def max_energy_ext_per_directory(energy_cutoffs):
"""
Get the energy extension string for each run directory,
for a given set of calculations
:param energy_cutoffs: of the form
{'zr': {0: np.linspace(60, 120, num=4),
1: np.linspace(60, 120, num=4),
2: np.linspace(90, 300, num=4),
3: np.linspace(60, 120, num=4)},
'o': {0: np.linspace(60, 120, num=4),
1: np.linspace(60, 120, num=4),
2: np.linspace(60, 120, num=4)}
}
:return: max_energy_exts
"""
max_energy_exts = []
for energy in restructure_energy_cutoffs(energy_cutoffs):
max_energy_per_species = [
max(energy_per_l_channel.values())
for energy_per_l_channel in energy.values()
]
max_energy_exts.append(str(int(max(max_energy_per_species))))
return max_energy_exts
def set_lo_channel_cutoffs(l_max: dict) -> list:
"""
Define the LO energy cut-offs per LO channel
Ranges from (6, 5) to (7, 6) for (Zr, O)
:return: list energy_cutoffs: Energy cut-offs for each LO channel of Zr and O
"""
# (6, 5)
if l_max['zr'] == 6 and l_max['o'] == 5:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200],
1: [80, 100, 120, 150, 180, 200],
2: [80, 100, 120, 150, 180, 200],
3: [80, 100, 120, 150, 180, 200],
4: [80, 100, 120, 150, 180, 200],
5: [80, 100, 120, 150, 180, 200],
6: [80, 100, 120, 150, 180, 200]
},
'o': {
0: [80, 100, 120, 150, 180, 200],
1: [80, 100, 120, 150, 180, 200],
2: [80, 100, 120, 150, 180, 200],
3: [80, 100, 120, 150, 180, 200],
4: [80, 100, 120, 150, 180, 200],
5: [80, 100, 120, 150, 180, 200]
}
}
# (7, 6)
elif l_max['zr'] == 7 and l_max['o'] == 6:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200],
1: [80, 100, 120, 150, 180, 200],
2: [80, 100, 120, 150, 180, 200],
3: [80, 100, 120, 150, 180, 200],
4: [80, 100, 120, 150, 180, 200],
5: [80, 100, 120, 150, 180, 200],
6: [80, 100, 120, 150, 180, 200],
7: [80, 100, 120, 150, 180, 200]
},
'o': {
0: [80, 100, 120, 150, 180, 200],
1: [80, 100, 120, 150, 180, 200],
2: [80, 100, 120, 150, 180, 200],
3: [80, 100, 120, 150, 180, 200],
4: [80, 100, 120, 150, 180, 200],
5: [80, 100, 120, 150, 180, 200],
6: [80, 100, 120, 150, 180, 200]
}
}
else:
sys.exit("L max pair for Zr and O not valid")
return restructure_energy_cutoffs(n_energies_per_channel, energy_cutoffs)
def gw_input(root_path: str,
ground_state_dir: str,
energy_cutoffs: List[int],
species=['zr', 'o'],
l_max={
'zr': 6,
'o': 5
}):
"""
:param str root_path: Top level path to calculations
:param ground_state_dir: Path to groundstate directory
:param List[int] energy_cutoffs: LO energy cut-offs
:param species:
:param l_max:
"""
# Run script settings
if cluster == 'Dune3':
env_vars = OrderedDict([
('EXE', '/users/sol/abuccheri/exciting/bin/excitingmpismp'),
('OUT', 'terminal.out')
])
module_envs = ['intel/2019']
slurm_directives = slurm.set_slurm_directives(time=[0, 72, 0, 0],
partition='all',
exclusive=True,
nodes=4,
ntasks_per_node=2,
cpus_per_task=18,
hint='nomultithread')
elif cluster == 'HAWK':
omp = 64
pbs_directives = set_pbs_pro_directives(time=[24, 00, 0],
queue_name='normal',
send_email='abe',
nodes=2,
mpi_ranks_per_node=1,
omp_threads_per_process=omp,
cores_per_node=128,
node_type='rome',
job_name='GW_gs')
env_vars = OrderedDict([(
'EXE',
'/zhome/academic/HLRS/pri/ipralbuc/exciting-oxygen_release/bin/exciting_mpismp'
), ('OUT', 'terminal.out')])
#module_envs = ['intel/19.1.0', 'mkl/19.1.0', 'impi/19.1.0']
module_envs = ['intel/19.1.0', 'impi/19.1.0']
mpi_options = ['omplace -nt ' + str(omp)]
else:
print('Cluster choice not recognised: ', cluster)
# GW settings
# Need some excessively large number for nempty => exciting takes upper bound
write_input_file_in_root(
root_path, A1_gs_input,
GWInput(taskname="g0w0",
nempty=3000,
ngridq=[2, 2, 2],
skipgnd=False,
n_omega=32,
freqmax=1.0))
# Default basis settings
default_linear_energies = parse_lo_linear_energies(ground_state_dir)
default_los = {
'zr': DefaultLOs(default_linear_energies['zr'], energy_tol=0.8),
'o': DefaultLOs(default_linear_energies['o'], energy_tol=0.8)
}
# Default basis strings with .format tags
default_basis_string = {
'zr': parse_basis_as_string(os.path.join(ground_state_dir, "Zr.xml")),
'o': parse_basis_as_string(os.path.join(ground_state_dir, "O.xml"))
}
# LO energies
lorecommendations = parse_lorecommendations(
root_path + '/../../lorecommendations.dat', species)
n_energies_per_channel = 3
energy_cutoffs = restructure_energy_cutoffs(n_energies_per_channel,
energy_cutoffs)
species_basis_string = "_".join(s.capitalize() + str(l_max[s])
for s in species)
for ie, energy_cutoff in enumerate(energy_cutoffs):
# Copy ground state directory to GW directory
job_dir = root_path + '/max_energy_' + str(ie)
print(
'Creating directory, with input.xml, run.sh and optimised basis:',
job_dir)
copy_tree(ground_state_dir, job_dir)
# Copy input.xml with GW settings
shutil.copy(root_path + "/input.xml", job_dir + "/input.xml")
# New run script
if cluster == 'Dune3':
slurm_directives[
'job-name'] = "gw_A1_lmax_" + species_basis_string + str(ie)
write_file(
job_dir + '/run.sh',
slurm.set_slurm_script(slurm_directives, env_vars,
module_envs))
else:
pbs_directives['N'] = "gw_A1_lmax_" + species_basis_string + str(
ie)
write_file(
job_dir + '/run.sh',
set_pbs_pro(pbs_directives, env_vars, module_envs,
mpi_options))
# Write optimised basis
write_optimised_lo_bases(species, l_max, energy_cutoff,
lorecommendations, default_basis_string,
default_los, job_dir)
# Remove problem LO from basis
cut_lo_function(job_dir + '/Zr.xml')
def set_up_g0w0(root_path: str, energy_cutoffs: dict):
# Material
species = ['zr', 'o']
l_max = {'zr': 3, 'o': 2}
gw_root = write_input_file_with_gw_settings(
root_path, A1_gs_input,
GWInput(taskname="g0w0",
nempty=2000,
ngridq=[2, 2, 2],
skipgnd=False,
n_omega=32))
# Default basis settings
# NOTE in this case ground state is one level up
default_linear_energies = parse_lo_linear_energies(root_path +
"/../groundstate")
default_los = {
'zr': DefaultLOs(default_linear_energies['zr'], energy_tol=1.5),
'o': DefaultLOs(default_linear_energies['o'], energy_tol=1.5)
}
# Default basis strings with .format tags
default_basis_string = {
'zr': parse_basis_as_string(root_path + "/../groundstate/Zr.xml"),
'o': parse_basis_as_string(root_path + "/../groundstate/O.xml")
}
# LO recommendation energies
lorecommendations = parse_lorecommendations(
root_path + '/lorecommendations.dat', species)
# Slurm script settings
env_vars = OrderedDict([
('EXE', '/users/sol/abuccheri/exciting/bin/excitingmpismp'),
('OUT', 'terminal.out')
])
module_envs = ['intel/2019']
slurm_directives = slurm.set_slurm_directives(time=[0, 24, 0, 0],
partition='all',
exclusive=True,
nodes=4,
ntasks_per_node=2,
cpus_per_task=18,
hint='nomultithread')
species_basis_string = "".join(s.capitalize() + str(l_max[s]) + '_'
for s in species)
# TODO Would be better to label with extra LOs added
for ie, energy_cutoff in enumerate(
restructure_energy_cutoffs(len(energy_cutoffs['zr'][0]),
