def sweep_setup(config_path):
config = load_config_file(config_path)
db.init_database(config["database_connection_string"])
session = db.get_session()
directional_atom_sweep_points = config['directional_atom_sweep_points']
sigma_sweep_points = config['sigma_sweep_points']
epsilon_sweep_points = config['epsilon_sweep_points']
scfg = config['structure_parameters']
eps_d = np.linspace(*scfg['epsilon_limits'], epsilon_sweep_points)
sig_d = np.linspace(*scfg['sigma_limits'], sigma_sweep_points)
atoms_d = np.linspace(*scfg['directional_atom_limits'], directional_atom_sweep_points, dtype=int)
atom_diameter = scfg['atom_diameter']
print("epsilons: ", eps_d)
print("sigmas: ", sig_d)
print("num_atoms: ", atoms_d)
for eps in eps_d:
for sig in sig_d:
for num_atoms in atoms_d:
material = Material.cube_pore_new(sig, eps, num_atoms, atom_diameter)
session.add(material)
session.commit()
def run_materials(config_path, workers=(1,1)):
config = load_config_file(config_path)
db.init_database(config["database_connection_string"])
session = db.get_session()
mats = session.query(Material).all()
num_workers, worker_num = workers
mats = mats[worker_num - 1::num_workers]
print(len(mats))
for m in mats:
print("---------------")
print("%d" % m.id)
run_all_simulations(m, config)
session.add(m)
session.commit()
def output_config_files(config_path, material_ids, database_path=None):
config = load_config_file(config_path)
db.init_database(db.get_sqlite_dbcs(database_path))
session = db.get_session()
from htsohm.db import Material
for m_id in material_ids:
m = session.query(Material).get(m_id)
for i in config["simulations"]:
simcfg = config["simulations"][i]
output_dir = "output_%d_%s_%s_%d" % (m.id, m.uuid[0:8], simcfg["type"], i)
os.makedirs(output_dir, exist_ok=True)
sim = getattr(simulate, simcfg["type"])
sim.write_output_files(m, simcfg, output_dir)
def sweep_setup(config_path):
config = load_config_file(config_path)
db.init_database(config["database_connection_string"])
session = db.get_session()
if 'sweep_points' in config:
lattice_sweep_points = sigma_sweep_points = epsilon_sweep_points = config['sweep_points']
else:
lattice_sweep_points = config['lattice_sweep_points']
sigma_sweep_points = config['sigma_sweep_points']
epsilon_sweep_points = config['epsilon_sweep_points']
scfg = config['structure_parameters']
eps_d = np.linspace(*scfg['epsilon_limits'], epsilon_sweep_points)
sig_d = np.linspace(*scfg['sigma_limits'], sigma_sweep_points)
a_d = np.linspace(*scfg['lattice_constant_limits'], lattice_sweep_points)
# always do symmetrical with one-atom only
lattice_coords = [(a, a, a) for a in a_d]
# b_d = np.linspace(*scfg['lattice_constant_limits'], config['sweep_points'])
# c_d = np.linspace(*scfg['lattice_constant_limits'], config['sweep_points'])
# lattice_coords = np.array(np.meshgrid(a_d, b_d, c_d)).T.reshape(-1,3)
# # remove symmetrical points
# lattice_coords = map(sorted, lattice_coords)
# lattice_coords = set(map(tuple, lattice_coords))
print("epsilons: ", eps_d)
print("sigmas: ", sig_d)
print("lattice constants: ", a_d)
for eps in eps_d:
for sig in sig_d:
for coords in lattice_coords:
material = Material.one_atom_new(sig, eps, *coords)
session.add(material)
session.commit()
def bin_graph(config_path, database_path=None, csv_path=None, last_material=None,
sigma_limits=None, epsilon_limits=None, addl_data_path=None, last_children=0):
config = load_config_file(config_path)
VoidFraction.set_column_for_void_fraction(config['void_fraction_subtype'])
prop1range = config['prop1range']
prop2range = config['prop2range']
num_bins = config['number_of_convergence_bins']
# vf_binunits = (prop1range[1] - prop1range[0]) / num_bins
# ml_binunits = (prop2range[1] - prop2range[0]) / num_bins
print("loading materials...")
