exist different environments for Python programming, each with their pros and cons. -Some examples are: - -* **IPython Console**: The most basic way to use Python is on the interactive console, the ipython console is a suffisticated Python console. After the mdevaluate module is loaded, ipython can be started with the command ``ipython``. -* **Jupyter Notebook**: Provides a Mathematica-style notebook, which is accesed through a web browser. After the mdevaluate module is loaded a (local) notebook server can be started with the command ``jupyter-notebook``. See the help menu in the notebook for a short introduction and http://jupyter.org/ for a detailed user guide. -* **Atom Editor**: When developing more complex code, like modules an editor comes in handy. Besides basic preinstalled editors (e.g. Gedit) the `atom editor `_ is a nice option. Recommended atpm packages for Python development are: language-python, autocomplete-python and linter-flake8. - -Common Pitfalls ---------------- - -* **For-Loops**: The biggest pitfall of data-intensive Python programming are ``for``-loops. Those loops perform bad in Python, but can be avoided in most cases through Numpy arrays, see the mentioned talk by Jake VdP. -* **Non-Portable Code**: Most non-programmers tend to write complex scripts. It's always advisable to source out your code into seperate Python modules (i.e. seperate files) and split the code into reusable functions. Since these modules can be imported from any Python code, this will save time in the long run and often reduces errors. - - -Pandas Dataframes ------------------ - -Most data in Mdevaluate is handled as Numpy arrays. -For example the function :func:`~mdevaluate.correlation.shifted_correlation` returns a multidimensional array, which contains the time steps and the value of the correlation function. -As pointed out above, those arrays a good for computation and can be used to plot data with, e.g. matplotlib. -But often there is metadata associated with this data, for example the temperature or the specific subset of atoms that were analyzed. -This is the point where **`Pandas dataframes `_** come in handy. -Dataframes are most basically tables of samples, with named columns. -The dataframe class allows easy acces of columns by label and complex operations, like grouping by columns or merging different datasets. - -As an example say we have simulations at some temperatures and want to calculate the ISF and do a KWW-Fit for each of these trajectories. -Details of the analysis will be explained at a later point of this document, thereby they will be omitted here:: - - import pandas - datasets = [] - - for T in [250, 260, 270, 280, 290, 300]: - # calculate the isf for this temperature - t, Sqt = ... - - # DataFrames can be created from dictionaries - datasets.append(pandas.DataFrame({'time': t, 'Sqt': Sqt, 'T': T})) - - # join the individual dataframes into one - isf_data = pandas.concat(datasets) - - # Now calculate the KWW fits for each temperature - from scipy.optimize import curve_fit - from mdevaluate.functions import kww - kww_datasets = [] - # The isf data is grouped by temperature, - # that is the loop iterates over all T values and the part of the data where isf_data['T'] == T - for T, data in isf_data.groupby('T'): - fit, cuv = curve_fit(kww, data['time'], data['Sqt']) - # DataFrames can also be cerated from arrays and a defintion of columns - df = pandas.DataFrame(fit, columns=['A', 'τ', 'β']) - # columns can be added dynamically - df['T'] = T - kww_datasets.append(df) - kww_data = pandas.concat(kww_datasets) - - # We have two dataframes now, one with time series of the ISF at each temperature - # and one with the fit parameters of the KWW for each temperature - - # We can merge this data into one dataframe, by the overlapping columns (i.e. 'T' in this example) - data = pandas.merge(isf_data, kww_data) - # We can now compute the kww fit value of each sample point of the isf in one line: - data['kww_fit'] = kww(data['time'], data['A'], data['τ'], data['β']) - # And plot the data, resolved by temperature. - for T, df in data.groupby('T'): - plot(df['time'], df['Sqt'], 'o') # The actual correlation value - plot(df['time'], df['kww_fit'], '-') # The kww fit diff --git a/doc/guide.rst b/doc/guide.rst deleted file mode 100644 index d095f07..0000000 --- a/doc/guide.rst +++ /dev/null @@ -1,11 +0,0 @@ - -User Guide -========== - -.. toctree:: - :maxdepth: 2 - - loading - static-evaluation - dynamic-evaluation - special-topics diff --git a/doc/index.rst b/doc/index.rst deleted file mode 100644 index d5124db..0000000 --- a/doc/index.rst +++ /dev/null @@ -1,29 +0,0 @@ -.. mdevaluate documentation master file, created by - sphinx-quickstart on Tue Nov 10 11:46:41 2015. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Documentation of mdevaluate -=========================== - -A python package for evaluation of molecular dynamics simulation data. - -Contents --------- - -.. toctree:: - :maxdepth: 1 - - installation - general-hints - guide - gallery/index - contributing - modules - -Indices and tables ------------------- - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/doc/installation.rst b/doc/installation.rst deleted file mode 100644 index 320262e..0000000 --- a/doc/installation.rst +++ /dev/null @@ -1,44 +0,0 @@ -Installation -============ - -Mdevaluate itself is a pure Python package and can be imported directly from the source directory, if needed. -The Gromacs dependency pygmx has to be installed into the Python distribution, -since parts are compiled with Cython. - -Requirements ------------- - -The package depends on some python packages that can all be installed via pip or conda: - -- Python 3.5 (or higher) -- NumPy -- SciPy - - -Install pygmx & mdevaluate ---------------------------- - -To instal pygmx, first get the source from its repository, https://github.com/mdevaluate/pygmx. -Installation instructions are given in the respective readme file. -Two steps have to be performed: - -1. Install Gromacs 2016 -2. Install pygmx - -When this requirement is met, installing mdevaluate simply means getting the source code from the repository and running - - python setup.py install - -form within the source directory. - - -Running Tests -------------- - -Some tests are included with the source that can be used too test the installation. -The testsuite requires `pytest `_. -To run the test simply execute - - pytest - -in the source directory. \ No newline at end of file diff --git a/doc/loading.rst b/doc/loading.rst deleted file mode 100644 index 8bed8f6..0000000 --- a/doc/loading.rst +++ /dev/null @@ -1,115 +0,0 @@ -Loading of simulation data -========================== - -Mdevaulate provides a convenient function :func:`mdevaluate.load_simulation` -that loads a simulation more or less automatically. -It takes a path as input and looks for all files it needs in this directory. - -For information about the topology either a `tpr` or `gro` a file is read, -where the former is the preferred choice. -Trajectory data will be read from a xtc file. -If the directory contains more than one file of any type, the desired file -has to be specified with the appropriate keyword argument. -For details see :func:`mdevaluate.open`. - -The function will return a coordinates object, for the whole system. -A subset of the system may be obtained directly from the coordinates object by -calling its :func:`~mdevaluate.coordinates.Coordinates.subset` method. -This function accepts the same input as :func:`mdevaluate.atoms.AtomSubset.subset`. -A new feature that was introduced in the function is the possibility to chose -atoms with regular expressions. - -Example -------- - -The following code loads the example trajectory and selects a subset of all CW atoms. -Since there are two CW atoms in each molecule (CW1 and CW2) a regular expression is -used when selecting the subset. - -:: - - import mdevaluate as md - - trajectory = md.open('/data/niels/tutorial') - CW_atoms = trajectory.subset(atom_name='CW.') - -And that's it, now one can evaluate stuff for this subset of atoms. - -Selecting a subset ------------------- - -As shown in the example above it is often necessary to select a subset of the system for analysis. -This can be a special group of atoms (e.g. all C atoms) or a whole residue for which the center of mass should be computed. -Subsets are selected with the :func:`~mdevaluate.Coordinates.subset` method of Coordinates objects. - -This method accepts four keyword arguments, with which the atom name, residue name and residue id or atom indices can be specified. -The former two name arguments accept a regular expression which allows two include several different names in one subset. -Some examples: - -- All carbon atoms (which are named CW1, CT1, CA, ...): ``tr.subset(atom_name='C.*')`` -- Atoms NA1, NA2 and OW: ``tr.subset(atom_name='NA.|OW')`` -- All oxygen atoms of residue EG: ``tr.subset(atom_name='O.*', residue_name='EG')`` - - -Specifying data files ---------------------- - -The above example only works if the directory contains exactly one tpr file and -one xtc file. -If your data files are located in subdirectories or multiple files of these types exist, -they can be specified by the keywords ``topology`` and ``trajectory``. -Those filenames can be a relative path to the simulation directory and can also make -use of *shell globing*. For example:: - - traj = md.open('/path/to/sim', topology='atoms.gro', trajectory='out/traj_*.xtc') - -Note that the topology can be specified as a gro file, with the limitation that -only atom and residue names will be read from those files. -Information about atom masses and charges for example will only be read from tpr files, -therefore it is generally recommended to use the latter topologies. - -The trajectory above is specified through a shell globing, meaning the ``*`` may be -expanded to any string (without containing a forward slash). -If more than one file exists which match this pattern an error will be raised, -since the trajectory can not be identified clearly. - -Caching of frames ------------------ - -One bottleneck in the analysis of MD data is the reading speed of the trajectory. -In many cases frames will be needed repeatedly and hence the amount of time spend reading -data from disk (or worse over the network) is huge. -Therefore the mdevaluate package implements a simple caching mechanism, which holds -on to a number of read frames. -The downside if this is increased memory usage which may slow down the computation too. - -Caching is done on the level of the trajectory readers, so that all ``Coordinate`` and -``CoordinateMap`` objects working on the same trajectory will be sharing a cache. -Caching has to be activated when opening a trajectory:: - - traj = md.open('/path/to/sim', cached=True) - -The ``cached`` keyword takes either a boolean, a integer or None as input value. -The value of ``cached`` controls the size of the cache and thereby the additional memory usage. -Setting it to True will activate the caching with a maximum size of 128 frames, -with an integer any other maximum size may be set. -The special value ``None`` will set the cache size to infinite, so all frames will be cached. -This will prevent the frames from being loaded twice but can also consume a whole lot of memory, -since a single frame can easily take 1 MB of memory. - -Clearing cached frames -++++++++++++++++++++++ - -In some scenarios it may be advisable to free cached frames which are no longer needed. -For this case the reader has a function ``clear_cache()``. -The current state of the cache can be displayed with the ``cache_info`` property:: - - >>> traj.frames.cache_info - CacheInfo(hits=12, misses=20, maxsize=128, currsize=20) - >>> traj.frames.clear_cache() - >>> traj.frames.cache_info - CacheInfo(hits=0, misses=0, maxsize=128, currsize=0) - -Clearing the cache when it is not needed anymore is advisable since this will help the -Python interpreter to reuse the memory. - diff --git a/doc/make.bat b/doc/make.bat deleted file mode 100644 index 726476a..0000000 --- a/doc/make.bat +++ /dev/null @@ -1,263 +0,0 @@ -@ECHO OFF - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set BUILDDIR=_build -set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . -set I18NSPHINXOPTS=%SPHINXOPTS% . -if NOT "%PAPER%" == "" ( - set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% - set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% -) - -if "%1" == "" goto help - -if "%1" == "help" ( - :help - echo.Please use `make ^` where ^ is one of - echo. html to make standalone HTML files - echo. dirhtml to make HTML files named index.html in directories - echo. singlehtml to make a single large HTML file - echo. pickle to make pickle files - echo. json to make JSON files - echo. htmlhelp to make HTML files and a HTML help project - echo. qthelp to make HTML files and a qthelp project - echo. devhelp to make HTML files and a Devhelp project - echo. epub to make an epub - echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter - echo. text to make text files - echo. man to make manual pages - echo. texinfo to make Texinfo files - echo. gettext to make PO message catalogs - echo. changes to make an overview over all changed/added/deprecated items - echo. xml to make Docutils-native XML files - echo. pseudoxml to make pseudoxml-XML files for display purposes - echo. linkcheck to check all external links for integrity - echo. doctest to run all doctests embedded in the documentation if enabled - echo. coverage to run coverage check of the documentation if enabled - goto end -) - -if "%1" == "clean" ( - for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i - del /q /s %BUILDDIR%\* - goto end -) - - -REM Check if sphinx-build is available and fallback to Python version if any -%SPHINXBUILD% 2> nul -if errorlevel 9009 goto sphinx_python -goto sphinx_ok - -:sphinx_python - -set SPHINXBUILD=python -m sphinx.__init__ -%SPHINXBUILD% 2> nul -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. 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The XML files are in %BUILDDIR%/xml. - goto end -) - -if "%1" == "pseudoxml" ( - %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml - if errorlevel 1 exit /b 1 - echo. - echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. - goto end -) - -:end diff --git a/doc/mdevaluate.rst b/doc/mdevaluate.rst deleted file mode 100644 index dff2077..0000000 --- a/doc/mdevaluate.rst +++ /dev/null @@ -1,83 +0,0 @@ - -Module contents ---------------- - -.. automodule:: mdevaluate - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.autosave -................... - -.. automodule:: mdevaluate.autosave - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.atoms -................ - -.. automodule:: mdevaluate.atoms - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.coordinates -...................... - -.. automodule:: mdevaluate.coordinates - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.correlation -...................... - -.. automodule:: mdevaluate.correlation - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.distribution -....................... - -.. automodule:: mdevaluate.distribution - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.evaluation -..................... - -mdevaluate.functions -.................... - -.. automodule:: mdevaluate.functions - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.pbc -.............. - -.. automodule:: mdevaluate.pbc - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.reader -..................... - -.. automodule:: mdevaluate.reader - :members: - :undoc-members: - :show-inheritance: - -mdevaluate.utils -..................... - -.. automodule:: mdevaluate.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/doc/modules.rst b/doc/modules.rst deleted file mode 100644 index eca92b9..0000000 --- a/doc/modules.rst +++ /dev/null @@ -1,9 +0,0 @@ -.. _reference-guide: - -Reference Guide -=============== - -.. toctree:: - :maxdepth: 4 - - mdevaluate diff --git a/doc/special-topics.rst b/doc/special-topics.rst deleted file mode 100644 index a889e96..0000000 --- a/doc/special-topics.rst +++ /dev/null @@ -1,12 +0,0 @@ - -Special Topics -============== - -This part of the documentation describes advanced ways of the use of mdevaluate. - -.. toctree:: - - special/autosave - special/spatial - special/overlap - special/energies diff --git a/doc/special/autosave.rst b/doc/special/autosave.rst deleted file mode 100644 index 592d1a0..0000000 --- a/doc/special/autosave.rst +++ /dev/null @@ -1,93 +0,0 @@ -Automatic Saving of Analysis Data -================================= - -Mdevaluate provides a functionality to save the result of analysis functions automatically. -The data is saved to a file after it was computed. -If an analysis was done in the exact same way before, the result is loaded from this file. - -This function may be activated through calling :func:`mdevaluate.autosave.enable`, which takes a directory as input. -If this directory is a relative path (e.g. no trailing slash) the results will be saved in a location -relative to the directory of the trajectory file. -If the output files of your simulations are located in a subdirectory, like ``/path/to/sim/Output`` it is possible -to specify the auto save location as ``../data`` such that the result files will be placed under ``/path/to/sim/data``. - -At the moment the two functions which use this behavior are: - -- :func:`~mdevaluate.correlation.shifted_correlation` -- :func:`~mdevaluate.distribution.time_average` - -Any other function can make use of the autosave mechanism by decorating it with :func:`mdevaluate.autosave.autosave_data`. - -A full example --------------- - -This is how it works, for a detailed explanation see below:: - - import mdevaluate as md - md.autosave.enable('data') - water = md.open('/path/to/sim').subset(atom_name='OW') - md.correlation.shifted_correlation( - md.correlation.msd, - water, - description='test' - ) - # The result will be saved to the file: - # /path/to/sim/data/shifted_correlation_msd_OW_test.npz - -Checksum of the Analysis Call ------------------------------ - -The autosave module calculates a checksum for each call of an analysis function, -which is used to validate a present the data file. -This way the result should only be loaded from file if the analysis is exactly the same. -This includes the function code that is evaluated, so the result will be recomputed if any bit of the code changes. -But there is always the possibility that checksums coincide accidentally, -by chance or due to a bug in the code, which should be kept in mind when using this functionality. - -Special Keyword Arguments -------------------------- - -The autosave module introduces two special keyword arguments to the decorated functions: - -- ``autoload``: This prevents the loading of previously calculated data even if a valid file was found. -- ``description``: A descriptive string of the specific analysis, see below. - -Those keywords may be passed to those function (shifted_correlation, time_average) like any other keyword argument. -If autosave was not enabled, they will be ignored. - -File names and Analysis Descriptions ------------------------------------- - -The evaluated data is saved to human readable files, whose name is derived from the function call -and the automatic description of the subset. -The latter one is assigned based on the ``atom_name`` and ``residue_name`` of the :func:`~mdevaluate.atoms.AtomSubset.subset` method. - -In some cases this is not enough, for example if the same subset is analyzed spatially resolved, -which would lead to identical filenames that would be overwritten. -Therefore a more detailed description of each specific analysis call needs to be provided. -For this reason the autosave module introduces the before mentioned keyword argument ``description``. -The value of this keyword is appended to the filename and in addition if any of -the other arguments of the function call has a attribute description, this will appended as well. -For example this (pseudo) code will lead to the filename ``shifted_correlation_isf_OW_1-2nm_nice.npz``:: - - OW = traj.subset(atom_name='OW') - - corr = subensemble_correlation(spatial_selector) - corr.description = '1-2nm' - - shifted_correlation( - isf, - OW, - correlation=corr, - description='nice' - ) - - -Reusing the autosaved data --------------------------- - -The results of the functions are saved in NumPy's npz format, see :func:`numpy.savez`. -If the result should be used in a different place, it can either be loaded with -:func:`numpy.load` or :func:`mdevaluate.autosave.load_data`. -The latter function will return the result of the function call directly, the former -returns a dict with the keys ``checksum`` and ``data``, the latter yielding the results data. diff --git a/doc/special/energies.rst b/doc/special/energies.rst deleted file mode 100644 index 370f835..0000000 --- a/doc/special/energies.rst +++ /dev/null @@ -1,18 +0,0 @@ -Gromacs Energy Files -==================== - -It is possible to read the energy files (.edr) written out by Gromacs with mdevaluate. -Those files contain thermodynamic properties of the system, like temperature or pressure. -The exact contents of an energy file depend on the type of ensemble that was simulated, -an NVT simulation's energy file for example will not contain information about the box size. - -To open these files use the function :func:`mdevaluate.open_energy`, which takes the filename of an energy file. -The types of energies stored in the file can be shown with the :attr:`types` attribute of the class :class:`~mdevaluate.reader.EnergyReader`, -the :attr:`units` attribute gives the units of these energy types. -The timesteps at which those energies were written out are accessible through the :attr:`~mdevaluate.reader.EnergyReader.time` property. -The time series of one of these energies can be accessed through the named index, comparable to python dictionaries. -:: - import mdevaluate as md - edr = md.open_energy('/path/to/energy.edr') - # plot the evolution of temperature - plot(edr.time, edr['Temperature']) diff --git a/doc/special/overlap.rst b/doc/special/overlap.rst deleted file mode 100644 index 8f4f9c6..0000000 --- a/doc/special/overlap.rst +++ /dev/null @@ -1,76 +0,0 @@ -Computing the Overlap Function -============================== - -The overlap function is defined as the portion of particles of a given set, -whose positions *overlap* after a given time :math:`t` with the reference configuration at :math:`t=0`. -This is calculated as follows: -The Initial positions define spheres of a given radius :math:`r` which then are used -to test how many of the particles at a later time are found within those spheres. -Normalized by the number of spheres this gives the correlation of the configurational overlap. - -.. math:: - - Q(t) = \frac{1}{N} \left\langle \sum\limits_{i=1}^N n_i(t) \right\rangle - -Where :math:`n_i(t)` defines the :math:`N` spheres, with :math:`n_i(t)=1` if a particle -is found within this sphere at time :math:`t` and :math:`n_i(0) = 1` for :math:`1\leq i \leq N`. - -Evaluation with mdevaluate --------------------------- - -Computation of the overlap requires the relatively expensive computation of next neighbor distances, -which scales with the order of :math:`\mathcal{O}(N^2)`. -There are more efficient ways for the solution of this problem, the one used here is -the so called :class:`~scipy.spatial.cKDTree`. -This is much more efficient and allows to compute the overlap relatively fast:: - - OW = md.open('/path/to/sim').subset(atom_name='OW') - tree = md.coordinates.CoordinatesKDTree(OW) - Qol = md.correlation.shifted_correlation( - partial(md.correlation.overlap, crds_tree=tree, radius=0.11), - OW - ) - -As seen above, mdevaluate provides the function :func:`~mdevaluate.correlation.overlap` -for this evaluation, which uses a special object of type :class:`~mdevaluate.coordinates.CoordinatesKDTree` -for the neighbor search. -The latter provides two features, necessary for the computation: -First it computes a :class:`~scipy.spatial.cKDTree` for each necessary frame of the trajectory; -second it caches those trees, since assembly of KDTrees is expensive. -The size of the cache can be controlled with the keyword argument ``maxsize`` of the CoordinatesKDTree initialization. - -Note that this class uses the C version (hence the lowercase C) rather than -the pure Python version :class:`~scipy.spatial.KDTree` since the latter is significantly slower. -The only downside is, that the C version had a memory leak before SciPy 0.17, -but as long as a recent version of SciPy is used, this shouldn't be a problem. - -Overlap of a Subsystem ----------------------- - -In many cases the overlap of a subsystem, e.g. a spatial region, should be computed. -This is done by selecting a subset of the initial configuration before defining the spheres. -The overlap is then probed with the whole system. -This has two benefits: - -1. It yields the correct results -2. The KDTree structures are smaller and thereby less computation and memory expensive - -An example of a spatial resolved analysis, where ``OW`` is loaded as above:: - - selector = partial( - md.coordinates.spatial_selector, - transform=md.coordinates.spherical_radius, - rmin=1.0, - rmax=1.5 - ) - tree = md.coordinates.CoordinatesKDTree(OW, selector=selector) - Qol = md.correlation.shifted_correlation( - partial(md.correlation.overlap, crds_tree=tree, radius=0.11), - OW - ) - -This computes the overlap of OW atoms in the region :math:`1.0 \leq r \leq 1.5`. -This method can of course be used to probe the overlap of any subsystem, which is selected by the given selector function. -It should return a viable index for a (m, 3) sized NumPy array when called with original frame of size (N, 3):: - - subset = frame[selector(frame)] diff --git a/doc/special/spatial.rst b/doc/special/spatial.rst deleted file mode 100644 index 66e66f3..0000000 --- a/doc/special/spatial.rst +++ /dev/null @@ -1,38 +0,0 @@ -Spatial Resolved Analysis -========================= - -This section describes how spatially resolved correlation can be analyzed with mdevaluate. -This guide assumes that the variable ``traj`` holds a trajectory where the subset of atoms that should be analyzed are selected. -For example:: - - traj = md.open('/path/to/sim', cached=1000).subset(atom_name='OW') - -Which would load a simulation from the directory ``/path/to/sim`` and select all ``OW`` atoms. -Note that for this use case, the caching is quite useful since it enables us to iterate over spatial regions -without significant time penalty. -Moving on let's calculate the ISF of water oxygens with spherical radius between 0.5 and 0.7 nm:: - - from functools import partial - func = partial(md.correlation.isf, q=22.7) - selector = partial( - md.coordinates.spatial_selector, - transform=md.coordinates.spherical_radius, - rmin=0.5, rmax=0.7 - ) - t, S = md.correlation.shifted_correlation( - func, traj, - correlation=md.correlation.subensemble_correlation(selector) - ) - -To explain how this works, let's go through the code from bottom to top. -The spatial filtering is done inside the shifted_correlation by the function -:func:`mdevaluate.correlation.subensemble_correlation`. -This function takes a selector function as argument that should take a frame as input -and return the selection of the coordinates that should be selected. -A new selection is taken for the starting frame of each shifted time segment. - -In this case the selection is done with the function :func:`mdevaluate.coordinates.spatial_selector`. -This function takes four arguments, the first being the frame of coordinates which is handed by :func:`subensemble_correlation`. -The second argument is a transformation function, which transforms the input coordinates to the coordinate which will be filtered, -in this case the spherical radius. -The two last arguments define the minimum and maximum value of this quantity. diff --git a/doc/static-evaluation.rst b/doc/static-evaluation.rst deleted file mode 100644 index 5874a41..0000000 --- a/doc/static-evaluation.rst +++ /dev/null @@ -1,76 +0,0 @@ - -Evaluation of static properties -=============================== - -.. note:: - All examples in this section assume, that the packages has been imported and a trajectory was loaded:: - - import mdevaluate.distribution as dist - - coords = mdevaluate.open('/path/to/simulation') - -Static properties of the system, like density distribution or pair correlation function, -can be evaluated with the :mod:`mdevaluate.distribution` module. -It provides the function :func:`mdevaluate.distribution.time_average` -that computes the average of a property over the whole trajectory. -An example call of this function is:: - - tetra = dist.time_average(dist.tetrahedral_order, coords) - -This will calculate the average of the tetrahedral order parameter for each atom. -The first argument of :func:`time_average` is a function that takes one argument. -It will be called for each frame in the trajectory and the output of this function -is than averaged over all these frames. - -Slicing of the trajectory -------------------------- - -In most cases averaging each frame of the trajectory is not necessary, -since the conformation of the atoms doesn't change significantly between two frames. -Hence it is sufficient to skip some frames without suffering significant statistics. -The exact amount of frames which can be skipped before the statistics suffer depends strongly -on the calculated property, therefore it has to be chosen manually. -For this purpose the Coordinates objects can be sliced like any python list:: - - tetra = dist.time_average(dist.tetrahedral_order, coords[1000::50]) - -This makes it possible to skip a number of frames at the start (or end) and with every step. -The above