def rdf(coords_a,
coords_b,
binsize=0.002,
cutoff=1.5,
periodic=None,
normalize=True):
"""Calculate the radial distribution function of *coords_a* against
*coords_b*.
**Parameters**
- coords_a: np.ndarray((3, NA))
first set of coordinates
- coords_b: np.ndarray((3, NB))
coordinates to calculate the RDF against
- periodic: np.ndarray((3, 3)) or None
Wether or not include periodic images in the calculation
- normalize: True or False
gromacs-like normalization
- cutoff:
where to cutoff the RDF
"""
period = periodic[0, 0], periodic[1, 1], periodic[2, 2]
distances = distances_within(coords_a, coords_b, cutoff,
np.array(period, dtype=np.double))
n_a = len(coords_a)
n_b = len(coords_b)
volume = periodic[0, 0] * periodic[1, 1] * periodic[2, 2]
int_distances = np.rint(distances / binsize).astype(int)
hist = np.bincount(int_distances)
bin_edges = np.arange(len(hist) + 1) * binsize
if normalize:
dr = binsize
normfac = volume / (n_a * n_b)
# Normalize this by a sphere shell
for i, r in enumerate(bin_edges[1:]):
hist[i] /= ((4.0 / 3.0 * np.pi * (r + 0.5 * dr)**3) -
(4.0 / 3.0 * np.pi * (r - 0.5 * dr)**3))
# Normalize by density
hist = hist * normfac
# Cutting up to rmax value
width = cutoff / binsize + 1
return bin_edges[0:width], hist[0:width]
def rdf(coords_a, coords_b, binsize=0.002,
cutoff=1.5, periodic=None, normalize=True):
"""Calculate the radial distribution function of *coords_a* against
*coords_b*.
**Parameters**
- coords_a: np.ndarray((3, NA))
first set of coordinates
- coords_b: np.ndarray((3, NB))
coordinates to calculate the RDF against
- periodic: np.ndarray((3, 3)) or None
Wether or not include periodic images in the calculation
- normalize: True or False
gromacs-like normalization
- cutoff:
where to cutoff the RDF
"""
period = periodic[0, 0], periodic[1,1], periodic[2,2]
distances = distances_within(coords_a, coords_b, cutoff,
np.array(period, dtype=np.double))
n_a = len(coords_a)
n_b = len(coords_b)
volume = periodic[0, 0] * periodic[1, 1] * periodic[2, 2]
int_distances = np.rint(distances/binsize).astype(int)
hist = np.bincount(int_distances)
bin_edges = np.arange(len(hist)+1) * binsize
if normalize:
dr = binsize
normfac = volume/(n_a*n_b)
# Normalize this by a sphere shell
for i, r in enumerate(bin_edges[1:]):
hist[i] /= ((4.0/3.0 * np.pi * (r + 0.5*dr)**3)
- (4.0/3.0 * np.pi * (r- 0.5*dr)**3))
# Normalize by density
hist = hist * normfac
# Cutting up to rmax value
width = cutoff/binsize + 1
return bin_edges[0:width], hist[0:width]
def order_par():
# Hidden
visible = visible_atoms()
r_array = current_system().r_array[visible]
box = current_system().box_vectors
local_dens = []
for r in r_array:
local_dens.append(len(distances_within([r], r_array,
0.60, periodic=box.diagonal())))
local_dens = np.array(local_dens)
return local_dens
def order_par():
# Hidden
visible = visible_atoms()
r_array = current_system().r_array[visible]
box = current_system().box_vectors
local_dens = []
for r in r_array:
local_dens.append(
len(distances_within([r], r_array, 0.60, periodic=box.diagonal())))
local_dens = np.array(local_dens)
return local_dens
