Usage

Command Line

AtomAccess is best run through its web interface, but advanced users can also run it from the command line.

atom_access <molecule.xyz>

where <molecule.xyz> is the name of an .xyz file containing a set of atomic coordinates. The .xyz file must have have a specific (XMOL) format, as detailed in File Formats.

The following additional command line arguments can be change the default settings and enable additional options.

Optional Argument(s)	Description
`--atom <integer>`	Select the atom for which to calculate the accessibility. `<integer>` is the index of the desired atom (starting from 1). Default: 1
`--cutoff <number>`	Set the radial cutoff distance from the atom of interest. `<number>` is \(r_{\text{max}}\) in Angstroms. Default: 5
`--density <integer>`	Control the number of rays emanating from the atom of interest using the Zaremba-Conroy-Wolfsberg (ZCW) density index.[1][2][3] `<integer>` is an integer between 0-15 - see Table 1 Default: 10
`--quiet`	Suppress printing to the terminal.
`--save_rays`	Save the unblocked ray objects.
`--plot`	Plot the unblocked rays in a web browser using plotly

Table 1: Number of rays for each ZCW density index value
ZCW density index \(\rho\)	Number of rays
0	21
1	34
2	55
3	89
4	144
5	233
6	377
7	610
8	987
9	1597
10	2584
11	4181
12	6765
13	10946
14	17711
15	28657

Scripting

Thanks to its modular design, AtomAccess can be included in your own python scripts with ease.

As an example, the following script calculates the steric hindrance for a molecule with a given set of atomic coordinates.

import xyz_py as xyzp
import atom_access as aa

# Load xyz file
labels, coords = xyzp.load_xyz('benzene.xyz')
# Remove indices from labels
# i.e. H2 --> H
labels_nn = xyzp.remove_label_indices(labels)

# Trace the rays
# This returns a list of blocked and unblocked rays,
# and the number of atoms which were excluded
# by the radial cutoff
blocked, unblocked, n_cut = aa.trace_rays(
    labels_nn,
    coords,
    centre=0,
    radial_cutoff=5,
    zcw_density=12
)

# calculate the percentage of unblocked rays
total_rays = len(blocked) + len(unblocked)
pc_unblocked = len(unblocked) / (total_rays) * 100.

# Cluster rays using agglomerative clustering
cluster_id = aa.cluster_rays(unblocked, zcw_density=12)

# Quit if the rays are all blocked
if not cluster_id.size:
    print('All Rays Blocked!')
    sys.exit()

# Calculate size of each cluster as a percentage of the total number of rays
clust_percent, _ = aa.cluster_size(cluster_id, total_rays)

print(
    f'\nThere are {clust_percent.size:d} clusters'
)

for idx, x in enumerate(clust_percent):
    print(
        'Cluster {:d} contains {:.2f} % solid angle'.format(
            idx + 1, x
        )
    )

# Print output file
aa.generate_output(
    'output.txt',
    zcw_density=12,
    pc_unblocked=pc_unblocked,
    clust_percent=clust_percent,
    radial_cutoff=5,
    no_header=True
)

To learn more about the available functions and their arguments, consult the API documentation.