call would start with frame 1000 of the trajectory and evaluate each 50th frame until the end. -Since the number of frames read and evaluated is reduced by about a factor of 50, the computational cost will decrease accordingly. - -Calculating distributions -------------------------- - -In many cases the static distributions of a property is of interest. -For example, the tetrahedral order parameter is often wanted as a distribution. -This can too be calculated with ``time_average`` but the bins of the distribution have to be specified:: - - from functools import partial - func = partial(dist.tetrahedral_order_distribution, bins=np.linspace(-3, 1, 401) - tetra_dist = dist.time_average(func, coords) - -The bins (which are ultimately used with the function :func:`numpy.histogram`) are specified -by partially evaluating the evaluation function with :func:`functools.partial`. -See the documentation of :func:`numpy.histogram` for details on bin specification. - -.. note:: - If :func:`numpy.histogram` is used with :func:`time_average` the bins have to be given explicitly. - When not specified, the bins will be chosen automatically for each call of ``histogram`` leading to - different bins for each frame, hence an incorrect average. - -Advanced evaluations --------------------- - -The function that will be evaluated by ``time_average`` can return numpy arrays of arbitrary shape. -It is for example possible to calculate the distribution of a property for several subsets of the system at once:: - - def subset_tetra(frame, bins): - tetra = dist.tetrahedral_order(frame) - return array([np.histogram(tetra[0::2], bins=bins), - np.histogram(tetra[1::2], bins=bins),]) - - func = partial(subset, bins=np.linspace(-1,1,201)) - tetra_subdist = dist.time_average(func, coords) - -In this example the tetrahedral order parameter is first calculated for each atom of the system. -Then the distribution is calculated for two subsets, containing atoms (0, 2, 4, 6, ...) and (1, 3, 5, 7, ...). diff --git a/examples/README.txt b/examples/README.txt deleted file mode 100644 index e2d4fcf..0000000 --- a/examples/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Example Gallery -=============== diff --git a/examples/plot_chi4.py b/examples/plot_chi4.py deleted file mode 100644 index cb02fcf..0000000 --- a/examples/plot_chi4.py +++ /dev/null @@ -1,47 +0,0 @@ -r""" -Four-Point susceptibility -========================= - -The dynamic four-point susceptibility :math:`\chi_4(t)` is a measure for heterogenous dynamics. [Berthier]_ -It can be calculated from the variance of the incoherent intermediate scattering function -:math:`F_q(t)`. - -.. math:: - \chi_4 (t) = N\cdot\left( \left\langle F_q^2(t) \right\rangle - \left\langle F_q(t) \right\rangle^2 \right) - -This is astraight forward calculation in mdevaluate. -First calculate the ISF without time average and then take the variance along the first axis of this data. -Note that this quantity requires good statistics, hence it is adviced to use a small time window -and a sufficient number of segments for the analysis. -Another way to reduce scatter is to smooth the data with a running mean, -calling :func:`~mdevaluate.utils.runningmean` as shown below. - -.. [Berthier] http://link.aps.org/doi/10.1103/Physics.4.42 -""" - -from functools import partial -import matplotlib.pyplot as plt -import mdevaluate as md -import tudplot - -OW = md.open('/data/niels/sim/water/bulk/260K', trajectory='out/*.xtc').subset(atom_name='OW') - -t, Fqt = md.correlation.shifted_correlation( - partial(md.correlation.isf, q=22.7), - OW, - average=False, - window=0.2, - skip=0.1, - segments=20 -) -chi4 = len(OW[0]) * Fqt.var(axis=0) - -tudplot.activate() - -plt.plot(t, chi4, 'h', label=r'$\chi_4$') -plt.plot(t[2:-2], md.utils.moving_average(chi4, 5), '-', label='smoothed') - -plt.semilogx() -plt.xlabel('time / ps') -plt.ylabel('$\\chi_4$') -plt.legend(loc='best') diff --git a/examples/plot_isf.py b/examples/plot_isf.py deleted file mode 100644 index 322335f..0000000 --- a/examples/plot_isf.py +++ /dev/null @@ -1,30 +0,0 @@ -""" -Calculating the ISF of Water -======================================================= - -In this example the ISF of water oxygens is calculated for a bulk simulation. -Additionally a KWW function is fitted to the results. -""" -from functools import partial -import matplotlib.pyplot as plt -from scipy.optimize import curve_fit -import mdevaluate as md -import tudplot - -OW = md.open('/data/niels/sim/water/bulk/260K', trajectory='out/*.xtc').subset(atom_name='OW') -t, S = md.correlation.shifted_correlation( - partial(md.correlation.isf, q=22.7), - OW, - average=True -) -# Only include data-points of the alpha-relaxation for the fit -mask = t > 3e-1 -fit, cov = curve_fit(md.functions.kww, t[mask], S[mask]) -tau = md.functions.kww_1e(*fit) - -tudplot.activate() -plt.figure() -plt.plot(t, S, '.', label='ISF of Bulk Water') -plt.plot(t, md.functions.kww(t, *fit), '-', label=r'KWW, $\tau$={:.2f}ps'.format(tau)) -plt.xscale('log') -plt.legend() diff --git a/examples/plot_spatialisf.py b/examples/plot_spatialisf.py deleted file mode 100644 index 3f7e6b1..0000000 --- a/examples/plot_spatialisf.py +++ /dev/null @@ -1,121 +0,0 @@ -""" -Spatially resolved analysis in a cylindrical pore -======================================================= - -Calculate the spatially resolved ISF inside a cylindrical neutral water pore -In this case the bins describe the shortest distance of an oxygen atom to any wall atom -""" - -import numpy as np -import matplotlib.pyplot as plt -import mdevaluate as md -import tudplot -from scipy import spatial -from scipy.optimize import curve_fit - -#trajectory with index file -#TODO eine allgemeinere stelle? -traj = md.open('/data/robin/sim/nvt/12kwater/240_r25_0_NVT', - trajectory='nojump.xtc', index_file='indexSL.ndx',topology='*.gro') -#Liquid oxygens -LO = traj.subset(indices= traj.atoms.indices['LH2O']) -#Solid oxygens -SO = traj.subset(indices= traj.atoms.indices['SH2O']) -#Solid oxygens and bonded hydrogens -SW = traj.subset(residue_id = SO.atom_subset.residue_ids) - -#TODO die folgenden beiden zusammen sind nochmal deutlich schneller als -#md.atom.distance_to_atoms, kannst du entweder in irgendeiner weise einbauen -#oder hier lassen, man muss aber auf thickness achten, dass das sinn macht -#adds periodic layers of the atoms -def pbc_points(points, box_vector, thickness=0, index=False, inclusive=True): - coordinates = np.copy(points)%box_vector - allcoordinates = np.copy(coordinates) - indices = np.tile(np.arange(len(points)),(27)) - for x in range(-1, 2, 1): - for y in range(-1, 2, 1): - for z in range(-1, 2, 1): - vv = np.array([x, y, z], dtype=float) - if not (vv == 0).all() : - allcoordinates = np.concatenate((allcoordinates, coordinates + vv*box_vector), axis=0) - - if thickness != 0: - mask = np.all(allcoordinates < box_vector+thickness, axis=1) - allcoordinates = allcoordinates[mask] - indices = indices[mask] - mask = np.all(allcoordinates > -thickness, axis=1) - allcoordinates = allcoordinates[mask] - indices = indices[mask] - if not inclusive: - allcoordinates = allcoordinates[len(points):] - indices = indices[len(points):] - if index: - return (allcoordinates, indices) - return allcoordinates - -#fast calculation of shortest distance from one subset to another, uses pbc_points -def distance_to_atoms(ref, observed_atoms, box=None, thickness=0.5): - if box is not None: - start_coords = np.copy(observed_atoms)%box - all_frame_coords = pbc_points(ref, box, thickness = thickness) - else: - start_coords = np.copy(observed_atoms) - all_frame_coords = np.copy(ref) - - tree = spatial.cKDTree(all_frame_coords) - first_neighbors = tree.query(start_coords)[0] - return first_neighbors - -#this is used to reduce the number of wall atoms to those relevant, speeds up the rest -dist = distance_to_atoms(LO[0], SW[0], np.diag(LO[0].box)) -wall_atoms = SW.atom_subset.indices[0] -wall_atoms = wall_atoms[dist < 0.35] -SW = traj.subset(indices = wall_atoms) - -from functools import partial -func = partial(md.correlation.isf, q=22.7) - -#selector function to choose liquid oxygens with a certain distance to wall atoms -def selector_func(coords, lindices, windices, dmin, dmax): - lcoords = coords[lindices] - wcoords = coords[windices] - dist = distance_to_atoms(wcoords, lcoords,box=np.diag(coords.box)) - #radial distance to pore center to ignore molecules that entered the wall - rad = np.sum((lcoords[:,:2]-np.diag(coords.box)[:2]/2)**2,axis=1)**.5 - return lindices[(dist >= dmin) & (dist < dmax) & (rad < 2.7)] - -#calculate the shifted correlation for several bins -#bin positions are roughly the average of the limits -bins = np.array([0.15,0.2,0.3,0.4,0.5,0.8,1.0,1.4,1.8,2.3]) -binpos = (bins[1:]+bins[:-1])/2 -S = np.empty(len(bins)-1, dtype='object') -for i in range(len(bins)-1): - selector = partial(selector_func,lindices=LO.atom_subset.indices[0], - windices=SW.atom_subset.indices[0],dmin=bins[i], - dmax = bins[i+1]) - t, S[i] = md.correlation.shifted_correlation( - func, traj,segments=50, skip=0.1,average=True, - correlation=md.correlation.subensemble_correlation(selector), - description=str(bins[i])+','+str(bins[i+1])) - -taus = np.zeros(len(S)) -tudplot.activate() -plt.figure() -for i,s in enumerate(S): - pl = plt.plot(t, s, '.', label='d = ' + str(binpos[i]) + ' nm') - #only includes the relevant data for 1/e fitting - mask = s < 0.6 - fit, cov = curve_fit(md.functions.kww, t[mask], s[mask], - p0=[1.0,t[t>1/np.e][-1],0.5]) - taus[i] = md.functions.kww_1e(*fit) - plt.plot(t, md.functions.kww(t, *fit), c=pl[0].get_color()) -plt.xscale('log') -plt.legend() -#plt.show() - -tudplot.activate() -plt.figure() -plt.plot(binpos, taus,'.',label=r'$\tau$(d)') -plt.yscale('log') -plt.legend() -#plt.show() diff --git a/examples/plot_temperature.py b/examples/plot_temperature.py deleted file mode 100644 index b675689..0000000 --- a/examples/plot_temperature.py +++ /dev/null @@ -1,17 +0,0 @@ -""" -Plotting the Temperature from an Energy File -============================================ - -This example reads an Gromacs energy file and plots the evolultion and mean of the temperature. -""" - -from matplotlib import pyplot as plt -import mdevaluate as md -import tudplot - -tudplot.activate() - -edr = md.open_energy('/data/niels/sim/water/bulk/300K/out/energy_water1000bulk300.edr') -T = edr['Temperature'] -plt.plot(edr.time, T) -plt.plot(edr.time[[0, -1]], [T.mean(), T.mean()]) From 218574d50c3e1d9c8344083b206a1cc4f36b915e Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 14:40:11 +0100 Subject: [PATCH 49/73] Fixed circular import --- src/mdevaluate/distribution.py | 2 +- src/mdevaluate/pbc.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index dd069a7..501b71a 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -120,7 +120,7 @@ def gr( def distance_distribution( - atoms: CoordinateFrame, bins: Optional[int, ArrayLike] + atoms: CoordinateFrame, bins: Union[int, ArrayLike] ) -> NDArray: connection_vectors = atoms[:-1, :] - atoms[1:, :] connection_lengths = (connection_vectors**2).sum(axis=1) ** 0.5 diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index 16678d8..8755b43 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -1,5 +1,6 @@ +from __future__ import annotations from collections import OrderedDict -from typing import Optional, Union +from typing import Optional, Union, TYPE_CHECKING import numpy as np from numpy.typing import ArrayLike, NDArray @@ -7,7 +8,8 @@ from numpy.typing import ArrayLike, NDArray from itertools import product from .logging import logger -from .coordinates import CoordinateFrame +if TYPE_CHECKING: + from mdevaluate.coordinates import CoordinateFrame def pbc_diff( @@ -70,7 +72,7 @@ def pbc_diff_tric(coords_a: NDArray, coords_b: NDArray, box: NDArray) -> NDArray def pbc_dist( - atoms_a: CoordinateFrame, atoms_b: CoordinateFrame, box: Optional[NDArray] = None + atoms_a: NDArray, atoms_b: NDArray, box: Optional[NDArray] = None ) -> ArrayLike: return ((pbc_diff(atoms_a, atoms_b, box) ** 2).sum(axis=1)) ** 0.5 From f48dff2eded3d3c7c99924a90b1d613c16503938 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 28 Dec 2023 15:18:08 +0100 Subject: [PATCH 50/73] Fixed imports in init files --- src/mdevaluate/__init__.py | 5 +++-- src/mdevaluate/extra/__init__.py | 2 ++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index 2687fda..8fe6d5a 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -5,16 +5,17 @@ from typing import Optional import pandas as pd from . import atoms +from . import autosave +from . import checksum from . import coordinates from . import correlation from . import distribution from . import functions from . import pbc -from . import autosave from . import reader from . import system from . import utils -from .extra import free_energy_landscape, chill +from . import extra from .logging import logger diff --git a/src/mdevaluate/extra/__init__.py b/src/mdevaluate/extra/__init__.py index e69de29..8d1d8fb 100644 --- a/src/mdevaluate/extra/__init__.py +++ b/src/mdevaluate/extra/__init__.py @@ -0,0 +1,2 @@ +from . import chill +from . import free_energy_landscape From 62d05423c547d8184c95b535fddcc226b93c247b Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 29 Dec 2023 16:59:56 +0100 Subject: [PATCH 51/73] Added new function for finding maxima of occupation --- src/mdevaluate/extra/free_energy_landscape.py | 28 ++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 429f951..17f1b9d 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -44,7 +44,7 @@ def occupation_matrix(trajectory, edge_length=0.05, segments=1000, skip=0.1, nod occupation_df = pd.DataFrame( {"x": coords[0], "y": coords[1], "z": coords[2], "occupation": matbin_new} ) - occupation_df = occupation_df.query("occupation != 0") + occupation_df = occupation_df.query("occupation != 0").reset_index(drop=True) return occupation_df @@ -64,6 +64,32 @@ def _calc_histogram(numberlist, trajectory, bins): return matbin +def find_maxima(occupation_df, box, edge_length=0.05): + maxima_df = occupation_df.copy() + maxima_df["maxima"] = None + points = np.array(maxima_df[["x", "y", "z"]]) + tree = KDTree(points, boxsize=box) + all_neighbors = tree.query_ball_point( + points, edge_length * 3 ** (1 / 2) + edge_length / 100 + ) + for i in range(len(maxima_df)): + if maxima_df.loc[i, "maxima"] is not None: + continue + neighbors = np.array(all_neighbors[i]) + neighbors = neighbors[neighbors != i] + if len(neighbors) == 0: + maxima_df.loc[i, "maxima"] = True + elif ( + maxima_df.loc[neighbors, "occupation"].max() + < maxima_df.loc[i, "occupation"] + ): + maxima_df.loc[neighbors, "maxima"] = False + maxima_df.loc[i, "maxima"] = True + else: + maxima_df.loc[i, "maxima"] = False + return maxima_df + + def get_fel( traj, path, From c9c4ffcc593487a4abf19c238a8bfcaf2b298399 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 2 Jan 2024 10:17:27 +0100 Subject: [PATCH 52/73] Updated version number --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c257407..300bee2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "mdevaluate" -version = "23.12" +version = "24.01" dependencies = [ "mdanalysis", "pandas", From ec3f6b1c671529b0cf8648174f74384f2b599da2 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 2 Jan 2024 15:54:43 +0100 Subject: [PATCH 53/73] Added new function for energy calculations --- src/mdevaluate/extra/free_energy_landscape.py | 65 +++++++++++++++++-- 1 file changed, 58 insertions(+), 7 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 17f1b9d..189a47b 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -4,15 +4,24 @@ import os.path import numpy as np import math import scipy +from numpy.typing import ArrayLike, NDArray from scipy.spatial import KDTree import cmath import pandas as pd import multiprocessing as mp +from ..coordinates import Coordinates + VALID_GEOMETRY = {"cylindrical", "slab"} -def occupation_matrix(trajectory, edge_length=0.05, segments=1000, skip=0.1, nodes=8): +def occupation_matrix( + trajectory: Coordinates, + edge_length: float = 0.05, + segments: int = 1000, + skip: float = 0.1, + nodes: int = 8, +) -> pd.DataFrame: frame_indices = np.unique( np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) ) @@ -48,7 +57,9 @@ def occupation_matrix(trajectory, edge_length=0.05, segments=1000, skip=0.1, nod return occupation_df -def _calc_histogram(numberlist, trajectory, bins): +def _calc_histogram( + numberlist: ArrayLike, trajectory: Coordinates, bins: ArrayLike +) -> NDArray: matbin = None for index in range(0, len(numberlist), 1000): try: @@ -64,7 +75,9 @@ def _calc_histogram(numberlist, trajectory, bins): return matbin -def find_maxima(occupation_df, box, edge_length=0.05): +def find_maxima( + occupation_df: pd.DataFrame, box: ArrayLike, edge_length: float = 0.05 +) -> pd.DataFrame: maxima_df = occupation_df.copy() maxima_df["maxima"] = None points = np.array(maxima_df[["x", "y", "z"]]) @@ -80,8 +93,8 @@ def find_maxima(occupation_df, box, edge_length=0.05): if len(neighbors) == 0: maxima_df.loc[i, "maxima"] = True elif ( - maxima_df.loc[neighbors, "occupation"].max() - < maxima_df.loc[i, "occupation"] + maxima_df.loc[neighbors, "occupation"].max() + < maxima_df.loc[i, "occupation"] ): maxima_df.loc[neighbors, "maxima"] = False maxima_df.loc[i, "maxima"] = True @@ -90,6 +103,44 @@ def find_maxima(occupation_df, box, edge_length=0.05): return maxima_df +def calc_energies(maxima_indices, maxima_df, bins): + points = np.array(maxima_df[["x", "y", "z"]]) + tree = KDTree(points, boxsize=box) + maxima = maxima_df.loc[maxima_indices, ["x", "y", "z"]] + maxima_occupations = np.array(maxima_df.loc[maxima_indices, "occupation"]) + num_of_neighbors = np.max( + tree.query_ball_point(maxima, bins[-1], return_length=True) + ) + distances, indices = tree.query( + maxima, k=num_of_neighbors, distance_upper_bound=bins[-1] + ) + all_energy_hist = [] + all_occupied_bins_hist = [] + if distances.ndim == 1: + current_distances = distances[1:][distances[1:] <= bins[-1]] + current_indices = indices[1:][distances[1:] <= bins[-1]] + energy = -np.log( + maxima_df.loc[current_indices, "occupation"] / maxima_occupations + ) + energy_hist = np.histogram(current_distances, bins=bins, weights=energy)[0] + occupied_bins_hist = np.histogram(current_distances, bins=bins)[0] + result = energy_hist / occupied_bins_hist + return r, result + for i, maxima_occupation in enumerate(maxima_occupations): + current_distances = distances[i, 1:][distances[i, 1:] <= bins[-1]] + current_indices = indices[i, 1:][distances[i, 1:] <= bins[-1]] + + energy = -np.log( + maxima_df.loc[current_indices, "occupation"] / maxima_occupation + ) + energy_hist = np.histogram(current_distances, bins=bins, weights=energy)[0] + occupied_bins_hist = np.histogram(current_distances, bins=bins)[0] + all_energy_hist.append(energy_hist) + all_occupied_bins_hist.append(occupied_bins_hist) + result = np.sum(all_energy_hist, axis=0) / np.sum(all_occupied_bins_hist, axis=0) + return result + + def get_fel( traj, path, @@ -309,7 +360,7 @@ def sphere_quotient( # Distances between maxima and other cubes in the system coordlist = coordlist.reshape(-1, 3) - numOfNeigbour = tree.query_ball_point(maxima[0], unitdist, return_length=True) + numOfNeigbour = np.max(tree.query_ball_point(maxima, unitdist, return_length=True)) d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) i = 0 @@ -365,7 +416,7 @@ def sphere_quotient_slab( maxima_masked = np.array(maxima)[mask] coordlist = coordlist.reshape(-1, 3) - numOfNeigbour = tree.query_ball_point(maxima[0], unitdist, return_length=True) + numOfNeigbour = np.max(tree.query_ball_point(maxima, unitdist, return_length=True)) d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) i = 0 From 9d8965ace99e8d745976cef816036ca7f7fb2c26 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Thu, 4 Jan 2024 16:25:23 +0100 Subject: [PATCH 54/73] Added new function for adding distances from the pore center --- src/mdevaluate/extra/free_energy_landscape.py | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 189a47b..673b917 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -103,7 +103,7 @@ def find_maxima( return maxima_df -def calc_energies(maxima_indices, maxima_df, bins): +def calc_energies(maxima_indices, maxima_df, bins, box): points = np.array(maxima_df[["x", "y", "z"]]) tree = KDTree(points, boxsize=box) maxima = maxima_df.loc[maxima_indices, ["x", "y", "z"]] @@ -125,7 +125,7 @@ def calc_energies(maxima_indices, maxima_df, bins): energy_hist = np.histogram(current_distances, bins=bins, weights=energy)[0] occupied_bins_hist = np.histogram(current_distances, bins=bins)[0] result = energy_hist / occupied_bins_hist - return r, result + return result for i, maxima_occupation in enumerate(maxima_occupations): current_distances = distances[i, 1:][distances[i, 1:] <= bins[-1]] current_indices = indices[i, 1:][distances[i, 1:] <= bins[-1]] @@ -141,6 +141,22 @@ def calc_energies(maxima_indices, maxima_df, bins): return result +def add_distances(occupation_df, pore_geometry, origin): + new_df = occupation_df.copy() + if pore_geometry is "cylindrical": + new_df["distance"] = ( + (new_df["x"] - origin[0]) ** 2 + (new_df["y"] - origin[1]) ** 2 + ) ** (1 / 2) + elif pore_geometry is "slit": + new_df["distance"] = np.abs(new_df["z"] - origin[2]) + else: + raise ValueError( + f"pore_geometry is {pore_geometry}, should either be " + f"'cylindrical' or 'slit'" + ) + return new_df + + def get_fel( traj, path, From 0968ff214c2854c5c9d6013c107f31401cd1a26c Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 5 Jan 2024 16:04:14 +0100 Subject: [PATCH 55/73] Completed new functions for free energy landscape calculations --- src/mdevaluate/extra/free_energy_landscape.py | 86 +++++++++++++++---- 1 file changed, 70 insertions(+), 16 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 673b917..76d4568 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -2,9 +2,10 @@ from functools import partial import os.path import numpy as np +from numpy.typing import ArrayLike, NDArray +from numpy.polynomial.polynomial import Polynomial as Poly import math import scipy -from numpy.typing import ArrayLike, NDArray from scipy.spatial import KDTree import cmath import pandas as pd @@ -103,7 +104,13 @@ def find_maxima( return maxima_df -def calc_energies(maxima_indices, maxima_df, bins, box): +def _calc_energies( + maxima_indices: ArrayLike, + maxima_df: pd.DataFrame, + bins: ArrayLike, + box: NDArray, + T: float, +) -> NDArray: points = np.array(maxima_df[["x", "y", "z"]]) tree = KDTree(points, boxsize=box) maxima = maxima_df.loc[maxima_indices, ["x", "y", "z"]] @@ -119,8 +126,9 @@ def calc_energies(maxima_indices, maxima_df, bins, box): if distances.ndim == 1: current_distances = distances[1:][distances[1:] <= bins[-1]] current_indices = indices[1:][distances[1:] <= bins[-1]] - energy = -np.log( - maxima_df.loc[current_indices, "occupation"] / maxima_occupations + energy = ( + -np.log(maxima_df.loc[current_indices, "occupation"] / maxima_occupations) + * T ) energy_hist = np.histogram(current_distances, bins=bins, weights=energy)[0] occupied_bins_hist = np.histogram(current_distances, bins=bins)[0] @@ -130,8 +138,9 @@ def calc_energies(maxima_indices, maxima_df, bins, box): current_distances = distances[i, 1:][distances[i, 1:] <= bins[-1]] current_indices = indices[i, 1:][distances[i, 1:] <= bins[-1]] - energy = -np.log( - maxima_df.loc[current_indices, "occupation"] / maxima_occupation + energy = ( + -np.log(maxima_df.loc[current_indices, "occupation"] / maxima_occupation) + * T ) energy_hist = np.histogram(current_distances, bins=bins, weights=energy)[0] occupied_bins_hist = np.histogram(current_distances, bins=bins)[0] @@ -141,20 +150,65 @@ def calc_energies(maxima_indices, maxima_df, bins, box): return result -def add_distances(occupation_df, pore_geometry, origin): - new_df = occupation_df.copy() - if pore_geometry is "cylindrical": - new_df["distance"] = ( - (new_df["x"] - origin[0]) ** 2 + (new_df["y"] - origin[1]) ** 2 +def add_distances( + occupation_df: pd.DataFrame, pore_geometry: str, origin: ArrayLike +) -> pd.DataFrame: + distance_df = occupation_df.copy() + if pore_geometry == "cylindrical": + distance_df["distance"] = ( + (distance_df["x"] - origin[0]) ** 2 + (distance_df["y"] - origin[1]) ** 2 ) ** (1 / 2) - elif pore_geometry is "slit": - new_df["distance"] = np.abs(new_df["z"] - origin[2]) + elif pore_geometry == "slit": + distance_df["distance"] = np.abs(distance_df["z"] - origin[2]) else: raise ValueError( f"pore_geometry is {pore_geometry}, should either be " f"'cylindrical' or 'slit'" ) - return new_df + return distance_df + + +def distance_resolved_energies( + maxima_df: pd.DataFrame, + distance_bins: ArrayLike, + r_bins: ArrayLike, + box: NDArray, + T: float, +) -> pd.DataFrame: + results = [] + for i in range(len(distance_bins) - 1): + maxima_indices = np.array( + maxima_df.index[ + (maxima_df["distance"] >= distance_bins[i]) + * (maxima_df["distance"] < distance_bins[i + 1]) + * (maxima_df["maxima"] == True) + ] + ) + results.append(_calc_energies(maxima_indices, maxima_df, r_bins, box, T)) + + distances = (distance_bins[:-1] + distance_bins[1:]) / 2 + radii = (r_bins[:-1] + r_bins[1:]) / 2 + d = np.array([d for d in distances for r in radii]) + r = np.array([r for d in distances for r in radii]) + result = np.array(results).flatten() + return pd.DataFrame({"d": d, "r": r, "energy": result}) + + +def find_energy_maxima( + energy_df: pd.DataFrame, r_min: float, r_max: float +) -> pd.DataFrame: + distances = [] + energies = [] + for d, data_d in energy_df.groupby("d"): + distances.append(d) + x = np.array(data_d["r"]) + y = np.array(data_d["energy"]) + mask = (x >= r_min) * (x <= r_max) + print(x[mask], y[mask]) + p3 = Poly.fit(x[mask], y[mask], deg=2) + print(p3) + energies.append(np.max(p3(np.linspace(r_min, r_max, 1000)))) + return pd.DataFrame({"d": distances, "energy": energies}) def get_fel( @@ -214,8 +268,8 @@ def get_fel( np.save(f"{path}/radiiBins", bins) energy_differences = np.array(calculate_energy_difference(data, bins, temperature)) - r = bins[1:] - (bins[1] - bins[0]) / 2 - return r, energy_differences + d = np.linspace(radiusmin, radiusmax, len(energy_differences)) + return d, energy_differences def fill_bins(traj, path, edge=0.05): From b1cb9d99cf178ac797bc7d6442d598a339c7cde3 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 5 Jan 2024 16:10:46 +0100 Subject: [PATCH 56/73] Marked old code as deprecated --- src/mdevaluate/extra/free_energy_landscape.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 76d4568..94a595a 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -13,8 +13,6 @@ import multiprocessing as mp from ..coordinates import Coordinates -VALID_GEOMETRY = {"cylindrical", "slab"} - def occupation_matrix( trajectory: Coordinates, @@ -211,6 +209,11 @@ def find_energy_maxima( return pd.DataFrame({"d": distances, "energy": energies}) +# DEPRECATED CODE STARTING FROM HERE +# LEFT FOR TESTING ONLY +VALID_GEOMETRY = {"cylindrical", "slab"} + + def get_fel( traj, path, From 76db85d6283231913ccc8206df7265c34c84eeff Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 5 Jan 2024 16:30:47 +0100 Subject: [PATCH 57/73] Added optional argument box to pbc_points --- src/mdevaluate/pbc.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index 8755b43..f7cfec6 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -8,6 +8,7 @@ from numpy.typing import ArrayLike, NDArray from itertools import product from .logging import logger + if TYPE_CHECKING: from mdevaluate.coordinates import CoordinateFrame @@ -179,6 +180,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame: def pbc_points( coordinates: CoordinateFrame, + box: Optional[NDArray] = None, thickness: Optional[float] = None, index: bool = False, shear: bool = False, @@ -191,7 +193,8 @@ def pbc_points( index=True also returns the indices with indices of images being their original values. """ - box = coordinates.box + if box is None: + box = coordinates.box if shear: box[2, 0] = box[2, 0] % box[0, 0] # Shifts the box images in the other directions if they moved more than From b39dd3c45f7139737b3454ecd191aa0d5f352bdb Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 5 Jan 2024 16:34:18 +0100 Subject: [PATCH 58/73] Changed type hint for pbc_points --- src/mdevaluate/pbc.