def test_tree(self):
aselist = [Atoms('C'), Atoms('H'), Atoms('N'), Atoms('O')]
testcoll = AtomsCollection(aselist)
testcoll.save_tree('test_save', 'xyz', safety_check=2)
loadcoll = AtomsCollection.load_tree('test_save', 'xyz')
self.assertTrue(''.join(loadcoll.all.get_chemical_formula()) == 'CHNO')
# Try a custom save format
def custom_save(a, path, format_string):
with open(os.path.join(path, 'struct.custom'), 'w') as f:
f.write(format_string.format(''.join(
a.get_chemical_formula())))
def custom_load(path, marker):
with open(os.path.join(path, 'struct.custom')) as f:
l = f.read().strip()
return Atoms(l.split(marker)[1].strip())
testcoll.save_tree('test_save',
custom_save,
opt_args={'format_string': 'Formula:{0}'},
safety_check=2)
loadcoll = AtomsCollection.load_tree('test_save',
custom_load,
opt_args={'marker': ':'})
self.assertTrue(''.join(loadcoll.all.get_chemical_formula()) == 'CHNO')
def load_muairss_collection(struct, params, batch_path=''):
# Output folder
out_path = os.path.join(batch_path, params['out_folder'])
load_formats = {
'dftb+': dftb_read_input,
'castep': castep_read_input,
'uep': uep_read_input
}
calcs = [s.strip().lower() for s in params['calculator'].split(',')]
if 'all' in calcs:
calcs = load_formats.keys()
loaded = {}
for cname in calcs:
opt_args = {}
if cname == 'uep':
opt_args['atoms'] = struct
calc_path = os.path.join(out_path, cname)
dc = AtomsCollection.load_tree(calc_path, load_formats[cname],
opt_args=opt_args, safety_check=2)
loaded[cname] = dc
return loaded
def load_muairss_collection(struct, params, batch_path=""):
# Output folder
out_path = os.path.join(batch_path, params["out_folder"])
io_formats = {
"castep": ReadWriteCastep,
"dftb+": ReadWriteDFTB,
"uep": ReadWriteUEP,
}
calcs = [s.strip().lower() for s in params["calculator"].split(",")]
if "all" in calcs:
calcs = io_formats.keys()
loaded = {}
for cname in calcs:
rw = io_formats[cname](params)
calc_path = os.path.join(out_path, cname)
dc = AtomsCollection.load_tree(
calc_path, rw.read, safety_check=2, tolerant=True
)
alln = len(glob.glob(calc_path + "/*"))
total_structures = len(dc.structures)
if total_structures == 0:
return
elif total_structures / alln < 0.9:
print(
"""If more than 10% of structures could not be loaded,
we advise adjusting the parameters and re-running the {0}
optimisation for the structures that failed.""".format(
cname
)
)
loaded[cname] = dc
return loaded
def load_muairss_collection(struct, params, batch_path=''):
# Output folder
out_path = os.path.join(batch_path, params['out_folder'])
io_formats = {
'castep': ReadWriteCastep,
'dftb+': ReadWriteDFTB,
'uep': ReadWriteUEP
}
calcs = [s.strip().lower() for s in params['calculator'].split(',')]
if 'all' in calcs:
calcs = io_formats.keys()
loaded = {}
for cname in calcs:
rw = io_formats[cname](params)
calc_path = os.path.join(out_path, cname)
dc = AtomsCollection.load_tree(calc_path,
rw.read,
safety_check=2,
tolerant=True)
print("If more than 10% of structures could not be loaded, \
we advise adjusting the parameters and re-running the {0} \
optimisation for the structures that failed.".format(cname))
total_structures = len(dc.structures)
if total_structures == 0:
return
loaded[cname] = dc
return loaded
def muon_vibrational_average_read(structure,
calculator="castep",
avgprop="hyperfine",
average_T=0,
average_file="averages.dat",
**kwargs):
# Open the structure file
sname = seedname(structure)
io_formats = {"castep": ReadWriteCastep, "dftb+": ReadWriteDFTB}
try:
displaced_coll = AtomsCollection.load_tree(
sname + "_displaced",
io_formats[calculator]().read,
opt_args={"read_magres": True},
safety_check=2,
)
except Exception:
raise
return
mu_i = displaced_coll.info["muon_index"]
displsch = displaced_coll.info["displacement_scheme"]
to_avg = []
for a in displaced_coll:
if avgprop == "hyperfine":
to_avg.append(a.get_array("hyperfine")[mu_i])
elif avgprop == "charge":
# Used mostly as test
try:
to_avg.append(a.get_charges()[mu_i])
except RuntimeError:
raise (IOError("Could not read charges."))
to_avg = np.array(to_avg)
displsch.recalc_weights(T=average_T)
# New shape
N = len(displaced_coll)
shape = tuple([slice(N)] + [None] * (len(to_avg.shape) - 1))
weights = displsch.weights[shape]
avg = np.sum(weights * to_avg, axis=0)
# Print output report
with open(average_file, "w") as f:
avgname = {
"hyperfine": "hyperfine tensor",
"charge": "charge"
}[avgprop]
f.write("""
Quantum average of {property} calculated on {cell}.
Scheme details:
{scheme}
Averaged value:
{avg}
All values, by configuration:
{vals}
""".format(
property=avgname,
cell=structure,
scheme=displsch,
avg=avg,
vals="\n".join([
"Conf: {0} (Weight = {1})\n{2}\n".format(
i, displsch.weights[i], v) for i, v in enumerate(to_avg)
]),
))
def muon_vibrational_average_read(cell_file,
calculator='castep',
avgprop='hyperfine',
average_T=0,
average_file='averages.dat',
**kwargs):
# Open the structure file
cell = io.read(cell_file)
path = os.path.split(cell_file)[0]
sname = seedname(cell_file)
num_atoms = len(cell)
reader_function = {
'castep': read_output_castep,
'dftb+': read_output_dftbp
}[calculator]
displaced_coll = AtomsCollection.load_tree(sname + '_displaced',
reader_function,
safety_check=2,
opt_args={'avgprop': avgprop})
mu_i = displaced_coll.info['muon_index']
displsch = displaced_coll.info['displacement_scheme']
to_avg = []
for a in displaced_coll:
if avgprop == 'hyperfine':
to_avg.append(a.get_array('hyperfine')[mu_i])
elif avgprop == 'charge':
# Used mostly as test
to_avg.append(a.get_charges()[mu_i])
to_avg = np.array(to_avg)
displsch.recalc_weights(T=average_T)
# New shape
N = len(displaced_coll)
shape = tuple([slice(N)] + [None] * (len(to_avg.shape) - 1))
avg = np.sum(displsch.weights[shape] * to_avg, axis=0)
# Print output report
with open(average_file, 'w') as f:
avgname = {
'hyperfine': 'hyperfine tensor',
'charge': 'charge'
}[avgprop]
f.write("""
Quantum average of {property} calculated on {cell}.
Scheme details:
{scheme}
Averaged value:
{avg}
All values, by configuration:
{vals}
""".format(property=avgname,
cell=cell_file,
scheme=displsch,
avg=avg,
vals='\n'.join([
'{0}\n{1}\n'.format(i, v) for i, v in enumerate(to_avg)
])))