if csv_path:
mats_r = np.loadtxt(csv_path, delimiter=',', skiprows=1, usecols=(12,13,5,6), max_rows=last_material)
print("%d rows loaded from csv" % mats_r.shape[0])
if sigma_limits:
mats_r = mats_r[(sigma_limits[0] <= mats_r[:,2]) & (mats_r[:,2] <= sigma_limits[1])]
print("%d rows after applying sigma limits" % mats_r.shape[0])
if epsilon_limits:
mats_r = mats_r[(epsilon_limits[0] <= mats_r[:,3]) & (mats_r[:,3] <= epsilon_limits[1])]
print("%d rows after applying epsilon limits" % mats_r.shape[0])
else:
db.init_database(db.get_sqlite_dbcs(database_path))
session = db.get_session()
mats_d = session.query(Material).options(joinedload("void_fraction"), joinedload("gas_loading"))
if last_material:
mats_d = mats_d.limit(last_material).all()
else:
mats_d = mats_d.all()
print("calculating material properties...")
mats_r = [(m.void_fraction[0].get_void_fraction(), m.gas_loading[0].absolute_volumetric_loading) for m in mats_d]
last_generation_start = len(mats_r) - last_children
print("calculating bins...")
bin_counts = np.zeros((num_bins, num_bins))
start_bins = calc_bins(mats_r[0:last_generation_start], num_bins, prop1range=prop1range, prop2range=prop2range)
for i, (bx, by) in enumerate(start_bins):
bin_counts[bx,by] += 1
bins_explored = np.count_nonzero(bin_counts)
new_bins = calc_bins(mats_r[last_generation_start:], num_bins, prop1range=prop1range, prop2range=prop2range)
print(len(new_bins), len(start_bins), len(set(new_bins) - set(start_bins)))
new_bins = set(new_bins) - set(start_bins)
print("bins explored = %d" % bins_explored)
children = []
parents = []
if last_children > 0:
children = np.array(mats_r[last_generation_start:])
parent_ids = np.array([m.parent_id for m in mats_d[last_generation_start:]])
parents = np.array([mats_r[pid - 1] for pid in parent_ids])
addl_data = None
if addl_data_path:
print("adding additional data from: %s" % addl_data_path)
addl_data = np.loadtxt(addl_data_path, delimiter=",", skiprows=1, usecols=(1,2))
print("outputting graph...")
output_path = "binplot_%d_materials.png" % len(mats_r)
delaunay_figure(mats_r, num_bins, output_path, bins=bin_counts, new_bins=new_bins,
title="%d Materials: %d/%d %5.2f%%" % (len(mats_r), bins_explored,
num_bins ** 2, 100*float(bins_explored / num_bins ** 2)),
prop1range=prop1range, prop2range=prop2range, show_triangulation=False, show_hull=False,
addl_data_set=addl_data, children=children, parents=parents)
def htsohm_run(config_path, restart_generation=-1, override_db_errors=False, num_processes=1, max_generations=None):
def _update_bins_counts_materials(all_bins, bins, start_index):
nonlocal bin_counts, bin_materials
for i, (bx, by) in enumerate(all_bins):
bin_counts[bx,by] += 1
bin_materials[bx][by].append(i + start_index)
new_bins = set(all_bins) - bins
return new_bins, bins.union(new_bins)
config = load_config_file(config_path)
os.makedirs(config['output_dir'], exist_ok=True)
print(config)
children_per_generation = config['children_per_generation']
prop1range = config['prop1range']
prop2range = config['prop2range']
num_bins = config['number_of_convergence_bins']
benchmarks = config['benchmarks']
next_benchmark = benchmarks.pop(0)
last_benchmark_reached = False
load_restart_path = config['load_restart_path']
if max_generations is None:
max_generations = config['max_generations']
engine, session = db.init_database(config["database_connection_string"],
backup=(load_restart_path != False or restart_generation > 0),
void_fraction_subtype=config['void_fraction_subtype'])
print('{:%Y-%m-%d %H:%M:%S}'.format(datetime.now()))
if restart_generation >= 0:
print("Restarting from database using generation: %s" % restart_generation)
box_d, box_r, bin_counts, bin_materials, bins, start_gen = load_restart_db(
restart_generation, num_bins, prop1range, prop2range, session)
print("Restarting at generation %d\nThere are currently %d materials" % (start_gen, len(box_r)))