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index f7cfec6..771ffd6 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -179,7 +179,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame: def pbc_points( - coordinates: CoordinateFrame, + coordinates: ArrayLike, box: Optional[NDArray] = None, thickness: Optional[float] = None, index: bool = False, From 4f17cfe87631dabb5cfce91102350aec9629de9e Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Fri, 5 Jan 2024 16:36:34 +0100 Subject: [PATCH 59/73] Added type hints and removed number_of_next_neighbors --- src/mdevaluate/extra/chill.py | 138 +++++++++++++++++++++++++++++++--- 1 file changed, 129 insertions(+), 9 deletions(-) diff --git a/src/mdevaluate/extra/chill.py b/src/mdevaluate/extra/chill.py index dfb0bc5..f626915 100644 --- a/src/mdevaluate/extra/chill.py +++ b/src/mdevaluate/extra/chill.py @@ -1,9 +1,17 @@ +from typing import Tuple, Callable + import numpy as np +from numpy.typing import ArrayLike, NDArray +import pandas as pd +from scipy import sparse from scipy.spatial import KDTree from scipy.special import sph_harm +from mdevaluate.coordinates import CoordinateFrame, Coordinates +from mdevaluate.pbc import pbc_points -def a_ij(atoms, N=4, l=3): + +def a_ij(atoms: ArrayLike, N: int = 4, l: int = 3) -> tuple[NDArray, NDArray]: tree = KDTree(atoms) dist, indices = tree.query(atoms, N + 1) @@ -30,13 +38,9 @@ def a_ij(atoms, N=4, l=3): return np.real(aij), indices -def number_of_neighbors(atoms): - tree = KDTree(atoms) - dist, _ = tree.query(atoms, 10, distance_upper_bound=0.35) - return np.array([len(distance[distance < 0.4]) - 1 for distance in dist]) - - -def classify_ice(aij, indices, neighbors, indexSOL): +def classify_ice( + aij: NDArray, indices: NDArray, neighbors: NDArray, indexSOL: NDArray +) -> NDArray: staggerdBonds = np.sum(aij <= -0.8, axis=1) eclipsedBonds = np.sum((aij >= -0.35) & (aij <= 0.25), axis=1) @@ -67,7 +71,7 @@ def classify_ice(aij, indices, neighbors, indexSOL): return iceTypes -def ice_parts(iceTypes): +def count_ice_types(iceTypes: NDArray) -> NDArray: cubic = len(iceTypes[iceTypes == 0]) hexagonal = len(iceTypes[iceTypes == 1]) interface = len(iceTypes[iceTypes == 2]) @@ -77,3 +81,119 @@ def ice_parts(iceTypes): return np.array( [cubic, hexagonal, interface, clathrate, clathrate_interface, liquid] ) + + +def selector_ice( + start_frame: CoordinateFrame, + traj: Coordinates, + chosen_ice_types: ArrayLike, + combined: bool = True, +) -> NDArray: + oxygen = traj.subset(atom_name="OW") + atoms = oxygen[start_frame.step] + atoms = atoms % np.diag(atoms.box) + atoms_PBC = pbc_points(atoms, thickness=1) + aij, indices = a_ij(atoms_PBC) + tree = KDTree(atoms_PBC) + neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) + index_SOL = atoms_PBC.tolist().index(atoms[0].tolist()) + index_SOL = np.arange(index_SOL, index_SOL + len(atoms)) + ice_Types = classify_ice(aij, indices, neighbors, index_SOL) + index = [] + if combined is True: + for i, ice_Type in enumerate(ice_Types): + if ice_Type in chosen_ice_types: + index.append(i) + else: + for entry in chosen_ice_types: + index_entry = [] + for i, ice_Type in enumerate(ice_Types): + if ice_Type == entry: + index_entry.append(i) + index.append(np.array(index_entry)) + return np.array(index) + + +def ice_types(trajectory: Coordinates, segments: int = 10000) -> pd.DataFrame: + def ice_types_distribution(frame: CoordinateFrame, selector: Callable) -> NDArray: + atoms_PBC = pbc_points(frame, thickness=1) + aij, indices = a_ij(atoms_PBC) + tree = KDTree(atoms_PBC) + neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) + index = selector(frame, atoms_PBC) + ice_types_data = classify_ice(aij, indices, neighbors, index) + ice_parts_data = count_ice_types(ice_types_data) + return ice_parts_data + + def selector(frame: CoordinateFrame, atoms_PBC: ArrayLike) -> NDArray: + atoms_SOL = traj.subset(residue_name="SOL")[frame.step] + index = atoms_PBC.tolist().index(atoms_SOL[0].tolist()) + index = np.arange(index, index + len(atoms_SOL)) + return np.array(index) + + traj = trajectory.subset(atom_name="OW") + + frame_indices = np.unique(np.int_(np.linspace(0, len(traj) - 1, num=segments))) + + result = np.array( + [ + [ + traj[frame_index].time, + *ice_types_distribution(traj[frame_index], selector), + ] + for frame_index in frame_indices + ] + ) + + return pd.DataFrame( + { + "time": result[:, 0], + "cubic": result[:, 1], + "hexagonal": result[:, 2], + "ice_interface": result[:, 3], + "clathrate": result[:, 4], + "clathrate_interface": result[:, 5], + "liquid": result[:, 6], + } + ) + + +def ice_clusters_traj( + traj: Coordinates, segments: int = 10000, skip: float = 0.1 +) -> list: + def ice_clusters(frame: CoordinateFrame, traj: Coordinates) -> Tuple[float, list]: + selection = selector_ice(frame, traj, [0, 1, 2]) + if len(selection) == 0: + return frame.time, [] + else: + ice = frame[selection] + ice_PBC, indices_PBC = pbc_points( + ice, box=frame.box, thickness=0.5, index=True + ) + ice_tree = KDTree(ice_PBC) + ice_matrix = ice_tree.sparse_distance_matrix( + ice_tree, 0.35, output_type="ndarray" + ) + new_ice_matrix = np.zeros((len(ice), len(ice))) + for entry in ice_matrix: + if entry[2] > 0: + new_ice_matrix[indices_PBC[entry[0]], indices_PBC[entry[1]]] = 1 + n_components, labels = sparse.csgraph.connected_components( + new_ice_matrix, directed=False + ) + clusters = [] + selection = np.array(selection) + for i in range(0, np.max(labels) + 1): + if len(ice[labels == i]) > 1: + clusters.append( + list(zip(selection[labels == i], ice[labels == i].tolist())) + ) + return frame.time, clusters + + frame_indices = np.unique( + np.int_(np.linspace(len(traj) * skip, len(traj) - 1, num=segments)) + ) + all_clusters = [ + ice_clusters(traj[frame_index], traj) for frame_index in frame_indices + ] + return all_clusters From 949ed877fb1c580e9f5ebf71088844d1a0405c1c Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Mon, 8 Jan 2024 07:42:04 +0100 Subject: [PATCH 60/73] Added module for water structure parameters --- src/mdevaluate/extra/__init__.py | 1 + src/mdevaluate/extra/water.py | 419 +++++++++++++++++++++++++++++++ 2 files changed, 420 insertions(+) create mode 100644 src/mdevaluate/extra/water.py diff --git a/src/mdevaluate/extra/__init__.py b/src/mdevaluate/extra/__init__.py index 8d1d8fb..9066fc3 100644 --- a/src/mdevaluate/extra/__init__.py +++ b/src/mdevaluate/extra/__init__.py @@ -1,2 +1,3 @@ from . import chill from . import free_energy_landscape +from . import water diff --git a/src/mdevaluate/extra/water.py b/src/mdevaluate/extra/water.py new file mode 100644 index 0000000..0a64f3b --- /dev/null +++ b/src/mdevaluate/extra/water.py @@ -0,0 +1,419 @@ +from functools import partial +from typing import Tuple, Callable, Optional + +import numpy as np +from numpy.typing import NDArray, ArrayLike +import pandas as pd +from scipy.spatial import KDTree + +from ..distribution import hbonds +from ..pbc import pbc_points +from ..correlation import shifted_correlation, overlap +from ..coordinates import Coordinates, CoordinateFrame + + +def tanaka_zeta( + trajectory: Coordinates, angle: float = 30, segments: int = 100, skip: float = 0.1 +) -> pd.DataFrame: + frame_indices = np.unique( + np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) + ) + sel = trajectory.atom_subset.selection + A = np.where( + trajectory.subset(atom_name="OW", residue_name="SOL").atom_subset.selection[sel] + )[0] + D = np.vstack([A] * 2).T.reshape((-1,)) + H = np.where( + trajectory.subset(atom_name="HW.", residue_name="SOL").atom_subset.selection[ + sel + ] + )[0] + + zeta_dist = [] + zeta_cg_dist = [] + for frame_index in frame_indices: + D_frame = trajectory[frame_index][D] + H_frame = trajectory[frame_index][H] + A_frame = trajectory[frame_index][A] + box = trajectory[frame_index].box + pairs = hbonds( + D_frame, H_frame, A_frame, box, min_cos=np.cos(angle / 180 * np.pi) + ) + pairs[:, 0] = np.int_((pairs[:, 0] / 2)) + pairs = np.sort(pairs, axis=1) + pairs = np.unique(pairs, axis=0) + pairs = pairs.tolist() + + A_PBC, A_index = pbc_points(A_frame, box, thickness=0.7, index=True) + A_tree = KDTree(A_PBC) + dist, dist_index = A_tree.query(A_frame, 16, distance_upper_bound=0.7) + + dist_index = A_index[dist_index] + zeta = [] + for i, indices in enumerate(dist_index): + dist_hbond = [] + dist_non_hbond = [] + for j, index in enumerate(indices): + if j != 0: + if np.sort([indices[0], index]).tolist() in pairs: + dist_hbond.append(dist[i, j]) + else: + dist_non_hbond.append(dist[i, j]) + try: + zeta.append(np.min(dist_non_hbond) - np.max(dist_hbond)) + except ValueError: + zeta.append(0) + + zeta = np.array(zeta) + + dist, dist_index = A_tree.query(A_frame, 16, distance_upper_bound=0.7) + dist_index = A_index[dist_index] + dist_index = np.array( + [indices[dist[i] <= 0.35] for i, indices in enumerate(dist_index)] + ) + zeta_cg = np.array([np.mean(zeta[indices]) for indices in dist_index]) + + bins = np.linspace(-0.1, 0.2, 301) + zeta_dist.append(np.histogram(zeta, bins=bins)[0]) + zeta_cg_dist.append(np.histogram(zeta_cg, bins=bins)[0]) + z = bins[1:] - (bins[1] - bins[0]) / 2 + + zeta_dist = np.mean(zeta_dist, axis=0) + zeta_dist = zeta_dist / np.mean(zeta_dist) + + zeta_cg_dist = np.mean(zeta_cg_dist, axis=0) + zeta_cg_dist = zeta_cg_dist / np.mean(zeta_cg_dist) + + return pd.DataFrame({"zeta": z, "result": zeta_dist, "result_cg": zeta_cg_dist}) + + +def chi_four_trans( + trajectory: Coordinates, skip: float = 0.1, segments: int = 10000 +) -> pd.DataFrame: + traj = trajectory.nojump + N = len(trajectory[0]) + t, S = shifted_correlation( + partial(overlap, radius=0.1), traj, skip=skip, segments=segments, average=False + ) + chi = 1 / N * S.var(axis=0)[1:] + return pd.DataFrame({"time": t[1:], "chi": chi}) + + +def tanaka_correlation_map( + trajectory: Coordinates, + data_chi_four_trans: pd.DataFrame, + angle: float = 30, + segments: int = 100, + skip: float = 0.1, +) -> pd.DataFrame: + def tanaka_zeta_cg( + trajectory: Coordinates, + angle: float = 30, + segments: int = 1000, + skip: float = 0.1, + ) -> Tuple[NDArray, NDArray]: + frame_indices = np.unique( + np.int_( + np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments) + ) + ) + sel = trajectory.atom_subset.selection + A = np.where( + trajectory.subset(atom_name="OW", residue_name="SOL").atom_subset.selection[ + sel + ] + )[0] + D = np.vstack([A] * 2).T.reshape((-1,)) + H = np.where( + trajectory.subset( + atom_name="HW.", residue_name="SOL" + ).atom_subset.selection[sel] + )[0] + + zeta_cg = [] + times = [] + for frame_index in frame_indices: + D_frame = trajectory[frame_index][D] + H_frame = trajectory[frame_index][H] + A_frame = trajectory[frame_index][A] + box = trajectory[frame_index].box + pairs = hbonds( + D_frame, H_frame, A_frame, box, min_cos=np.cos(angle / 180 * np.pi) + ) + pairs[:, 0] = np.int_((pairs[:, 0] / 2)) + pairs = np.sort(pairs, axis=1) + pairs = np.unique(pairs, axis=0) + pairs = pairs.tolist() + + A_PBC, A_index = pbc_points(A_frame, box, thickness=0.7, index=True) + A_tree = KDTree(A_PBC) + dist, dist_index = A_tree.query(A_frame, 16, distance_upper_bound=0.7) + + dist_index = A_index[dist_index] + zeta = [] + for i, indices in enumerate(dist_index): + dist_hbond = [] + dist_non_hbond = [] + for j, index in enumerate(indices): + if j != 0: + if np.sort([indices[0], index]).tolist() in pairs: + dist_hbond.append(dist[i, j]) + else: + dist_non_hbond.append(dist[i, j]) + try: + zeta.append(np.min(dist_non_hbond) - np.max(dist_hbond)) + except ValueError: + zeta.append(0) + zeta = np.array(zeta) + dist_index = np.array( + [indices[dist[i] <= 0.35] for i, indices in enumerate(dist_index)] + ) + zeta_cg.append(np.array([np.mean(zeta[indices]) for indices in dist_index])) + times.append(trajectory[frame_index].time) + return np.array(times), np.array(zeta_cg) + + def delta_r_max( + trajectory: Coordinates, frame: CoordinateFrame, tau_4: float + ) -> NDArray: + dt = trajectory[1].time - trajectory[0].time + index_start = frame.step + index_end = index_start + int(tau_4 / dt) + 1 + frame_indices = np.arange(index_start, index_end + 1) + end_cords = np.array([trajectory[frame_index] for frame_index in frame_indices]) + vectors = trajectory[index_start] - end_cords + + delta_r = np.linalg.norm(vectors, axis=-1) + delta_r = np.max(delta_r, axis=0) + return delta_r + + d = np.array(data_chi_four_trans[["time", "chi"]]) + mask = d[:, 1] >= 0.7 * np.max(d[:, 1]) + fit = np.polyfit(d[mask, 0], d[mask, 1], 4) + p = np.poly1d(fit) + x_inter = np.linspace(d[mask, 0][0], d[mask, 0][-1], 1e6) + y_inter = p(x_inter) + tau_4 = x_inter[y_inter == np.max(y_inter)] + + oxygens = trajectory.nojump.subset(atom_name="OW") + window = tau_4 / trajectory[-1].time + start_frames = np.unique( + np.linspace( + len(trajectory) * skip, + len(trajectory) * (1 - window), + num=segments, + endpoint=False, + dtype=int, + ) + ) + + times, zeta_cg = tanaka_zeta_cg(trajectory, angle=angle) + + zeta_cg_mean = np.array( + [ + np.mean( + zeta_cg[ + (times >= trajectory[start_frame].time) + * (times <= (trajectory[start_frame].time + tau_4)) + ], + axis=0, + ) + for start_frame in start_frames + ] + ).flatten() + delta_r = np.array( + [ + delta_r_max(oxygens, oxygens[start_frame], tau_4) + for start_frame in start_frames + ] + ).flatten() + return pd.DataFrame({"zeta_cg": zeta_cg_mean, "delta_r": delta_r}) + + +def LSI_atom(distances: ArrayLike) -> NDArray: + r_j = distances[distances <= 0.37] + r_j = r_j.tolist() + r_j.append(distances[len(r_j)]) + delta_ji = [r_j[i + 1] - r_j[i] for i in range(0, len(r_j) - 1)] + mean_delta_i = np.mean(delta_ji) + I = 1 / len(delta_ji) * np.sum((mean_delta_i - delta_ji) ** 2) + return I + + +def LSI( + trajectory: Coordinates, segments: int = 10000, skip: float = 0 +) -> pd.DataFrame: + def LSI_distribution( + frame: CoordinateFrame, bins: NDArray, selector: Optional[Callable] = None + ) -> NDArray: + atoms_PBC = pbc_points(frame, frame.box, thickness=0.7) + atoms_tree = KDTree(atoms_PBC) + if selector: + index = selector(frame) + else: + index = np.arange(len(frame)) + dist, _ = atoms_tree.query(frame[index], 50, distance_upper_bound=0.6) + distances = dist[:, 1:] + LSI_values = np.array([LSI_atom(distance) for distance in distances]) + dist = np.histogram(LSI_values, bins=bins, density=True)[0] + return dist + + bins = np.linspace(0, 0.007, 201) + I = bins[1:] - (bins[1] - bins[0]) / 2 + + frame_indices = np.unique( + np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) + ) + distributions = np.array( + [ + LSI_distribution(trajectory[frame_index], trajectory, bins, selector=None) + for frame_index in frame_indices + ] + ) + P = np.mean(distributions, axis=0) + return pd.DataFrame({"I": I, "P": P}) + + +def HDL_LDL_positions(frame, trajectory, selector=None): + atoms_PBC = pbc_points(frame, frame.box, thickness=0.7) + atoms_tree = KDTree(atoms_PBC) + if selector: + index = selector(frame) + else: + index = range(len(frame)) + dist = atoms_tree.query(frame[index], 50, distance_upper_bound=0.6)[0] + distances = dist[:, 1:] + LSI_values = np.array([LSI_atom(distance) for distance in distances]) + LDL = LSI_values >= 0.0013 + HDL = LSI_values < 0.0013 + pos_HDL = frame[index][HDL] + pos_LDL = frame[index][LDL] + return pos_HDL, pos_LDL + + +def HDL_LDL_gr(trajectory, segments=10000, skip=0): + def gr_frame(frame, trajectory, bins): + atoms_ALL = frame + atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory) + + atoms_PBC_ALL = pbc_points(atoms_ALL, frame.box) + atoms_PBC_LDL = pbc_points(atoms_LDL, frame.box) + atoms_PBC_HDL = pbc_points(atoms_HDL, frame.box) + + tree_ALL = KDTree(atoms_PBC_ALL) + tree_LDL = KDTree(atoms_PBC_LDL) + tree_HDL = KDTree(atoms_PBC_HDL) + + dist_ALL_ALL, _ = tree_ALL.query( + atoms_ALL, len(frame) // 2, distance_upper_bound=bins[-1] + 0.1 + ) + dist_HDL_HDL, _ = tree_HDL.query( + atoms_HDL, len(frame) // 2, distance_upper_bound=bins[-1] + 0.1 + ) + dist_LDL_LDL, _ = tree_LDL.query( + atoms_LDL, len(frame) // 2, distance_upper_bound=bins[-1] + 0.1 + ) + dist_HDL_LDL, _ = tree_LDL.query( + atoms_HDL, len(frame) // 2, distance_upper_bound=bins[-1] + 0.1 + ) + + dist_ALL_ALL = dist_ALL_ALL[:, 1:].flatten() + dist_HDL_HDL = dist_HDL_HDL[:, 1:].flatten() + dist_LDL_LDL = dist_LDL_LDL[:, 1:].flatten() + dist_HDL_LDL = dist_HDL_LDL.flatten() + + hist_ALL_ALL = np.histogram( + dist_ALL_ALL, bins=bins, range=(0, bins[-1]), density=False + )[0] + hist_HDL_HDL = np.histogram( + dist_HDL_HDL, bins=bins, range=(0, bins[-1]), density=False + )[0] + hist_LDL_LDL = np.histogram( + dist_LDL_LDL, bins=bins, range=(0, bins[-1]), density=False + )[0] + hist_HDL_LDL = np.histogram( + dist_HDL_LDL, bins=bins, range=(0, bins[-1]), density=False + )[0] + + n = [len(atoms_ALL), len(atoms_HDL), len(atoms_LDL)] / np.prod( + np.diag(frame.box) + ) + + return ( + np.array( + [ + hist_ALL_ALL / len(atoms_ALL), + hist_HDL_HDL / len(atoms_HDL), + hist_LDL_LDL / len(atoms_LDL), + hist_HDL_LDL / len(atoms_HDL), + ] + ), + n, + ) + + start_frame = trajectory[int(len(trajectory) * skip)] + upper_bound = round(np.min(np.diag(start_frame.box)) / 2 - 0.05, 1) + bins = np.linspace(0, upper_bound, upper_bound * 500 + 1) + frame_indices = np.unique( + np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) + ) + + gr = [] + ns = [] + for frame_index in frame_indices: + hists, n = gr_frame(trajectory[frame_index], trajectory, bins) + gr.append(hists) + ns.append(n) + + gr = np.mean(gr, axis=0) + gr = gr / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) + r = bins[1:] - (bins[1] - bins[0]) / 2 + n = np.mean(ns, axis=0) + + return pd.DataFrame( + {"r": r, "gr_ALL": [0], "gr_HDL": gr[1], "gr_LDL": gr[2], "gr_MIX": gr[3]} + ) + + +def HDL_LDL_concentration(trajectory, segments=10000, skip=0): + def HDL_LDL_concentration_frame(frame, trajectrory, bins): + atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory) + atoms_PBC_HDL = pbc_points(atoms_HDL, frame.box, thickness=0.61) + atoms_PBC_LDL = pbc_points(atoms_LDL, frame.box, thickness=0.61) + tree_LDL = KDTree(atoms_PBC_LDL) + tree_HDL = KDTree(atoms_PBC_HDL) + dist_HDL_HDL, _ = tree_HDL.query(atoms_HDL, 31, distance_upper_bound=0.6) + dist_HDL_LDL, _ = tree_LDL.query(atoms_HDL, 30, distance_upper_bound=0.6) + HDL_near_HDL = np.sum( + dist_HDL_HDL <= 0.5, axis=-1 + ) # Ausgangsteilchen dazu zählen + LDL_near_HDL = np.sum(dist_HDL_LDL <= 0.5, axis=-1) + x_HDL = HDL_near_HDL / (HDL_near_HDL + LDL_near_HDL) + x_HDL_dist = np.histogram(x_HDL, bins=bins, range=(0, bins[-1]), density=True)[ + 0 + ] + dist_LDL_LDL, _ = tree_LDL.query(atoms_LDL, 31, distance_upper_bound=0.6) + dist_LDL_HDL, _ = tree_HDL.query(atoms_LDL, 30, distance_upper_bound=0.6) + LDL_near_LDL = np.sum( + dist_LDL_LDL <= 0.5, axis=-1 + ) # Ausgangsteilchen dazu zählen + HDL_near_LDL = np.sum(dist_LDL_HDL <= 0.5, axis=-1) + x_LDL = LDL_near_LDL / (LDL_near_LDL + HDL_near_LDL) + x_LDL_dist = np.histogram(x_LDL, bins=bins, range=(0, bins[-1]), density=True)[ + 0 + ] + return x_HDL_dist, x_LDL_dist + + bins = np.linspace(0, 1, 21) + x = bins[1:] - (bins[1] - bins[0]) / 2 + frame_indices = np.unique( + np.int_(np.linspace(len(trajectory) * skip, len(trajectory) - 1, num=segments)) + ) + local_concentration_dist = np.array( + [ + HDL_LDL_concentration_frame(trajectory[frame_index], trajectory, bins) + for frame_index in frame_indices + ] + ) + x_HDL = np.mean(local_concentration_dist[:, 0], axis=0) + x_LDL = np.mean(local_concentration_dist[:, 1], axis=0) + return pd.DataFrame({"x": x, "x_HDL": x_HDL, "x_LDL": x_LDL}) From 7dd2074ee5023ba39f8d7695fac76cc28d8ae60f Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 9 Jan 2024 13:00:12 +0100 Subject: [PATCH 61/73] Renamed matrixes to matrices --- src/mdevaluate/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/mdevaluate/__init__.py b/src/mdevaluate/__init__.py index 8fe6d5a..921a6cd 100644 --- a/src/mdevaluate/__init__.py +++ b/src/mdevaluate/__init__.py @@ -38,7 +38,7 @@ def open( used, if there is exactly one in the directoy. trajectory (opt.): Descriptor of the trajectory (xtc or trr file). nojump (opt.): - If nojump matrixes should be generated. They will alwyas be loaded + If nojump matrices should be generated. They will alwyas be loaded if present index_file (opt.): Descriptor of the index file (ndx file). charges (opt.): @@ -97,9 +97,9 @@ def open( coords = coordinates.Coordinates(frames, atom_subset=atom_set) if nojump: try: - frames.nojump_matrixes + frames.nojump_matrices except reader.NojumpError: - reader.generate_nojump_matrixes(coords) + reader.generate_nojump_matrices(coords) return coords From 0870a94b44080e72a711e7b4f8d4f0969dc399f2 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 9 Jan 2024 13:43:04 +0100 Subject: [PATCH 62/73] Selector now also works correctly on lists. --- src/mdevaluate/correlation.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index 7d2eee6..d78c43a 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -99,20 +99,18 @@ def shifted_correlation( return get_correlation(frames, start_frame, index, shifted_idx) else: index = selector(frames[start_frame]) - if index.ndim == 1: + if len(index) == 0: + return np.zeros(len(idx)) + + if isinstance(index[0], int) or isinstance(index[0], bool): return get_correlation(frames, start_frame, index, shifted_idx) - elif index.ndim == 2: + else: correlations = [] for ind in index: correlations.append( get_correlation(frames, start_frame, ind, shifted_idx) ) return correlations - else: - raise ValueError( - f"Index list of selector has {index.ndim} dimensions, " - "but should have 1 or 2" - ) if 1 - skip < window: window = 1 - skip From a559f72221e8b4cbb5e8ff919ad2e97ba7328134 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 9 Jan 2024 13:46:46 +0100 Subject: [PATCH 63/73] Selector now checks for to many dimensions. --- src/mdevaluate/correlation.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index d78c43a..786f57b 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -100,16 +100,25 @@ def shifted_correlation( else: index = selector(frames[start_frame]) if len(index) == 0: - return np.zeros(len(idx)) + return np.zeros(len(shifted_idx)) - if isinstance(index[0], int) or isinstance(index[0], bool): + elif isinstance(index[0], int) or isinstance(index[0], bool): return get_correlation(frames, start_frame, index, shifted_idx) else: correlations = [] for ind in index: - correlations.append( - get_correlation(frames, start_frame, ind, shifted_idx) - ) + if len(ind) == 0: + correlations.append(np.zeros(len(shifted_idx))) + + elif isinstance(ind[0], int) or isinstance(ind[0], bool): + correlations.append( + get_correlation(frames, start_frame, ind, shifted_idx) + ) + else: + raise ValueError( + "selector has more than two dimensions or does not " + "contain int or bool types" + ) return correlations if 1 - skip < window: From ba893881a3e68eca205c7feb4113519d7602c0aa Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 10 Jan 2024 12:41:06 +0100 Subject: [PATCH 64/73] Removed deprecated code --- src/mdevaluate/extra/free_energy_landscape.py | 440 +----------------- 1 file changed, 5 insertions(+), 435 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index 94a595a..e9da30e 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -57,15 +57,15 @@ def occupation_matrix( def _calc_histogram( - numberlist: ArrayLike, trajectory: Coordinates, bins: ArrayLike + indices: ArrayLike, trajectory: Coordinates, bins: ArrayLike ) -> NDArray: matbin = None - for index in range(0, len(numberlist), 1000): + for index in range(0, len(indices), 1000): try: - indices = numberlist[index : index + 1000] + current_indices = indices[index : index + 1000] except IndexError: - indices = numberlist[index:] - frames = np.concatenate(np.array([trajectory.pbc[i] for i in indices])) + current_indices = indices[index:] + frames = np.concatenate(np.array([trajectory.pbc[i] for i in current_indices])) hist, _ = np.histogramdd(frames, bins=bins) if matbin is None: matbin = hist @@ -202,436 +202,6 @@ def find_energy_maxima( x = np.array(data_d["r"]) y = np.array(data_d["energy"]) mask = (x >= r_min) * (x <= r_max) - print(x[mask], y[mask]) p3 = Poly.fit(x[mask], y[mask], deg=2) - print(p3) energies.append(np.max(p3(np.linspace(r_min, r_max, 1000)))) return pd.DataFrame({"d": distances, "energy": energies}) - - -# DEPRECATED CODE STARTING FROM HERE -# LEFT FOR TESTING ONLY -VALID_GEOMETRY = {"cylindrical", "slab"} - - -def get_fel( - traj, - path, - geometry, - temperature, - edge=0.05, - radiusmin=0.05, - radiusmax=2.05, - z=[-np.inf, np.inf], - overwrite=False, -): - """ - The main method of this script. Will calculate the energy difference based on radius. - - This method will calculate the relative energy of different minima based on radius - to the pore center. - After that it will save those results in a .npy file in the filepath give by the - "path" parameter and will also try to load from there. - - - Parameters: - traj : The trajectory of the system to be evaluated - path : The save and load location of the files - geometry : Either "cylindrical" or "slab". The geometry of your system. Other types - currently not supported - temperature: The temperature of your system. Needed for the energy difference - edge (opt.) : The length of the cubes in which your system will be divided - radiusmin (opt.) : The radius where the calculation begins. Will create a bin of - +- 0.05 of that number. - radiusmax (opt.) : The radius where the calculation ends. Will create a bin of - +- 0.05 of that number. - z (opt.) : The evaluated slice of the trajectory for the energy landscape. - - Returns: - list: A list of the energy difference based on radius - - """ - if geometry not in VALID_GEOMETRY: - raise ValueError("results: status must be one of %r." % VALID_GEOMETRY) - - if (os.path.exists(f"{path}/radiiData.npy")) & (not overwrite): - data = np.load(f"{path}/radiiData.npy") - bins = np.load(f"{path}/radiiBins.npy") - # Here the different standard geometries are inserted - else: - if geometry == "cylindrical": - bins, data = short_all_radii( - traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z - ) - elif geometry == "slab": - bins, data = short_all_radii_slab( - traj, path, edge=edge, radiusmin=radiusmin, radiusmax=radiusmax, z=z - ) - np.save(f"{path}/radiiData", data) - np.save(f"{path}/radiiBins", bins) - - energy_differences = np.array(calculate_energy_difference(data, bins, temperature)) - d = np.linspace(radiusmin, radiusmax, len(energy_differences)) - return d, energy_differences - - -def fill_bins(traj, path, edge=0.05): - # If available Matrix is directly loaded - if os.path.exists(f"{path}/Matrix{edge}.npy"): - matbin = np.load(f"{path}/Matrix{edge}.npy") - return matbin - - pool = mp.Pool(8) - size = math.ceil(len(traj) / 8) - - # Trajectory is split for parallel computing - indices = list(chunksplit(np.arange(0, len(traj), 1), size)) - # indices = list(Chunksplit(np.arange(len(traj)-80, len(traj), 1), size)) - fill = partial(help_fill, traj=traj) - results = pool.map(fill, indices) - boxmat = traj[0].box - - a = math.ceil(boxmat[0][0] / 0.05) - b = math.ceil(boxmat[1][1] / 0.05) - c = math.ceil(boxmat[2][2] / 0.05) - matbin = np.zeros((a, b, c)) - - pool.close() - - for mat in results: - matbin = matbin + mat - np.save(file=f"{path}/Matrix{edge}", arr=matbin) - return matbin - - -def help_fill(numberlist, traj, edge=0.05): - boxmat = traj[0].box - - a = math.ceil(boxmat[0][0] / edge) - b = math.ceil(boxmat[1][1] / edge) - c = math.ceil(boxmat[2][2] / edge) - matbin = np.zeros((a, b, c)) - temp = np.array([[]]).reshape(0, 3) - - # Trajectory is split in chunks of 1000 frames to increase efficency while - # keeping ram usage low - h = 1000 - while h < len(numberlist): - temp = np.array([[]]).reshape(0, 3) - x = numberlist[h - 1000] - y = numberlist[h] - for j in traj.pbc[x:y]: - l = np.floor(j / edge).astype("int32") - temp = np.concatenate((temp, np.array(l))) - # Positions are counted for whole chunk - unique, counts = np.unique(temp, return_counts=True, axis=0) - m = 0 - # Count is combined into matrix with position in Matrix corresponding to - # position in system - for z in unique.astype("int"): - a = z[0] - b = z[1] - c = z[2] - matbin[a][b][c] += counts[m] - m += 1 - h += 1000 - # The last few frames of the system are seperately calculated - x = numberlist[h - 1000] - y = numberlist[-1] - temp = np.array([[]]).reshape(0, 3) - for j in traj.pbc[x : y + 1]: - l = np.floor(j / edge).astype("int32") - temp = np.concatenate((temp, np.array(l))) - - unique, counts = np.unique(temp, return_counts=True, axis=0) - m = 0 - for z in unique.astype("int"): - a = z[0] - b = z[1] - c = z[2] - matbin[a][b][c] += counts[m] - m += 1 - return matbin - - -def calculate_maxima(matbin): - maxima = [] - - z = [-1, 0, 1] - - max_i = matbin.shape[0] - max_j = matbin.shape[1] - max_k = matbin.shape[2] - - # For each element in the matrix all surrounding 26 elements are compared to - # determine the minimum. - # Algorithm can definitely be improved but is so fast that it's not necessary. - - for i in range(max_i): - for j in range(max_j): - for k in range(max_k): - a = matbin[i][j][k] - b = True - for l in z: - for m in z: - for n in z: - if (l != 0) or (m != 0) or (n != 0): - b = b and ( - a - > matbin[(i + l) % max_i][(j + m) % max_j][ - (k + n) % max_k - ] - ) - - if b: - maxima.append([i, j, k]) - return maxima - - -def list_of_coordinates(matbin): - # Matrix elements are translated back into postion vectors - max_i = matbin.shape[0] - max_j = matbin.shape[1] - max_k = matbin.shape[2] - - coord = np.zeros((max_i, max_j, max_k, 3)) - - for i in range(max_i): - for j in range(max_j): - for k in range(max_k): - coord[i][j][k] = [i, j, k] - - return coord - - -def sphere_quotient( - matbin, - maxima, - coordlist, - tree, - radius, - box, - edge, - unitdist, - z=np.array([-np.inf, np.inf]), -): - # Here pore center is assumed to be system center - diff = np.diag(box)[0:2] / 2 - - # Distance to the z-axis - distance = np.linalg.norm(np.array(maxima)[:, 0:2] * edge - diff, axis=1) - - # selection with given parameters - mask = ( - (distance > radius - 0.05) - & (distance < radius + 0.05) - & (np.array(maxima)[:, 2] * edge > z[0]) - & (np.array(maxima)[:, 2] * edge < z[1]) - ) - - maxima_masked = np.array(maxima)[mask] - - # Distances between maxima and other cubes in the system - coordlist = coordlist.reshape(-1, 3) - numOfNeigbour = np.max(tree.query_ball_point(maxima, unitdist, return_length=True)) - d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) - - i = 0 - - average = 0 - - y = [] - num = [] - for neighbours in neighbourlist: - current = maxima_masked[i] - - # energy between minimum and all sourrounding cubes is calculated - energy = -np.log( - matbin[current[0], current[1], current[2]] - / matbin.flatten()[neighbours[1:]] - ) - - v, b = np.histogram( - d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], - bins=np.arange(1, 40.5, 0.5), - weights=energy[(energy < np.Inf) & (energy > -np.Inf)], - ) - - # For averaging purposes number weighted is also the calculated - k, l = np.histogram( - d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], - bins=np.arange(1, 40.5, 0.5), - ) - y.append(v) - num.append(k) - i += 1 - - # energy is averaged over minima - quotient = np.sum(y, axis=0) / np.sum(num, axis=0) - if len(neighbourlist) == 0: - return np.arange(1, 40.5, 0.5) * edge, np.arange(1, 40, 0.5) * 0 - return b * edge, quotient - - -def sphere_quotient_slab( - matbin, maxima, coordlist, tree, radius, box, edge, unitdist, z=[-np.inf, np.inf] -): - # Same