energy_cutoffs)):
# Copy ground state directory to GW directory
# Use an index not max energy, as the max energy does not change in 3/4 runs
job_dir = gw_root + '/max_energy_i' + str(ie)
print(
'Creating directory, with input.xml, run.sh and optimised basis:',
job_dir)
copy_tree(root_path + '/../groundstate', job_dir)
# Copy input.xml with GW settings
shutil.copy(gw_root + "/input.xml", job_dir + "/input.xml")
# New Slurm script
slurm_directives[
'job-name'] = "gw_A1_lmax_" + species_basis_string + str(
ie) + 'loEcutoff'
write_file(
job_dir + '/run.sh',
slurm.set_slurm_script(slurm_directives, env_vars, module_envs))
# Write optimised basis
write_optimised_lo_basis('zr', l_max['zr'], energy_cutoff['zr'],
lorecommendations['zr'],
default_basis_string['zr'], default_los['zr'],
job_dir)
write_optimised_lo_basis('o', l_max['o'], energy_cutoff['o'],
lorecommendations['o'],
default_basis_string['o'], default_los['o'],
job_dir)
return
def set_lo_channel_cutoffs(l_max: dict) -> list:
"""
Define the LO energy cut-offs per LO channel
Ranges from (4, 3) to (7, 6) for (Zr, O)
I assume 150 will be enough but may need to go higher
:return: list energy_cutoffs: Energy cut-offs for each LO channel of Zr and O
"""
# (4, 3)
if l_max['zr'] == 4 and l_max['o'] == 3:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250]
},
'o': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250]
}
}
# (5, 4)
elif l_max['zr'] == 5 and l_max['o'] == 4:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250],
5: [80, 100, 120, 150, 180, 200, 250]
},
'o': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250]
}
}
# (6, 5)
elif l_max['zr'] == 6 and l_max['o'] == 5:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250],
5: [80, 100, 120, 150, 180, 200, 250],
6: [80, 100, 120, 150, 180, 200, 250]
},
'o': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250],
5: [80, 100, 120, 150, 180, 200, 250]
}
}
# (7, 6)
elif l_max['zr'] == 7 and l_max['o'] == 6:
energy_cutoffs = {
'zr': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250],
5: [80, 100, 120, 150, 180, 200, 250],
6: [80, 100, 120, 150, 180, 200, 250],
7: [80, 100, 120, 150, 180, 200, 250]
},
'o': {
0: [80, 100, 120, 150, 180, 200, 250],
1: [80, 100, 120, 150, 180, 200, 250],
2: [80, 100, 120, 150, 180, 200, 250],
3: [80, 100, 120, 150, 180, 200, 250],
4: [80, 100, 120, 150, 180, 200, 250],
5: [80, 100, 120, 150, 180, 200, 250],
6: [80, 100, 120, 150, 180, 200, 250]
}
}
else:
sys.exit("L max pair for Zr and O not valid")
return restructure_energy_cutoffs(n_energies_per_channel, energy_cutoffs)
def set_up_g0w0(root_path: str):
# Material
species = ['zr', 'o']
l_max = {'zr': 3, 'o': 2}
# GW root and exciting input file
gw_root = write_input_file_with_gw_settings(root_path,
A1_gs_input,
GWInput(taskname="g0w0", nempty=2000, ngridq=[2, 2, 2], skipgnd=False, n_omega=32)
)
# Default basis settings
default_linear_energies = parse_lo_linear_energies(root_path + "/groundstate")
default_los = {'zr': DefaultLOs(default_linear_energies['zr'], energy_tol=1.5),
'o': DefaultLOs(default_linear_energies['o'], energy_tol=1.5)}
# Default basis strings with .format tags
default_basis_string = {'zr': parse_basis_as_string(root_path + "/groundstate/Zr.xml"),
'o': parse_basis_as_string(root_path + "/groundstate/O.xml")}
# LO recommendation energies
lorecommendations = parse_lorecommendations(root_path + '/lorecommendations.dat', species)