check_db_materials_for_restart(len(box_r), session, delete_excess=override_db_errors)
elif load_restart_path:
print("Restarting from file: %s" % load_restart_path)
box_d, box_r, bin_counts, bin_materials, bins, start_gen = load_restart(load_restart_path)
print("Restarting at generation %d\nThere are currently %d materials" % (start_gen, len(box_r)))
check_db_materials_for_restart(len(box_r), session, delete_excess=override_db_errors)
else:
if session.query(Material).count() > 0:
print("ERROR: cannot have existing materials in the database for a new run")
sys.exit(1)
# generate initial generation of random materials
print("applying random seed to initial points: %d" % config['initial_points_random_seed'])
random.seed(config['initial_points_random_seed'])
box_d, box_r = parallel_simulate_generation(generator.random.new_material, num_processes, None,
config, gen=0, children_per_generation=config['children_per_generation'])
random.seed() # flush the seed so that only the initial points are set, not generated points
# setup initial bins
bin_counts = np.zeros((num_bins, num_bins))
bin_materials = empty_lists_2d(num_bins, num_bins)
all_bins = calc_bins(box_r, num_bins, prop1range=prop1range, prop2range=prop2range)
new_bins, bins = _update_bins_counts_materials(all_bins, set(), 0)
output_path = os.path.join(config['output_dir'], "binplot_0.png")
# delaunay_figure(box_r, num_bins, output_path, bins=bin_counts, \
# title="Starting random materials", show_triangulation=False, show_hull=False, \
# prop1range=prop1range, prop2range=prop2range)
start_gen = 1
if config['generator_type'] == 'random':
generator_method = generator.random.new_material
elif config['generator_type'] == 'mutate':
generator_method = generator.mutate.mutate_material
for gen in range(start_gen, max_generations + 1):
benchmark_just_reached = False
# mutate materials and simulate properties
parents_d, parents_r = select_parents(children_per_generation, box_d, box_r, bin_materials, config)
new_box_d, new_box_r = parallel_simulate_generation(generator_method, num_processes, parents_d,
config, gen=gen, children_per_generation=config['children_per_generation'])
# track bins
all_bins = calc_bins(new_box_r, num_bins, prop1range=prop1range, prop2range=prop2range)
new_bins, bins = _update_bins_counts_materials(all_bins, bins, gen * children_per_generation)
# output space + calced bins
for i, box_r in enumerate(new_box_r):
print("(%s) => (%s)" % (box_r, all_bins[i]))
# evaluate algorithm effectiveness
bin_fraction_explored = len(bins) / num_bins ** 2
print_block('GENERATION %s: %5.2f%%' % (gen, bin_fraction_explored * 100))
while bin_fraction_explored >= next_benchmark:
benchmark_just_reached = True
print_block("%s: %5.2f%% exploration accomplished at generation %d" %
('{:%Y-%m-%d %H:%M:%S}'.format(datetime.now()), bin_fraction_explored * 100, gen))
if benchmarks:
next_benchmark = benchmarks.pop(0)
else:
last_benchmark_reached = True
# if config['bin_graph_on'] and (
# (benchmark_just_reached or gen == config['max_generations']) or \
# (config['bin_graph_every'] > 0 and gen % config['bin_graph_every'] == 0)):
#
# output_path = os.path.join(config['output_dir'], "binplot_%d.png" % gen)
# delaunay_figure(box_r, num_bins, output_path, children=new_box_r, parents=parents_r,
# bins=bin_counts, new_bins=new_bins,
# title="Generation %d: %d/%d (+%d) %5.2f%% (+%5.2f %%)" %
# (gen, len(bins), num_bins ** 2, len(new_bins),
# 100*float(len(bins)) / num_bins ** 2, 100*float(len(new_bins)) / num_bins ** 2 ),
# patches=None, prop1range=prop1range, prop2range=prop2range, \
# perturbation_methods=["all"]*children_per_generation, show_triangulation=False, show_hull=False)
#
# if config['tri_graph_on'] and (
# (benchmark_just_reached or gen == config['max_generations']) or \
# (config['tri_graph_every'] > 0 and gen % config['tri_graph_every'] == 0)):
#
# output_path = os.path.join(config['output_dir'], "triplot_%d.png" % gen)
# delaunay_figure(box_r, num_bins, output_path, children=new_box_r, parents=parents_r,
# bins=bin_counts, new_bins=new_bins,
# title="Generation %d: %d/%d (+%d) %5.2f%% (+%5.2f %%)" %
# (gen, len(bins), num_bins ** 2, len(new_bins),
# 100*float(len(bins)) / num_bins ** 2, 100*float(len(new_bins)) / num_bins ** 2 ),
# patches=None, prop1range=prop1range, prop2range=prop2range, \
# perturbation_methods=["all"]*children_per_generation)
box_d = np.append(box_d, new_box_d, axis=0)