as SphereQuotient bur for Slabs - diff = box[2, 2] / 2 - distance = abs(np.array(maxima)[:, 2] * edge - diff) - mask = ( - (distance > radius - 0.05) - & (distance < radius + 0.05) - & (np.array(maxima)[:, 2] > z[0]) - & (np.array(maxima)[:, 2] < z[1]) - ) - - maxima_masked = np.array(maxima)[mask] - - coordlist = coordlist.reshape(-1, 3) - numOfNeigbour = np.max(tree.query_ball_point(maxima, unitdist, return_length=True)) - d, neighbourlist = tree.query(maxima_masked, k=numOfNeigbour, workers=-1) - - i = 0 - - average = 0 - - y = [] - num = [] - for neighbours in neighbourlist: - current = maxima_masked[i] - - energy = -np.log( - matbin[current[0], current[1], current[2]] - / matbin.flatten()[neighbours[1:]] - ) - - # Energy is ordered according to distance to the minimum - v, z = np.histogram( - d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], - bins=unitdist * 2 - 2, - weights=energy[(energy < np.Inf) & (energy > -np.Inf)], - ) - - k, l = np.histogram( - d[i, 1:].flatten()[(energy < np.Inf) & (energy > -np.Inf)], - bins=unitdist * 2 - 2, - ) - - y.append(v) - - num.append(k) - i += 1 - - quotient = np.sum(y, axis=0) / np.sum(num, axis=0) - if len(neighbourlist) == 0: - return np.arange(1, 40.5, 0.5), np.arange(1, 40, 0.5) * 0 - return z * edge, quotient - - -def short_all_radii( - traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] -): - # Shorthand function cylindrical systems - matbin = fill_bins(traj, path, edge) - maxima = calculate_maxima(matbin) - coordinates = list_of_coordinates(matbin) - tree = KDTree(np.reshape(coordinates, (-1, 3)), boxsize=matbin.shape) - bins = [] - data = [] - for radius in np.arange(radiusmin, radiusmax, 0.1): - b, d = sphere_quotient( - matbin, - maxima, - coordinates, - tree, - radius=radius, - box=traj[0].box, - edge=edge, - unitdist=40, - z=z, - ) - bins.append(b) - data.append(d) - return bins, data - - -def short_all_radii_slab( - traj, path, edge=0.05, radiusmin=0.05, radiusmax=2.05, z=[-np.inf, np.inf] -): - # Shorthand function for Slab systems - matbin = fill_bins(traj, path, edge) - maxima = calculate_maxima(matbin) - coordinates = list_of_coordinates(matbin) - tree = KDTree(np.reshape(coordinates, (-1, 3)), boxsize=matbin.shape) - bins = [] - data = [] - for radius in np.arange(radiusmin, radiusmax, 0.1): - c, d = sphere_quotient_slab( - matbin, - maxima, - coordinates, - tree, - radius=radius, - box=traj[0].box, - edge=edge, - unitdist=40, - z=z, - ) - bins.append(c) - data.append(d) - return bins, data - - -def calculate_energy_difference(data, bins, temperature): - """ - Calculates the energy difference between local energy minimum and the - minimum in the center - """ - difference = [] - i = 0 - while i < len(data): - # - q = ( - get_minimum(data[0], bins[0])[1] - get_minimum(data[i], bins[0])[1] - ) * temperature - difference.append(q) - i += 1 - return difference - - -def chunksplit(list_a, chunk_size): - for i in range(0, len(list_a), chunk_size): - yield list_a[i : i + chunk_size] - - -def get_minimum(data, bins): - # Fits a polynom of order 3 to determine the energy minimum and analytically - # calculates the minimum location - y = data[:10] - x = bins[1:11] - - popt, _ = scipy.optimize.curve_fit(pol3, x, y, maxfev=80000) - - a, b = solve_quadratic(3 * popt[0], 2 * popt[1], popt[2]) - - if 6 * popt[0] * a + 2 * popt[1] > 0: - if (np.real(a) < 0) or (np.real(a) > 0.3): - return np.real(a), np.average(y) - return np.real(a), np.real(pol3(a, *popt)) - else: - if (np.real(b) < 0) or (np.real(b) > 0.3): - return np.real(b), np.average(y) - return np.real(b), np.real(pol3(b, *popt)) - - -def solve_quadratic(a, b, c): - d = (b**2) - (4 * a * c) - - sol1 = (-b - cmath.sqrt(d)) / (2 * a) - sol2 = (-b + cmath.sqrt(d)) / (2 * a) - return sol1, sol2 - - -def pol3(x, a, b, c, d): - return a * x**3 + b * x**2 + c * x + d From 9e9c865cf9c6fd212fd7cefd9b311a5b21249f99 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 10 Jan 2024 12:41:18 +0100 Subject: [PATCH 65/73] Adjusted tests --- test/test_free_energy_landscape.py | 68 +++++++++++++++--------------- test/test_utils.py | 13 ------ 2 files changed, 34 insertions(+), 47 deletions(-) diff --git a/test/test_free_energy_landscape.py b/test/test_free_energy_landscape.py index 44b131f..9835b23 100644 --- a/test/test_free_energy_landscape.py +++ b/test/test_free_energy_landscape.py @@ -4,7 +4,7 @@ import pytest import numpy as np import mdevaluate -from mdevaluate import free_energy_landscape as fel +import mdevaluate.extra.free_energy_landscape as fel @pytest.fixture @@ -15,40 +15,40 @@ def trajectory(request): def test_get_fel(trajectory): test_array = np.array( [ - 0.0, - 12.87438176, - 4.95868203, - 11.02055197, - 5.44195534, - 6.73933442, - 3.30971789, - 6.10424055, - 8.56153733, - 5.45777331, - 5.64545817, - 8.42100423, - 6.28132121, - 7.4777172, - 11.64839354, - 4.52566354, - 40.84730838, - 93.86241602, - 140.3039937, - 173.55970021, + 174.46253634, + 174.60905476, + 178.57658092, + 182.43001192, + 180.57916378, + 176.49886217, + 178.96018547, + 181.13561782, + 178.31026314, + 176.08903996, + 180.71215345, + 181.59703135, + 180.34329368, + 187.02474488, + 197.99167477, + 214.05788031, + 245.58571282, + 287.52457507, + 331.53492965, ] ) - oxygens_water = trajectory.subset(atom_name="OW", residue_name="SOL") - r, energy_differences = fel.get_fel( - oxygens_water, - os.path.join(os.path.dirname(__file__), "data/pore"), - "cylindrical", - 225, - edge=0.05, - radiusmin=0.05, - radiusmax=2.05, - z=[-np.inf, np.inf], - overwrite=True, - ) + OW = trajectory.subset(atom_name="OW") - assert (np.round(energy_differences) == np.round(test_array)).all() + box = np.diag(trajectory[0].box) + box_voxels = (box // [0.05, 0.05, 0.05] + [1, 1, 1]) * [0.05, 0.05, 0.05] + occupation_matrix = fel.occupation_matrix(OW, skip=0, segments=1000) + maxima_matrix = fel.find_maxima(occupation_matrix, box=box_voxels, edge_length=0.05) + maxima_matrix = fel.add_distances(maxima_matrix, "cylindrical", box / 2) + r_bins = np.arange(0, 2, 0.02) + distance_bins = np.arange(0.05, 2.05, 0.1) + energy_df = fel.distance_resolved_energies( + maxima_matrix, distance_bins, r_bins, box, 225 + ) + result = fel.find_energy_maxima(energy_df, r_min=0.05, r_max=0.15) + + assert (np.round(np.array(result["energy"])) == np.round(test_array)).all() diff --git a/test/test_utils.py b/test/test_utils.py index 211e141..9223f9e 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -31,16 +31,3 @@ def test_filon_fourier_transformation(logdata): xdata, xdata, frequencies=freqs, derivative='linear', imag=False ) assert np.isclose(filon_imag.real, filon_real).all() - - -def test_histogram(): - data = np.random.rand(100) - bins = np.linspace(0, 1) - np_hist = np.histogram(data, bins=bins)[0] - ut_hist = utils.histogram(data, bins=bins)[0] - assert (np_hist == ut_hist).all() - - bins = np.linspace(0.3, 1.5) - np_hist = np.histogram(data, bins=bins)[0] - ut_hist = utils.histogram(data, bins=bins)[0] - assert (np_hist == ut_hist).all() From 25cb3d38b39acc6af7e4ec2ea21e8eb5f716d173 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 10 Jan 2024 15:11:42 +0100 Subject: [PATCH 66/73] Added type hints --- src/mdevaluate/extra/water.py | 46 +++++++++++++++++------------------ 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/src/mdevaluate/extra/water.py b/src/mdevaluate/extra/water.py index 0a64f3b..0e72f22 100644 --- a/src/mdevaluate/extra/water.py +++ b/src/mdevaluate/extra/water.py @@ -273,7 +273,9 @@ def LSI( return pd.DataFrame({"I": I, "P": P}) -def HDL_LDL_positions(frame, trajectory, selector=None): +def HDL_LDL_positions( + frame: CoordinateFrame, selector: Optional[Callable] = None +) -> Tuple[NDArray, NDArray]: atoms_PBC = pbc_points(frame, frame.box, thickness=0.7) atoms_tree = KDTree(atoms_PBC) if selector: @@ -290,8 +292,12 @@ def HDL_LDL_positions(frame, trajectory, selector=None): return pos_HDL, pos_LDL -def HDL_LDL_gr(trajectory, segments=10000, skip=0): - def gr_frame(frame, trajectory, bins): +def HDL_LDL_gr( + trajectory: Coordinates, segments: int = 10000, skip: float = 0.1 +) -> pd.DataFrame: + def gr_frame( + frame: CoordinateFrame, trajectory: Coordinates, bins: ArrayLike + ) -> NDArray: atoms_ALL = frame atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory) @@ -334,20 +340,13 @@ def HDL_LDL_gr(trajectory, segments=10000, skip=0): dist_HDL_LDL, bins=bins, range=(0, bins[-1]), density=False )[0] - n = [len(atoms_ALL), len(atoms_HDL), len(atoms_LDL)] / np.prod( - np.diag(frame.box) - ) - - return ( - np.array( - [ - hist_ALL_ALL / len(atoms_ALL), - hist_HDL_HDL / len(atoms_HDL), - hist_LDL_LDL / len(atoms_LDL), - hist_HDL_LDL / len(atoms_HDL), - ] - ), - n, + return np.array( + [ + hist_ALL_ALL / len(atoms_ALL), + hist_HDL_HDL / len(atoms_HDL), + hist_LDL_LDL / len(atoms_LDL), + hist_HDL_LDL / len(atoms_HDL), + ] ) start_frame = trajectory[int(len(trajectory) * skip)] @@ -358,24 +357,25 @@ def HDL_LDL_gr(trajectory, segments=10000, skip=0): ) gr = [] - ns = [] for frame_index in frame_indices: - hists, n = gr_frame(trajectory[frame_index], trajectory, bins) + hists = gr_frame(trajectory[frame_index], trajectory, bins) gr.append(hists) - ns.append(n) gr = np.mean(gr, axis=0) gr = gr / (4 / 3 * np.pi * bins[1:] ** 3 - 4 / 3 * np.pi * bins[:-1] ** 3) r = bins[1:] - (bins[1] - bins[0]) / 2 - n = np.mean(ns, axis=0) return pd.DataFrame( {"r": r, "gr_ALL": [0], "gr_HDL": gr[1], "gr_LDL": gr[2], "gr_MIX": gr[3]} ) -def HDL_LDL_concentration(trajectory, segments=10000, skip=0): - def HDL_LDL_concentration_frame(frame, trajectrory, bins): +def HDL_LDL_concentration( + trajectory: Coordinates, segments: int = 10000, skip: float = 0.1 +) -> pd.DataFrame: + def HDL_LDL_concentration_frame( + frame: CoordinateFrame, bins: ArrayLike + ) -> Tuple[NDArray, NDArray]: atoms_HDL, atoms_LDL = HDL_LDL_positions(frame, trajectory) atoms_PBC_HDL = pbc_points(atoms_HDL, frame.box, thickness=0.61) atoms_PBC_LDL = pbc_points(atoms_LDL, frame.box, thickness=0.61) From 13532b3db1d9bfc66f52c649d2d3790baf9b0a12 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 10 Jan 2024 15:44:55 +0100 Subject: [PATCH 67/73] Added time_distribution function --- src/mdevaluate/distribution.py | 38 +++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index 501b71a..bebe26d 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -1,4 +1,4 @@ -from typing import Callable, Optional, Union +from typing import Callable, Optional, Union, Tuple, List import numpy as np from numpy.typing import ArrayLike, NDArray @@ -52,6 +52,42 @@ def time_average( return np.mean(result, axis=0) +@autosave_data(nargs=2, kwargs_keys=("coordinates_b",)) +def time_distribution( + function: Callable, + coordinates: Coordinates, + coordinates_b: Optional[Coordinates] = None, + skip: float = 0.1, + segments: int = 100, +) -> Tuple[NDArray, List]: + """ + Compute the time distribution of a function. + + Args: + function: + The function that will be averaged, it has to accept exactly one argument + which is the current atom set (or two if coordinates_b is provided) + coordinates: The coordinates object of the simulation + coordinates_b: Additional coordinates object of the simulation + skip: + segments: + """ + frame_indices = np.unique( + np.int_( + np.linspace(len(coordinates) * skip, len(coordinates) - 1, num=segments) + ) + ) + times = np.array([coordinates[frame_index].time for frame_index in frame_indices]) + if coordinates_b is None: + result = [function(coordinates[frame_index]) for frame_index in frame_indices] + else: + result = [ + function(coordinates[frame_index], coordinates_b[frame_index]) + for frame_index in frame_indices + ] + return times, result + + def gr( atoms_a: CoordinateFrame, atoms_b: Optional[CoordinateFrame] = None, From cf865930a593fa4046eb464d3c8585ad6d83410a Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Wed, 10 Jan 2024 15:50:42 +0100 Subject: [PATCH 68/73] Added pytest as a requirement --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5e1531f..6492fa1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,5 @@ mdanalysis pandas dask pathos -tables \ No newline at end of file +tables +pytest \ No newline at end of file From 5d16c7de5e38d430c31330fc9e8e2ba5e1d73d12 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 16 Jan 2024 13:36:56 +0100 Subject: [PATCH 69/73] Cleaned up imports and renamed T to temperature --- src/mdevaluate/extra/free_energy_landscape.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/mdevaluate/extra/free_energy_landscape.py b/src/mdevaluate/extra/free_energy_landscape.py index e9da30e..af558fa 100644 --- a/src/mdevaluate/extra/free_energy_landscape.py +++ b/src/mdevaluate/extra/free_energy_landscape.py @@ -1,13 +1,10 @@ from functools import partial -import os.path import numpy as np from numpy.typing import ArrayLike, NDArray from numpy.polynomial.polynomial import Polynomial as Poly import math -import scipy from scipy.spatial import KDTree -import cmath import pandas as pd import multiprocessing as mp @@ -171,7 +168,7 @@ def distance_resolved_energies( distance_bins: ArrayLike, r_bins: ArrayLike, box: NDArray, - T: float, + temperature: float, ) -> pd.DataFrame: results = [] for i in range(len(distance_bins) - 1): @@ -182,7 +179,9 @@ def distance_resolved_energies( * (maxima_df["maxima"] == True) ] ) - results.append(_calc_energies(maxima_indices, maxima_df, r_bins, box, T)) + results.append( + _calc_energies(maxima_indices, maxima_df, r_bins, box, temperature) + ) distances = (distance_bins[:-1] + distance_bins[1:]) / 2 radii = (r_bins[:-1] + r_bins[1:]) / 2 From 9200d31af6c6e69c8c75beb2516cc7071156142b Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 16 Jan 2024 13:37:49 +0100 Subject: [PATCH 70/73] Fixed spelling --- src/mdevaluate/pbc.