def visit_tree(node):
datastr = {'value': node.get_count(),
'id': node.get_id(),
'children': []}
# Add label text if possible
for br in (node.left, node.right):
if br is None:
continue
datastr['children'].append(visit_tree(br))
# Now the hue and the struct name. If this is a leaf, just use the structure value
if len(datastr['children']) == 0:
if Emax > Emin:
hue = (Emax - CalcEnergy.get(aC.structures[node.get_id()])) \
/(Emax - Emin)*240.0
else:
hue = 120.0
struct_name = aC.structures[node.get_id()].info['name']
else:
# Otherwise, an average
hue = 0.0
struct_name = ""
for c in datastr['children']:
hue += c['hue']
struct_name += c['struct_name'] + ',\n'
hue /= len(datastr['children'])
struct_name = struct_name[:-2] # Remove last newline and comma
datastr['struct_name'] = struct_name
datastr['hue'] = hue
return datastr
def tree2json(aC, pC, w=500, h=500):
"""Produce a dictionary representation of a Scipy cluster tree object
suitable to be saved as JSON"""
max_dist = pC.get_max_cluster_dist()
# First find absolute minimum and maximum energy (for hue)
struct_Es = CalcEnergy.get(aC)
Emin = min(struct_Es)
Emax = max(struct_Es)
# Create the master node by using the tree root, then visit recursively
def visit_tree(node):
datastr = {
'value': node.get_count(),
'id': node.get_id(),
'children': []
}
# Add label text if possible
for br in (node.left, node.right):
if br is None:
continue
datastr['children'].append(visit_tree(br))
# Now the hue and the struct name. If this is a leaf, just use the
# structure value
if len(datastr['children']) == 0:
if Emax > Emin:
hue = (Emax - CalcEnergy.get(aC.structures[node.get_id()])) \
/ (Emax - Emin)*240.0
else:
hue = 120.0
struct_name = aC.structures[node.get_id()].info['name']
else:
# Otherwise, an average
hue = 0.0
struct_name = ""
for c in datastr['children']:
hue += c['hue']
struct_name += c['struct_name'] + ',\n'
hue /= len(datastr['children'])
struct_name = struct_name[:-2] # Remove last newline and comma
datastr['struct_name'] = struct_name
datastr['hue'] = hue
return datastr
return visit_tree(pC.get_hier_tree())
def parsegene_energy(c):
return np.array(CalcEnergy.get(c))
# Then create an AtomsCollection
if not args.pkl:
aC = AtomsCollection(args.input_files,
info={'name': args.seedname},
cell_reduce=True,
progress=True)
else:
aC = AtomsCollection([])
for f in args.input_files:
aC += AtomsCollection.load(f)
# Take only n lowest energies if required
if (args.n is not None):
# Sort by energy
CalcEnergy.get(aC, store_array=True)
aC = aC.sorted_byarray('calc_energy')[:args.n]
#discard_Z = args.Z is not None
# Calculate genomes
nrange = (0, 1) if args.gene_clamp else (None, None)
ndist = 1.0 if args.gene_clamp else None
pC = PhylogenCluster(aC, genes=[], norm_range=nrange, norm_dist=ndist)
pC.set_genes(gene_list, load_arrays=True)
if args.savepkl is not None:
# Save the structure
pC.save_collection(os.path.join(args.savepkl, args.seedname + '.pkl'))
# And now for the output
def __main__():
# So, first parse the command line arguments
parser = ap.ArgumentParser()
# Main argument
parser.add_argument('seedname',
type=str,
default=None,
help="Seedname of the job")
parser.add_argument('input_files',
type=str,
nargs='+',
default=None,
help="Files to load and analyse")
# Optional arguments
# Boolean args
parser.add_argument('-pkl',
action='store_true',
default=False,
help="Input is one or more AtomsCollections"
" in PICKLE format")
parser.add_argument('-tree',
action='store_true',
default=False,
help="Create JSON tree file")
parser.add_argument('-distmat',
action='store_true',
default=False,
help="Save a distance matrix file")
parser.add_argument('-forcemat',
action='store_true',
default=False,
help="Create JSON distance matrix file for force "