box_r = np.append(box_r, new_box_r, axis=0)
restart_path = os.path.join(config['output_dir'], "restart.txt.npz")
dump_restart(restart_path, box_d, box_r, bin_counts, bin_materials, bins, gen + 1)
if benchmark_just_reached or gen == max_generations:
shutil.move(restart_path, os.path.join(config['output_dir'], "restart%d.txt.npz" % gen))
if last_benchmark_reached:
break
with open("pm.csv", 'w', newline='') as f:
output_csv_from_db(session, output_file=f)
with open("pm-binned.csv", 'w', newline='') as f:
# column 12 is void_fraction_geo, 13 is methane loading
csv_add_bin_column("pm.csv", [(12, *prop1range, num_bins), (13, *prop2range, num_bins)], output_file=f)
def bin_graph(config_path, csv_path=None, database_path=None):
config = load_config_file(config_path)
num_bins = config['number_of_convergence_bins']
prop1range = config['structure_parameters']['lattice_constant_limits']
prop2range = config['prop2range']
if 'sweep_points' in config:
lattice_sweep_points = sigma_sweep_points = epsilon_sweep_points = config[
'sweep_points']
else:
lattice_sweep_points = config['lattice_sweep_points']
sigma_sweep_points = config['sigma_sweep_points']
epsilon_sweep_points = config['epsilon_sweep_points']
xticks = lattice_sweep_points
if xticks > 11:
xticks = 11
print("loading materials...")
mats_by_lj = {}
if csv_path:
csvrows = np.loadtxt(csv_path, delimiter=',', skiprows=1)
for m in csvrows:
lj = (m[4], m[5]) # sigma, epsilon
if lj not in mats_by_lj:
mats_by_lj[lj] = []
mats_by_lj[lj].append([m[1],
m[7]]) # lattice a, abs volumetric loading
else:
db.init_database(db.get_sqlite_dbcs(database_path))
session = db.get_session()
mats = session.query(Material) \
.options(joinedload("structure").joinedload("atom_types")) \
.options(joinedload("gas_loading")).all()
print("calculating material properties...")
for m in mats:
lj = (m.structure.atom_types[0].sigma,
m.structure.atom_types[0].epsilon)
if lj not in mats_by_lj:
mats_by_lj[lj] = []
mats_by_lj[lj].append(
[m.structure.a, m.gas_loading[0].absolute_volumetric_loading])
print("plotting...")
fig = plt.figure(figsize=(12, 12), tight_layout=True)
ax = fig.add_subplot(1, 1, 1)
ax.set_xlim(prop1range[0], prop1range[1])
ax.set_ylim(prop2range[0], prop2range[1])
ax.set_xlabel("Lattice constant [Å]")
ax.set_ylabel("Methane Loading (V [STP]/V)")
ax.set_yticks(prop2range[1] * np.array([0.0, 0.25, 0.5, 0.75, 1.0]))
ax.set_yticks(prop2range[1] * np.array(range(0, num_bins + 1)) / num_bins,
minor=True)
ax.set_xticks(prop1range[0] + (prop1range[1] - prop1range[0]) *
np.array(range(0, xticks)) / (xticks - 1))
ax.set_xticks(prop1range[0] + (prop1range[1] - prop1range[0]) *
np.array(range(0, num_bins + 1)) / num_bins,
minor=True)
# if show_grid:
ax.grid(linestyle='-', color='0.8', zorder=0)
absolute_limits_a = np.linspace(prop1range[0], prop1range[1], 100)
ml_atoms_a3_stp = 2.69e-5
absolute_limits_ml = [(1 / a**3) / ml_atoms_a3_stp
for a in absolute_limits_a]
ax.plot(absolute_limits_a,
absolute_limits_ml,
lw=3,
linestyle="--",
color="black",
zorder=15,
label="all sites filled")
tab10 = plt.get_cmap("tab10").colors
for (sig, eps), a_ml in mats_by_lj.items():
a_ml = np.array(a_ml)
sig_index = limit_index(sig,
config['structure_parameters']['sigma_limits'],
sigma_sweep_points)
eps_index = limit_index(
eps, config['structure_parameters']['epsilon_limits'],
epsilon_sweep_points)