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/mdevaluate/pbc.py b/src/mdevaluate/pbc.py index 771ffd6..8b60721 100644 --- a/src/mdevaluate/pbc.py +++ b/src/mdevaluate/pbc.py @@ -153,7 +153,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame: delta + np.array( np.vstack( - [m[i0 : abstep + 1].sum(axis=0) for m in reader.nojump_matrixes] + [m[i0 : abstep + 1].sum(axis=0) for m in reader.nojump_matrices] ).T ) * frame.box.diagonal() @@ -169,7 +169,7 @@ def nojump(frame: CoordinateFrame, usecache: bool = True) -> CoordinateFrame: np.vstack( [ m[: frame.step + 1, selection].sum(axis=0) - for m in reader.nojump_matrixes + for m in reader.nojump_matrices ] ).T ) From 95b46c43bea5e2e521d957c5c5d9a050736c0859 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 16 Jan 2024 13:38:13 +0100 Subject: [PATCH 71/73] Changed input of hbonds to atom_indices --- src/mdevaluate/distribution.py | 49 +++++++++++++++++++--------------- 1 file changed, 27 insertions(+), 22 deletions(-) diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py index bebe26d..cb20d21 100644 --- a/src/mdevaluate/distribution.py +++ b/src/mdevaluate/distribution.py @@ -12,6 +12,7 @@ from .coordinates import ( Coordinates, CoordinateFrame, next_neighbors, + number_of_neighbors, ) from .autosave import autosave_data from .pbc import pbc_diff, pbc_points @@ -54,11 +55,11 @@ def time_average( @autosave_data(nargs=2, kwargs_keys=("coordinates_b",)) def time_distribution( - function: Callable, - coordinates: Coordinates, - coordinates_b: Optional[Coordinates] = None, - skip: float = 0.1, - segments: int = 100, + function: Callable, + coordinates: Coordinates, + coordinates_b: Optional[Coordinates] = None, + skip: float = 0, + segments: int = 100, ) -> Tuple[NDArray, List]: """ Compute the time distribution of a function. @@ -88,7 +89,7 @@ def time_distribution( return times, result -def gr( +def rdf( atoms_a: CoordinateFrame, atoms_b: Optional[CoordinateFrame] = None, bins: Optional[ArrayLike] = None, @@ -122,10 +123,9 @@ def gr( if bins is None: bins = np.arange(0, 1, 0.01) - box = atoms_b.box - n = len(atoms_a) / np.prod(np.diag(box)) - V = 4 / 3 * np.pi * bins[-1] ** 3 - particles_in_volume = int(n * V * 1.1) + particles_in_volume = int( + np.max(number_of_neighbors(atoms_a, query_atoms=atoms_b, r_max=bins[-1])) * 1.1 + ) distances, indices = next_neighbors( atoms_a, atoms_b, @@ -144,7 +144,7 @@ def gr( ) distances = np.concatenate(new_distances) else: - distances = distances.flatten() + distances = [d for dist in distances for d in dist] hist, bins = np.histogram(distances, bins=bins, range=(0, bins[-1]), density=False) hist = hist / len(atoms_a) @@ -310,26 +310,27 @@ def next_neighbor_distribution( def hbonds( - D: CoordinateFrame, - H: CoordinateFrame, - A: CoordinateFrame, + atoms: CoordinateFrame, + donator_indices: ArrayLike, + hydrogen_indices: ArrayLike, + acceptor_indices: ArrayLike, DA_lim: float = 0.35, HA_lim: float = 0.35, - min_cos: float = np.cos(30 * np.pi / 180), + max_angle_deg: float = 30, full_output: bool = False, ) -> Union[NDArray, tuple[NDArray, NDArray, NDArray]]: """ Compute h-bond pairs Args: - D: Set of coordinates for donators. - H: Set of coordinates for hydrogen atoms. Should have the same + atoms: Set of all coordinates for a frame. + donator_indices: Set of indices for donators. + hydrogen_indices: Set of indices for hydrogen atoms. Should have the same length as D. - A: Set of coordinates for acceptors. - DA_lim (opt.): Minimum distance beteen donator and acceptor. - HA_lim (opt.): Minimum distance beteen hydrogen and acceptor. - min_cos (opt.): Minimum cosine for the HDA angle. Default is - equivalent to a maximum angle of 30 degree. + acceptor_indices: Set of indices for acceptors. + DA_lim (opt.): Minimum distance between donator and acceptor. + HA_lim (opt.): Minimum distance between hydrogen and acceptor. + max_angle_deg (opt.): Maximum angle in degree for the HDA angle. full_output (opt.): Returns additionally the cosine of the angles and the DA distances @@ -362,6 +363,10 @@ def hbonds( pairs[:, 1] = pind[pairs[:, 1]] return pairs + D = atoms[donator_indices] + H = atoms[hydrogen_indices] + A = atoms[acceptor_indices] + min_cos = np.cos(max_angle_deg * np.pi / 180) box = D.box if len(D) <= len(A): pairs = dist_DltA(D, A, DA_lim) From 5fdc9c8698b2b009a0ec7522fd28c9f65c6241b3 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 16 Jan 2024 13:38:44 +0100 Subject: [PATCH 72/73] Adjusted type hints --- src/mdevaluate/coordinates.py | 163 ++++++++++++++++++++++------------ 1 file changed, 108 insertions(+), 55 deletions(-) diff --git a/src/mdevaluate/coordinates.py b/src/mdevaluate/coordinates.py index bfeac00..fc50995 100755 --- a/src/mdevaluate/coordinates.py +++ b/src/mdevaluate/coordinates.py @@ -1,10 +1,10 @@ from functools import partial, wraps from copy import copy from .logging import logger -from typing import Optional, Callable +from typing import Optional, Callable, List, Tuple import numpy as np -import numpy.typing as npt +from numpy.typing import ArrayLike, NDArray from scipy.spatial import KDTree from .atoms import AtomSubset @@ -17,7 +17,7 @@ class UnknownCoordinatesMode(Exception): pass -class CoordinateFrame(np.ndarray): +class CoordinateFrame(NDArray): _known_modes = ("pbc", "whole", "nojump") @property @@ -99,7 +99,7 @@ class CoordinateFrame(np.ndarray): box=None, mode=None, ): - obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides) + obj = NDArray.__new__(subtype, shape, dtype, buffer, offset, strides) obj.coordinates = coordinates obj.step = step @@ -319,7 +319,7 @@ class CoordinatesMap: return CoordinatesMap(self.coordinates.pbc, self.function) -def rotate_axis(coords: npt.ArrayLike, axis: npt.ArrayLike) -> np.ndarray: +def rotate_axis(coords: ArrayLike, axis: ArrayLike) -> NDArray: """ Rotate a set of coordinates to a given axis. """ @@ -352,8 +352,8 @@ def rotate_axis(coords: npt.ArrayLike, axis: npt.ArrayLike) -> np.ndarray: def spherical_radius( - frame: CoordinateFrame, origin: Optional[npt.ArrayLike] = None -) -> np.ndarray: + frame: CoordinateFrame, origin: Optional[ArrayLike] = None +) -> NDArray: """ Transform a frame of cartesian coordinates into the spherical radius. If origin=None, the center of the box is taken as the coordinates' origin. @@ -363,7 +363,7 @@ def spherical_radius( return ((frame - origin) ** 2).sum(axis=-1) ** 0.5 -def polar_coordinates(x: npt.ArrayLike, y: npt.ArrayLike) -> (np.ndarray, np.ndarray): +def polar_coordinates(x: ArrayLike, y: ArrayLike) -> (NDArray, NDArray): """Convert cartesian to polar coordinates.""" radius = (x**2 + y**2) ** 0.5 phi = np.arctan2(y, x) @@ -371,8 +371,8 @@ def polar_coordinates(x: npt.ArrayLike, y: npt.ArrayLike) -> (np.ndarray, np.nda def spherical_coordinates( - x: npt.ArrayLike, y: npt.ArrayLike, z: npt.ArrayLike -) -> (np.ndarray, np.ndarray, np.ndarray): + x: ArrayLike, y: ArrayLike, z: ArrayLike +) -> (NDArray, NDArray, NDArray): """Convert cartesian to spherical coordinates.""" xy, phi = polar_coordinates(x, y) radius = (x**2 + y**2 + z**2) ** 0.5 @@ -384,8 +384,8 @@ def selector_radial_cylindrical( atoms: CoordinateFrame, r_min: float, r_max: float, - origin: Optional[npt.ArrayLike] = None, -) -> np.ndarray: + origin: Optional[ArrayLike] = None, +) -> NDArray: box = atoms.box atoms = atoms % np.diag(box) if origin is None: @@ -397,7 +397,7 @@ def selector_radial_cylindrical( def map_coordinates( - func: Callable[[CoordinateFrame, ...], np.ndarray] + func: Callable[[CoordinateFrame, ...], NDArray] ) -> Callable[..., CoordinatesMap]: @wraps(func) def wrapped(coordinates: Coordinates, **kwargs) -> CoordinatesMap: @@ -408,14 +408,14 @@ def map_coordinates( @map_coordinates def center_of_masses( - frame: CoordinateFrame, atoms=None, shear: bool = False -) -> np.ndarray: - if atoms is None: - atoms = list(range(len(frame))) - res_ids = frame.residue_ids[atoms] - masses = frame.masses[atoms] + frame: CoordinateFrame, atom_indices=None, shear: bool = False +) -> NDArray: + if atom_indices is None: + atom_indices = list(range(len(frame))) + res_ids = frame.residue_ids[atom_indices] + masses = frame.masses[atom_indices] if shear: - coords = frame[atoms] + coords = frame[atom_indices] box = frame.box sort_ind = res_ids.argsort(kind="stable") i = np.concatenate([[0], np.where(np.diff(res_ids[sort_ind]) > 0)[0] + 1]) @@ -423,7 +423,7 @@ def center_of_masses( cor = pbc_diff(coords, coms, box) coords = coms + cor else: - coords = frame.whole[atoms] + coords = frame.whole[atom_indices] mask = np.bincount(res_ids)[1:] != 0 positions = np.array( [ @@ -437,8 +437,8 @@ def center_of_masses( @map_coordinates def pore_coordinates( - frame: CoordinateFrame, origin: npt.ArrayLike, sym_axis: str = "z" -) -> np.ndarray: + frame: CoordinateFrame, origin: ArrayLike, sym_axis: str = "z" +) -> NDArray: """ Map coordinates of a pore simulation so the pore has cylindrical symmetry. @@ -459,17 +459,17 @@ def pore_coordinates( @map_coordinates def vectors( frame: CoordinateFrame, - atoms_indices_a: npt.ArrayLike, - atoms_indices_b: npt.ArrayLike, + atom_indices_a: ArrayLike, + atom_indices_b: ArrayLike, normed: bool = False, -) -> np.ndarray: +) -> NDArray: """ Compute the vectors between the atoms of two subsets. Args: frame: The Coordinates object the atoms will be taken from - atoms_indices_a: Mask or indices of the first atom subset - atoms_indices_b: Mask or indices of the second atom subset + atom_indices_a: Mask or indices of the first atom subset + atom_indices_b: Mask or indices of the second atom subset normed (opt.): If the vectors should be normed The definition of atoms_a/b can be any possible subript of a numpy array. @@ -492,10 +492,10 @@ def vectors( ]) """ box = frame.box - coords_a = frame[atoms_indices_a] + coords_a = frame[atom_indices_a] if len(coords_a.shape) > 2: coords_a = coords_a.mean(axis=0) - coords_b = frame[atoms_indices_b] + coords_b = frame[atom_indices_b] if len(coords_b.shape) > 2: coords_b = coords_b.mean(axis=0) vec = pbc_diff(coords_a, coords_b, box=box) @@ -507,8 +507,8 @@ def vectors( @map_coordinates def dipole_vector( - frame: CoordinateFrame, atom_indices: npt.ArrayLike, normed: bool = None -) -> np.ndarray: + frame: CoordinateFrame, atom_indices: ArrayLike, normed: bool = None +) -> NDArray: coords = frame.whole[atom_indices] res_ids = frame.residue_ids[atom_indices] charges = frame.charges[atom_indices] @@ -525,9 +525,9 @@ def dipole_vector( @map_coordinates def sum_dipole_vector( coordinates: CoordinateFrame, - atom_indices: npt.ArrayLike, + atom_indices: ArrayLike, normed: bool = True, -) -> np.ndarray: +) -> NDArray: coords = coordinates.whole[atom_indices] charges = coordinates.charges[atom_indices] dipole = np.array([c * charges for c in coords.T]).T @@ -539,11 +539,11 @@ def sum_dipole_vector( @map_coordinates def normal_vectors( frame: CoordinateFrame, - atom_indices_a: npt.ArrayLike, - atom_indices_b: npt.ArrayLike, - atom_indices_c: npt.ArrayLike, + atom_indices_a: ArrayLike, + atom_indices_b: ArrayLike, + atom_indices_c: ArrayLike, normed: bool = True, -) -> np.ndarray: +) -> NDArray: coords_a = frame[atom_indices_a] coords_b = frame[atom_indices_b] coords_c = frame[atom_indices_c] @@ -571,8 +571,8 @@ def displacements_without_drift( @map_coordinates def cylindrical_coordinates( - frame: CoordinateFrame, origin: npt.ArrayLike = None -) -> np.ndarray: + frame: CoordinateFrame, origin: ArrayLike = None +) -> NDArray: if origin is None: origin = np.diag(frame.box) / 2 x = frame[:, 0] - origin[0] @@ -584,10 +584,10 @@ def cylindrical_coordinates( def layer_of_atoms( - atoms: CoordinateFrame, - thickness: float, - plane_normal: npt.ArrayLike, - plane_offset: Optional[npt.ArrayLike] = np.array([0, 0, 0]), + atoms: CoordinateFrame, + thickness: float, + plane_normal: ArrayLike, + plane_offset: Optional[ArrayLike] = np.array([0, 0, 0]), ) -> np.array: if plane_offset is None: np.array([0, 0, 0]) @@ -597,13 +597,13 @@ def layer_of_atoms( def next_neighbors( - atoms: CoordinateFrame, - query_atoms: Optional[CoordinateFrame] = None, - number_of_neighbors: int = 1, - distance_upper_bound: float = np.inf, - distinct: bool = False, - **kwargs -) -> (np.ndarray, np.ndarray): + atoms: CoordinateFrame, + query_atoms: Optional[CoordinateFrame] = None, + number_of_neighbors: int = 1, + distance_upper_bound: float = np.inf, + distinct: bool = False, + **kwargs +) -> Tuple[List, List]: """ Find the N next neighbors of a set of atoms. @@ -634,7 +634,15 @@ def next_neighbors( query_atoms, number_of_neighbors + dnn, distance_upper_bound=distance_upper_bound, - ) + ) + distances = distances[:, dnn:] + indices = indices[:, dnn:] + distances_new = [] + indices_new = [] + for dist, ind in zip(distances, indices): + distances_new.append(dist[dist <= distance_upper_bound]) + indices_new.append(ind[dist <= distance_upper_bound]) + return distances_new, indices_new else: atoms_pbc, atoms_pbc_index = pbc_points( query_atoms, box, thickness=distance_upper_bound + 0.1, index=True, **kwargs @@ -644,7 +652,52 @@ def next_neighbors( query_atoms, number_of_neighbors + dnn, distance_upper_bound=distance_upper_bound, - ) - indices = atoms_pbc_index[indices] + ) + distances = distances[:, dnn:] + indices = indices[:, dnn:] + distances_new = [] + indices_new = [] + for dist, ind in zip(distances, indices): + distances_new.append(dist[dist <= distance_upper_bound]) + indices_new.append(atoms_pbc_index[ind[dist <= distance_upper_bound]]) + return distances_new, indices_new - return distances[:, dnn:], indices[:, dnn:] + +def number_of_neighbors( + atoms: CoordinateFrame, + query_atoms: Optional[CoordinateFrame] = None, + r_max: float = 1, + distinct: bool = False, + **kwargs +) -> Tuple[List, List]: + """ + Find the N next neighbors of a set of atoms. + + Args: + atoms: + The reference atoms and also the atoms which are queried if `query_atoms` + is net provided + query_atoms (opt.): If this is not None, these atoms will be queried + r_max (float, opt.): + Upper bound of the distance between neighbors + distinct (bool, opt.): + If this is true, the atoms and query atoms are taken as distinct sets of + atoms + """ + dnn = 0 + if query_atoms is None: + query_atoms = atoms + dnn = 1 + elif not distinct: + dnn = 1 + + box = atoms.box + if np.all(np.diag(np.diag(box)) == box): + atoms = atoms % np.diag(box) + tree = KDTree(atoms, boxsize=np.diag(box)) + else: + atoms_pbc = pbc_points(query_atoms, box, thickness=r_max + 0.1, **kwargs) + tree = KDTree(atoms_pbc) + + num_of_neighbors = tree.query_ball_point(query_atoms, r_max, return_length=True) + return num_of_neighbors - dnn From 87ffa1e67ed11f1c9a2574bd58748ea54ab456c8 Mon Sep 17 00:00:00 2001 From: Sebastian Kloth Date: Tue, 16 Jan 2024 13:39:48 +0100 Subject: [PATCH 73/73] Fixed to find dimension of selector output --- src/mdevaluate/correlation.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/src/mdevaluate/correlation.py b/src/mdevaluate/correlation.py index 786f57b..a8b3265 100644 --- a/src/mdevaluate/correlation.py +++ b/src/mdevaluate/correlation.py @@ -22,7 +22,7 @@ def log_indices(first: int, last: int, num: int = 100) -> np.ndarray: def shifted_correlation( function: Callable, frames: Coordinates, - selector: ArrayLike = None, + selector: Optional[Callable] = None, segments: int = 10, skip: float = 0.1, window: float = 0.5, @@ -102,7 +102,12 @@ def shifted_correlation( if len(index) == 0: return np.zeros(len(shifted_idx)) - elif isinstance(index[0], int) or isinstance(index[0], bool): + elif ( + isinstance(index[0], int) + or isinstance(index[0], bool) + or isinstance(index[0], np.integer) + or isinstance(index[0], np.bool_) + ): return get_correlation(frames, start_frame, index, shifted_idx) else: correlations = [] @@ -110,7 +115,12 @@ def shifted_correlation( if len(ind) == 0: correlations.append(np.zeros(len(shifted_idx))) - elif isinstance(ind[0], int) or isinstance(ind[0], bool): + elif ( + isinstance(ind[0], int) + or isinstance(ind[0], bool) + or isinstance(ind[0], np.integer) + or isinstance(ind[0], np.bool_) + ): correlations.append( get_correlation(frames, start_frame, ind, shifted_idx) )

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