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Adjusted functions to work with new pbc_poits

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sebastiankloth 2022-11-03 16:20:04 +01:00
parent 0bd82ee281
commit a38e601fcb
1 changed files with 6 additions and 6 deletions
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12
mdevaluate/distribution.py
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@ -4,7 +4,7 @@ from .coordinates import rotate_axis, polar_coordinates, spherical_coordinates
from .atoms import next_neighbors from .atoms import next_neighbors
from .autosave import autosave_data from .autosave import autosave_data
from .utils import runningmean from .utils import runningmean
from .pbc import pbc_diff, pbc_points, make_PBC from .pbc import pbc_diff, pbc_points
from .logging import logger from .logging import logger
from scipy import spatial from scipy import spatial
@ -144,7 +144,7 @@ def rdf(atoms_a, atoms_b=None, bins=None, box=None, kind=None, chunksize=50000,
def pbc_tree_rdf(atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs): def pbc_tree_rdf(atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs):
if box is None: if box is None:
box = atoms_a.box.diagonal() box = atoms_a.box.diagonal()
all_coords = pbc_points(pbc_diff(atoms_b,box=box), box, thickness=np.amax(bins)+0.1, center=0) all_coords = pbc_points(atoms_b, box, thickness=np.amax(bins)+0.1)
to_tree = spatial.cKDTree(all_coords) 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 = 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] dist = dist[dist < np.inf]
@ -159,8 +159,8 @@ def pbc_tree_rdf(atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=
def pbc_spm_rdf(atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs): def pbc_spm_rdf(atoms_a, atoms_b=None, bins=None, box=None, exclude=0, returnx=False, **kwargs):
if box is None: if box is None:
box = atoms_a.box.diagonal() box = atoms_a.box
all_coords = pbc_points(pbc_diff(atoms_b,box=box), box, thickness=np.amax(bins)+0.1, center=0) all_coords = pbc_points(atoms_b, box, thickness=np.amax(bins)+0.1)
to_tree = spatial.cKDTree(all_coords) to_tree = spatial.cKDTree(all_coords)
if all_coords.nbytes/1024**3 * len(atoms_a) < 2: if all_coords.nbytes/1024**3 * len(atoms_a) < 2:
from_tree = spatial.cKDTree(pbc_diff(atoms_a,box=box)) from_tree = spatial.cKDTree(pbc_diff(atoms_a,box=box))
@ -361,7 +361,7 @@ def next_neighbor_distribution(atoms, reference=None, number_of_neighbors=4, bin
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): 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):
def dist_DltA(D, H, A, box, max_dist=0.35): def dist_DltA(D, H, A, box, max_dist=0.35):
ppoints, pind = make_PBC(D, box, thickness=max_dist+0.1, index=True) ppoints, pind = pbc_points(D, box, thickness=max_dist+0.1, index=True)
Dtree = cKDTree(ppoints) Dtree = cKDTree(ppoints)
Atree = cKDTree(A) Atree = cKDTree(A)
pairs = Dtree.sparse_distance_matrix(Atree, max_dist, output_type='ndarray') pairs = Dtree.sparse_distance_matrix(Atree, max_dist, output_type='ndarray')
@ -371,7 +371,7 @@ def hbonds(D, H, A, box, DA_lim=0.35, HA_lim=0.35, min_cos=np.cos(30*np.pi/180),
return pairs return pairs
def dist_AltD(D, H, A, box, max_dist=0.35): def dist_AltD(D, H, A, box, max_dist=0.35):
ppoints, pind = make_PBC(A, box, thickness=max_dist+0.1, index=True) ppoints, pind = pbc_points(A, box, thickness=max_dist+0.1, index=True)
Atree = cKDTree(ppoints) Atree = cKDTree(ppoints)
Dtree = cKDTree(D) Dtree = cKDTree(D)
pairs = Atree.sparse_distance_matrix(Dtree, max_dist, output_type='ndarray') pairs = Atree.sparse_distance_matrix(Dtree, max_dist, output_type='ndarray')