# Optimised LO energy cutoffs.zr l=2 channel requires way more LOs to converge.
# HOWEVER, with a reduced MT radius, the max cut-off should be less than last time
# Increased rgkmax to 8
# Directory index: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
energy_cutoffs = {'zr': {0: [75, 100, 100, 100, 100, 150, 160, 180, 200, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180],
1: [75, 100, 100, 100, 100, 150, 160, 180, 200, 180, 180, 180, 200, 250, 300, 350, 400, 460, 520],
2: [75, 100, 120, 150, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200],
3: [75, 100, 100, 100, 100, 150, 160, 180, 200, 180, 180, 180, 180, 180, 180, 180, 180, 180, 180]},
'o': {0: [75, 100, 100, 100, 100, 100, 100, 100, 100, 120, 140, 160, 140, 140, 140, 140, 140, 140, 140],
1: [75, 100, 100, 100, 100, 100, 100, 100, 100, 120, 140, 160, 140, 140, 140, 140, 140, 140, 140],
2: [75, 100, 100, 100, 100, 100, 100, 100, 100, 120, 140, 160, 140, 140, 140, 140, 140, 140, 140]}
}
# 12 - 15 really are trying to converge the l=1 channel of Zr, adding one LO at a time (see LO recommendations)
# Then should add one LO into every l=0,2,3 of Zr and check how much it changes.
# Slurm script settings
env_vars = OrderedDict([('EXE', '/users/sol/abuccheri/exciting/bin/excitingmpismp'),
('OUT', 'terminal.out')
])
module_envs = ['intel/2019']
slurm_directives = slurm.set_slurm_directives(time=[0, 24, 0, 0],
partition='all',
exclusive=True,
nodes=4,
ntasks_per_node=2,
cpus_per_task=18,
hint='nomultithread')
species_basis_string = "".join(s.capitalize() + str(l_max[s]) + '_' for s in species)
for ie, energy_cutoff in enumerate(restructure_energy_cutoffs(len(energy_cutoffs['zr'][0]), energy_cutoffs)):
# Copy ground state directory to GW directory
# Use an index not max energy, as the max energy does not change in 3/4 runs
job_dir = gw_root + '/max_energy_i' + str(ie)
print('Creating directory, with input.xml, run.sh and optimised basis:', job_dir)
copy_tree(root_path + '/groundstate', job_dir)
# Copy input.xml with GW settings
shutil.copy(gw_root + "/input.xml", job_dir + "/input.xml")
# New Slurm script
slurm_directives['job-name'] = "gw_A1_lmax_" + species_basis_string + str(ie) + 'loEcutoff'
write_file(job_dir + '/run.sh', slurm.set_slurm_script(slurm_directives, env_vars, module_envs))
# Write optimised basis
write_optimised_lo_basis('zr', l_max['zr'], energy_cutoff['zr'], lorecommendations['zr'],
default_basis_string['zr'], default_los['zr'], job_dir)
write_optimised_lo_basis('o', l_max['o'], energy_cutoff['o'], lorecommendations['o'],
default_basis_string['o'], default_los['o'], job_dir)
return