def test_ternary_hull_plot(self):
""" Test plotting ternary hull. """
expected_files = ["KSnP_hull.png", "KSnP_voltage.png"]
for expected_file in expected_files:
if os.path.isfile(expected_file):
os.remove(expected_file)
res_list = glob(REAL_PATH + "data/hull-KPSn-KP/*.res")
self.assertEqual(
len(res_list),
87,
"Could not find test res files, please check installation...",
)
cursor = [res2dict(res)[0] for res in res_list]
QueryConvexHull(
cursor=cursor,
elements=["K", "Sn", "P"],
no_plot=False,
png=True,
quiet=False,
voltage=True,
labels=True,
label_cutoff=0.05,
hull_cutoff=0.1,
capmap=True,
)
self.assertTrue(os.path.isfile(expected_file))
for expected_file in expected_files:
os.remove(expected_file)
def test_pxrd_plot(self):
structure = res2dict(REAL_PATH + "data/res_files/KPSn-OQMD_123456.res",
as_model=True)[0]
plot_pxrd(structure, png=True)
self.assertTrue(os.path.isfile("K7PSn_pxrd.png"))
plot_pdf([structure, structure], filename="test_pxrd", png=True)
self.assertTrue(os.path.isfile("test_pxrd.png"))
def test_voltage_labels(self):
expected_files = ["KP_voltage.png"]
cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0]
hull = QueryConvexHull(cursor=cursor,
species="KP",
no_plot=True,
voltage=True,
labels=True)
plot_voltage_curve(hull.voltage_data,
labels=True,
savefig=expected_files[0])
for expected_file in expected_files:
self.assertTrue(os.path.isfile(expected_file))
def test_binary_hull_plot(self):
""" Test plotting binary hull. """
expected_files = ["KP_hull_simple.svg"]
cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0]
QueryConvexHull(
cursor=cursor,
elements=["K", "P"],
svg=True,
hull_cutoff=0.0,
plot_kwargs={
"plot_fname": "KP_hull_simple",
"svg": True
},
)
for expected_file in expected_files:
self.assertTrue(os.path.isfile(expected_file))
def test_binary_battery_plots(self):
""" Test plotting binary hull. """
expected_files = ["KP_hull.png", "KP_voltage.png", "KP_volume.png"]
cursor = res2dict(REAL_PATH + "data/hull-KP-KSnP_pub/*.res")[0]
QueryConvexHull(
cursor=cursor,
elements=["K", "P"],
no_plot=False,
png=True,
quiet=False,
voltage=True,
labels=True,
label_cutoff=0.05,
hull_cutoff=0.1,
volume=True,
plot_kwargs={"colour_by_source": True},
)
for expected_file in expected_files:
self.assertTrue(os.path.isfile(expected_file))
#!/usr/bin/env python
""" This example runs directly from a matador DBQuery. """
from matador.hull import QueryConvexHull
from matador.scrapers import res2dict
from ilustrado.ilustrado import ArtificialSelector
# prepare best structures from hull as gene pool
cursor, failures = res2dict("hull/*.res")
hull = QueryConvexHull(
cursor=cursor, intersection=True,
no_plot=True, kpoint_tolerance=0.03,
summary=True, hull_cutoff=7.5e-2
)
def filter_fn(doc):
""" Filter out any non-binary phases. """
return len(doc['stoichiometry']) == 2
print('Filtering down to only ternary phases... {}'.format(len(hull.hull_cursor)))
cursor = [doc for doc in hull.hull_cursor if len(doc['stoichiometry']) == 2]
print('Final cursor length... {}'.format(len(cursor)))
ArtificialSelector(
gene_pool=cursor,
compute_mode="manual",
hull=hull,
fitness_metric='hull',
check_dupes=True,
check_dupes_hull=False,
def emt_relax():
""" Perform a test run of Ilustrado with dummy DFT,
on all cores of current machine.
"""
from matador.hull import QueryConvexHull
from ilustrado.ilustrado import ArtificialSelector
from ase.calculators.emt import EMT
if os.uname()[1] == "cluster2":
cpus = cpu_count() - 2
else:
cpus = cpu_count()
structures, _ = res2dict(REAL_PATH + "AuCu_structures/*.res")
# prepare best structures from hull as gene pool
hull = QueryConvexHull(
cursor=structures,
elements=["Au", "Cu"],
intersection=True,
subcmd="hull",
no_plot=True,
hull_cutoff=0,
)
from multiprocessing import Queue
cursor = hull.cursor
for doc in cursor:
queue = Queue()
relaxer = AseRelaxation(doc, queue)
relaxer.relax()
doc.update(queue.get())
print("Running on {} cores on {}.".format(cpus, os.uname()[1]))
def filter_fn(doc):
return len(doc["stoichiometry"]) == 2
ArtificialSelector(
gene_pool=cursor,
hull=hull,
debug=False,
fitness_metric="hull",
nprocs=cpus,
structure_filter=filter_fn,
check_dupes=0,
check_dupes_hull=False,
ncores=1,
testing=True,
ase_calculator=EMT(),
mutations=[
"nudge_positions", "permute_atoms", "random_strain", "vacancy"
],
max_num_mutations=1,
max_num_atoms=50,
mutation_rate=0.5,
crossover_rate=0.5,
num_generations=3,
population=20,
num_survivors=10,
elitism=0.5,
loglevel="debug",
)