"layout")
parser.add_argument('-genomes',
action='store_true',
default=False,
help="Save genomes text file")
parser.add_argument('-gene_png',
action='store_true',
default=False,
help="Save genomes png file")
parser.add_argument('-gene_clamp',
action='store_true',
default=True,
help="Clamp genes between 0 and 1")
# Value args
parser.add_argument('-n',
type=int,
default=None,
help="Number of lowest energy structures to keep")
# parser.add_argument('-Z', type=int, default=None,
# help="Expected numbers of molecules in cell (discard structures "
# "that don't agree)")
parser.add_argument('-corrmat_minforce',
type=float,
default=0.01,
help="Correlation matrix minimum force")
parser.add_argument('-corrmat_maxforce',
type=float,
default=1.0,
help="Correlation matrix maximum force")
parser.add_argument('-savepkl',
type=str,
default=None,
help="Directory to save collection as pickle file")
args = parser.parse_args()
# First, parse the gene list
gene_list = load_genefile(args.seedname + '.gene')
# Then create an AtomsCollection
if not args.pkl:
aC = AtomsCollection(args.input_files,
info={'name': args.seedname},
cell_reduce=True,
progress=True)
else:
aC = AtomsCollection([])
for f in args.input_files:
aC += AtomsCollection.load(f)
# Take only n lowest energies if required
if (args.n is not None):
# Sort by energy
CalcEnergy.get(aC, store_array=True)
aC = aC.sorted_byarray('calc_energy')[:args.n]
#discard_Z = args.Z is not None
# Calculate genomes
nrange = (0, 1) if args.gene_clamp else (None, None)
ndist = 1.0 if args.gene_clamp else None
pC = PhylogenCluster(aC, genes=[], norm_range=nrange, norm_dist=ndist)
pC.set_genes(gene_list, load_arrays=True)
if args.savepkl is not None:
# Save the structure
pC.save_collection(os.path.join(args.savepkl, args.seedname + '.pkl'))
# And now for the output
if args.genomes:
with open(args.seedname + "_genomes.dat", 'w') as ofile:
genvec, legend = pC.get_genome_vectors()
# Create a header
header = ('Structure\t'
'{0}\n').format('\t'.join([
'\t'.join([
'{0}_{1}'.format(l[0], i + 1)
for i in range(l[1])
]) for l in legend
]))
ofile.write(header)
try:
for i, gen in enumerate(genvec):
ofile.write(aC.structures[i].info['name'] + '\t' +
' '.join([str(x) for x in gen]) + '\n')
except KeyError:
# Someone has no name!
raise RuntimeError('Not all structures have a name, '
'can\'t print out genomes as table')
if args.gene_png and _has_matplotlib:
with open(args.seedname + "_genomes.png", 'w') as ofile:
genomes2png(aC, pC, ofile)
if args.tree:
tree = pC.get_hier_tree()
with open(args.seedname + "_tree.json", 'w') as ofile:
json.dump(tree2json(aC, pC), ofile)
@staticmethod
def extract(s): # This is where the core of the calculation happens
# s is a single Atoms object passed to this method
chemsyms = s.get_chemical_symbols()
h_inds = [i for i, sym in enumerate(chemsyms) if sym == 'H']
h_pos = s.get_positions()[h_inds]
com = np.average(h_pos, axis=0)
return com
print "---- Hydrogen COM for all NH3 configurations\n"
print '\n'.join(['{0}'.format(x) for x in HydrogenCOM.get(nh3coll)]), "\n\n"
"""
3 - CALCULATORS
The Atomic Simulation Environment provides bindings to many codes in the form of calculators.
These include ab initio codes like CASTEP and VASP as well as empirical force fields. These calculators can be set
and used in Soprano as well. Here we're going to use the most basic one, the Lennard-Jones force field,
as an example.
"""
from ase.calculators.lj import LennardJones
from soprano.properties.basic import CalcEnergy
nh3coll.set_calculators(
LennardJones) # Creates calculators of the given type for all structures
print "---- NH3 Lennard-Jones energy for all configurations ----\n"
print '\n'.join(['{0}'.format(x) for x in CalcEnergy.get(nh3coll)]), "\n\n"