IPKM/mdevaluate
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Changed dynamic calculations to include 2D cases

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This commit is contained in:
Sebastian Kloth
2024-01-30 16:46:11 +01:00
parent 77771738ab
commit f527d25864
1 changed files with 31 additions and 1 deletions
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32
src/mdevaluate/correlation.py
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@@ -2,7 +2,7 @@ from typing import Callable, Optional
import numpy as np
from numpy.typing import ArrayLike
from scipy.special import legendre
from scipy.special import legendre, jn
import dask.array as darray
from functools import partial
from scipy.spatial import KDTree
@@ -183,6 +183,12 @@ def msd(
displacements = displacements_without_drift(start_frame, end_frame, trajectory)
if axis == "all":
return (displacements**2).sum(axis=1).mean()
elif axis == "xy" or axis == "yx":
return (displacements[:, [0, 1]]**2).sum(axis=1).mean()
elif axis == "xz" or axis == "zx":
return (displacements[:, [0, 2]]**2).sum(axis=1).mean()
elif axis == "yz" or axis == "zy":
return (displacements[:, [1, 2]]**2).sum(axis=1).mean()
elif axis == "x":
return (displacements[:, 0] ** 2).mean()
elif axis == "y":
@@ -211,6 +217,15 @@ def isf(
if axis == "all":
distance = (displacements**2).sum(axis=1) ** 0.5
return np.sinc(distance * q / np.pi).mean()
elif axis == "xy" or axis == "yx":
distance = (displacements[:, [0, 1]]**2).sum(axis=1) ** 0.5
return np.real(jn(0, distance * q / np.pi)).mean()
elif axis == "xz" or axis == "zx":
distance = (displacements[:, [0, 2]]**2).sum(axis=1) ** 0.5
return np.real(jn(0, distance * q / np.pi)).mean()
elif axis == "yz" or axis == "zy":
distance = (displacements[:, [1, 2]]**2).sum(axis=1) ** 0.5
return np.real(jn(0, distance * q / np.pi)).mean()
elif axis == "x":
distance = np.abs(displacements[:, 0])
return np.mean(np.cos(np.abs(q * distance)))
@@ -262,6 +277,12 @@ def van_hove_self(
vectors = displacements_without_drift(start_frame, end_frame, trajectory)
if axis == "all":
delta_r = (vectors**2).sum(axis=1) ** 0.5
elif axis == "xy" or axis == "yx":
return (vectors[:, [0, 1]]**2).sum(axis=1) ** 0.5
elif axis == "xz" or axis == "zx":
return (vectors[:, [0, 2]]**2).sum(axis=1) ** 0.5
elif axis == "yz" or axis == "zy":
return (vectors[:, [1, 2]]**2).sum(axis=1) ** 0.5
elif axis == "x":
delta_r = np.abs(vectors[:, 0])
elif axis == "y":
@@ -423,6 +444,15 @@ def non_gaussian_parameter(
if axis == "all":
r = (vectors**2).sum(axis=1)
dimensions = 3
elif axis == "xy" or axis == "yx":
r = (vectors[:, [0, 1]]**2).sum(axis=1)
dimensions = 2
elif axis == "xz" or axis == "zx":
r = (vectors[:, [0, 2]]**2).sum(axis=1)
dimensions = 2
elif axis == "yz" or axis == "zy":
r = (vectors[:, [1, 2]]**2).sum(axis=1)
dimensions = 2
elif axis == "x":
r = vectors[:, 0] ** 2
dimensions = 1