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…er_atoms and per_frame
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Original file line number | Diff line number | Diff line change |
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from typing import List | ||
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import numba as nb | ||
import numpy as np | ||
import numpy.typing as npt | ||
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@nb.njit(fastmath=True, error_model="numpy") # type: ignore # , cache=True) #(parallel=True) | ||
def calculate(frames: npt.NDArray[np.float32]) -> npt.NDArray[np.float32]: | ||
"""calculate the progression of the lindemann index over the frames. | ||
Args: | ||
frames: numpy array of shape(frames,atoms) | ||
Returns: | ||
npt.NDArray[np.float32]: Returns 1D array with the progression of the lindeman index per frame of shape(frames) | ||
""" | ||
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first = True | ||
dt = frames.dtype | ||
natoms = len(frames[0]) | ||
nframes = len(frames) | ||
len_frames = len(frames) | ||
array_mean = np.zeros((natoms, natoms), dtype=dt) | ||
array_var = np.zeros((natoms, natoms), dtype=dt) | ||
iframe = dt.type(1) | ||
lindex_array = np.zeros((len_frames), dtype=dt) | ||
for q, coords in enumerate(frames): | ||
n, p = coords.shape | ||
array_distance = np.zeros((n, n), dtype=dt) | ||
for i in range(n): | ||
for j in range(i + 1, n): | ||
d = 0.0 | ||
for k in range(p): | ||
d += (coords[i, k] - coords[j, k]) ** dt.type(2) | ||
array_distance[i, j] = np.sqrt(d) | ||
array_distance += array_distance.T | ||
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################################################################################# | ||
# update mean and var arrays based on Welford algorithm suggested by Donald Knuth | ||
################################################################################# | ||
for i in range(natoms): | ||
for j in range(i + 1, natoms): | ||
xn = array_distance[i, j] | ||
mean = array_mean[i, j] | ||
var = array_var[i, j] | ||
delta = xn - mean | ||
# update mean | ||
array_mean[i, j] = mean + delta / iframe | ||
# update variance | ||
array_var[i, j] = var + delta * (xn - array_mean[i, j]) | ||
iframe += 1 # type: ignore[assignment] | ||
if iframe > nframes + 1: | ||
break | ||
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for i in range(natoms): | ||
for j in range(i + 1, natoms): | ||
array_mean[j, i] = array_mean[i, j] | ||
array_var[j, i] = array_var[i, j] | ||
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if first: | ||
lindemann_indices = 0 | ||
first = False | ||
else: | ||
np.fill_diagonal(array_mean, 1) | ||
lindemann_indices = np.zeros((natoms), dtype=dt) # type: ignore[assignment] | ||
lindemann_indices = np.divide(np.sqrt(np.divide(array_var, iframe - 1)), array_mean) # type: ignore[assignment] | ||
lindemann_indices = np.mean( | ||
np.asarray([np.mean(lin[lin != 0]) for lin in lindemann_indices]) # type: ignore[attr-defined] | ||
) | ||
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lindex_array[q] = lindemann_indices | ||
return lindex_array | ||
@nb.njit(fastmath=True, parallel=False) | ||
def calculate(positions): | ||
num_frames, num_atoms, _ = positions.shape | ||
num_distances = num_atoms * (num_atoms - 1) // 2 | ||
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mean_distances = np.zeros(num_distances, dtype=np.float32) | ||
m2_distances = np.zeros(num_distances, dtype=np.float32) | ||
linde_per_frame = np.zeros(num_frames, dtype=np.float32) | ||
for frame in range(num_frames): | ||
index = 0 | ||
frame_count = frame + 1 | ||
for i in range(num_atoms): | ||
for j in range(i + 1, num_atoms): | ||
dist = 0.0 | ||
for k in range(3): | ||
dist += (positions[frame, i, k] - positions[frame, j, k]) ** 2 | ||
dist = np.sqrt(dist) | ||
delta = dist - mean_distances[index] | ||
mean_distances[index] += delta / frame_count | ||
delta2 = dist - mean_distances[index] | ||
m2_distances[index] += delta * delta2 | ||
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index += 1 | ||
linde_per_frame[frame] = np.mean(np.sqrt(m2_distances / frame_count) / mean_distances) | ||
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return linde_per_frame |