# print(sig, eps, eps_index)
color = tab10[sig_index]
alpha = (eps_index + 1) / epsilon_sweep_points
if eps_index + 1 == epsilon_sweep_points:
label = "sigma = %4.3f" % sig
else:
label = None
ax.plot(a_ml[:, 0],
a_ml[:, 1],
lw=3,
color=color,
zorder=20,
alpha=alpha,
label=label)
ax.legend()
ax.set_title(
"Methane loading vs lattice constant. Lines colored by sigma. \nLine transparency shows epsilon (no transparency is highest epsilon value; highest transparency is lowest epsilon value)"
)
fig.savefig("sig_eps_a_ml.png")
plt.close(fig)
def dof_analysis(config_path, output_directory):
config = load_config_file(config_path)
db.init_database(config["database_connection_string"])
session = db.get_session()
children_per_generation = config['children_per_generation']
prop1range = config['prop1range']
prop2range = config['prop2range']
num_bins = config['number_of_convergence_bins']
bin_counts = np.zeros((num_bins, num_bins))
vf_binunits = (prop1range[1] - prop1range[0]) / num_bins
ml_binunits = (prop2range[1] - prop2range[0]) / num_bins
materials = session.query(Material)
perturbation_types = [
"lattice", "lattice_nodens", "atom_types", "atom_sites", "density",
"all"
]
tsv_output_path = os.path.join(output_directory, "data.tsv")
tsvfile = open(tsv_output_path, 'w')
tsv = csv.writer(tsvfile, delimiter="\t", lineterminator="\n")
tsv.writerow(
[""] + list(chain.from_iterable([[t] * 5
for t in perturbation_types])))
tsv.writerow(["gen"] + list(
chain.from_iterable([["#", "∆vf", "∆ml", "dist", "new_bins"]
for t in perturbation_types])))
mats_d = materials.all()
mats_r = [(m.void_fraction[0].void_fraction,
m.gas_loading[0].absolute_volumetric_loading) for m in mats_d]
new_mats_d = mats_d[0:children_per_generation]
new_mats_r = mats_r[0:children_per_generation]
new_bins = calc_bins(new_mats_r,
num_bins,
prop1range=prop1range,
prop2range=prop2range)
for i, (bx, by) in enumerate(new_bins):
bin_counts[bx, by] += 1
pts = {t: [] for t in perturbation_types}
gen = 1
new_mats_d = mats_d[gen * children_per_generation:(gen + 1) *
children_per_generation]
new_mats_r = mats_r[gen * children_per_generation:(gen + 1) *
children_per_generation]
animation = [[[b[0], b[1], -1, -1] for b in new_bins]]
while len(new_mats_d) > 0:
new_bins = calc_bins(new_mats_r,
num_bins,
prop1range=prop1range,
prop2range=prop2range)
gen_animation = []
gen_stats = {t: [0, 0.0, 0.0, 0.0, 0] for t in perturbation_types}
for i, m in enumerate(new_mats_d):
m_stats = gen_stats[m.perturbation]
m_stats[0] += 1
dvf = (m.void_fraction[0].void_fraction -
m.parent.void_fraction[0].void_fraction) / vf_binunits
dml = (m.gas_loading[0].absolute_volumetric_loading -
m.parent.gas_loading[0].absolute_volumetric_loading
) / ml_binunits
m_stats[1] += dvf
m_stats[2] += dml
m_stats[3] += (dvf**2 + dml**2)**0.5
if bin_counts[new_bins[i][0], new_bins[i][1]] == 0:
m_stats[4] += 1
# generate information for animation script
parent_r = (m.parent.void_fraction[0].void_fraction,
m.parent.gas_loading[0].absolute_volumetric_loading)
parent_bin = calc_bins([parent_r],
num_bins,
prop1range=prop1range,
prop2range=prop2range)[0]
gen_animation.append(
[new_bins[i][0], new_bins[i][1], parent_bin[0], parent_bin[1]])
# this and dml needed for output of numpy arrays # num_materials, ∆vf, ∆ml, ∆all, new_bins
pts[m.perturbation].append([
m.parent.gas_loading[0].absolute_volumetric_loading /
ml_binunits, dml
])
for i, (bx, by) in enumerate(new_bins):
bin_counts[bx, by] += 1
row = [gen] + list(
chain.from_iterable([gen_stats[t] for t in perturbation_types]))
tsv.writerow(row)
gen += 1
new_mats_d = mats_d[gen * children_per_generation:(gen + 1) *
children_per_generation]
new_mats_r = mats_r[gen * children_per_generation:(gen + 1) *
children_per_generation]
animation.append(gen_animation)
np.save(os.path.join(output_directory, "animation"), animation)
for k in pts:
np.save(os.path.join(output_directory, k), pts[k])
