atomic_Distance_Density

esta.plot.atomic_Distance_Density

atom_distance_density

script to calculate the atomic distance density 
for a specific bond of atoms in the POSCAR file


Note:

A Gaussian distribution function is used for the delta function:

The probability density for the Gaussian distribution is

p(x) = rac{1}{\sqrt{ 2 \pi \sigma^2 }} e^{ - rac{ (x - \mu)^2 } {2 \sigma^2} },

where \mu is the mean and \sigma the standard deviation. 
The square of the standard deviation, \sigma^2, is called the variance.

The function has its peak at the mean, and its “spread” increases with 
the standard deviation (the function reaches 0.607 times its maximum 
at x + \sigma and x - \sigma [2]). This implies that numpy.random.normal 
is more likely to return samples lying close to the mean, rather than 
those far away.

Parameters/Inputs

x : ?

Returns

`atomic distance density` array  and distance
array are returned.

See Also

--------

add other related things here.

Notes

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Examples

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distance_array(natom_type, tau_cartesian)

calculate the distance array among different atoms

distance_array1(natom_type, atm_type, tau_cartesian)

calculate the distance array among different atoms

remove_duplicate(list_entries)

remove duplicates ??

gaussian_distribution(x0, x, sigma)

generate a gaussian dist for data along 1D axis

dos(array_distribution, sigma, dgrid_min=None, dgrid_max=None, normalise=False)

Now calculation of atomic distance density for given sigma of gauss distribution

get_neighbors(label, tau_cartesian, n_pts, n_neighbors)

get all neighbors of N points (n_pts= atomic positions) along with distance

input:

list: label: label of pts/atoms 
array: tau_cartesian, atomic-positions/pts in cartesian coordinates
integer: n_pts, no. of pts/atomic-coordinate whose neighbors are 
    to be found
integer: n_neighbors, integer for how many eneighbours

output:

arrray: ind, array of indices of neighbors including self 
array: dist, array of distances from neighbors incuding self distance (self 
    distance is zero, as we know)