def generate_energy_data_global_minimum():
jobdef_file = "scripts/JobDef.json"
jobdef = JobDef(jobdef_file)
modelfile = "models/TBcanonical_s.json"
model = Model(modelfile)
input_density_file = "scripts/rho.json"
input_density = InputDensity(input_density_file)
with BackupFiles(input_density_file, jobdef_file, modelfile):
jobdef.write_total_energy()
jobdef.update_hamiltonian("scase")
jobdef.update_model("TBcanonical_s")
jobdef.update_input_rho(input_density_file)
results_dir = jobdef['results_dir']
energy_file = os.path.join(jobdef['results_dir'], "energy.txt")
energy_array_filename = os.path.join(results_dir, "total_energy_array_global_minimum.csv")
execution_args = ['pylato/main.py', jobdef_file]
U_array = np.linspace(0.005, 10, num=100)
energy_array = []
for U in U_array:
input_density.update_U(U)
energy_array.append(calculate_energy_result(
U, 0, 0, model, energy_file, execution_args))
x_label = "U/|t|"
y_label = "energy/eV"
save_1D_raw_data(U_array, energy_array, x_label, y_label, energy_array_filename)
def generate_mag_mom_corr_pcase():
jobdef_file = "scripts/JobDef.json"
jobdef = JobDef(jobdef_file)
modelfile = "models/TBcanonical_p.json"
model = Model(modelfile)
electrons_of_interest = [2]
with BackupFiles(jobdef_file, modelfile):
jobdef.write_magnetic_correlation()
jobdef.write_groundstate_classification()
jobdef.update_hamiltonian("pcase")
jobdef.update_model("TBcanonical_p")
results_dir = jobdef['results_dir']
mag_corr_file = os.path.join(results_dir, "mag_corr.txt")
classification_file = os.path.join(results_dir, "classification.txt")
execution_args = ['pylato/main.py', jobdef_file]
U_array = np.linspace(0.005, 10, num=50)
J_array = np.linspace(0.005, 2.5, num=50)
for num_electrons in electrons_of_interest:
model.update_num_electrons(num_electrons)
mag_corr_result_filename = os.path.join(
results_dir,
"mag_mom_corr_and_class_pcase_{}_electrons_per_atom.csv".
format(num_electrons))
mag_corr_result = {}
classification_result = {}
for U_index, U in enumerate(U_array):
for J_index, J in enumerate(J_array):
key = (U_index, J_index)
if U >= J:
mag_corr_result[key], classification_result[key] = (
calculate_mag_corr_result(
U,
J,
0,
model,
mag_corr_file,
execution_args,
classification_file=classification_file))
else:
mag_corr_result[key] = None
classification_result[key] = None
x_label = "U/|t|"
y_label = "J/|t|"
values_label = "C_avg"
extra_info_label = "classification"
save_2D_with_extra_info_raw_data(
x_vals=U_array,
y_vals=J_array,
results=mag_corr_result,
extra_info=classification_result,
labels=[x_label, y_label, values_label, extra_info_label],
filename=mag_corr_result_filename)
def generate_energy_data_dcase():
jobdef_file = "scripts/JobDef.json"
jobdef = JobDef(jobdef_file)
modelfile = "models/TBcanonical_d.json"
model = Model(modelfile)
electrons_of_interest = [1, 2, 3, 4, 5, 6, 7, 8, 9]
with BackupFiles(jobdef_file, modelfile):
jobdef.write_total_energy()
jobdef.update_hamiltonian("dcase")
jobdef.update_model("TBcanonical_d")
results_dir = jobdef['results_dir']
energy_file = os.path.join(results_dir, "energy.txt")
filename = "total_energy_array_dcase_{}_electrons_per_atom_dJ_{}.csv"
execution_args = ['pylato/main.py', jobdef_file]
dJ_val1 = 0.0
dJ_val2 = 0.1
U_array = np.linspace(0.005, 10, num=5)
J_array = np.linspace(0.005, 2.5, num=5)
for num_electrons in electrons_of_interest:
model.update_num_electrons(num_electrons)
energy_array_filename_1 = os.path.join(
results_dir,
filename.format(num_electrons, dJ_val1)
)
energy_array_filename_2 = os.path.join(
results_dir,
filename.format(num_electrons, dJ_val2)
)
energy_result_1 = {}
energy_result_2 = {}
for U_index, U in enumerate(U_array):
for J_index, J in enumerate(J_array):
if U >= J:
energy_result_1[(U_index, J_index)] = calculate_energy_result(
U, J, dJ_val1, model, energy_file, execution_args)
energy_result_2[(U_index, J_index)] = calculate_energy_result(
U, J, dJ_val2, model, energy_file, execution_args)
else:
energy_result_1[(U_index, J_index)] = None
energy_result_2[(U_index, J_index)] = None
x_label = "U/|t|"
y_label = "J/|t|"
values_label = "energy/eV"
save_2D_raw_data(U_array, J_array, energy_result_1, x_label, y_label, values_label, energy_array_filename_1)
save_2D_raw_data(U_array, J_array, energy_result_2, x_label, y_label, values_label, energy_array_filename_2)
def generate_mag_mom_corr_scase_global_minimum():
jobdef_file = "scripts/JobDef.json"
jobdef = JobDef(jobdef_file)
modelfile = "models/TBcanonical_s.json"
model = Model(modelfile)
input_density_file = "scripts/rho.json"
input_density = InputDensity(input_density_file)
with BackupFiles(input_density_file, jobdef_file, modelfile):
jobdef.write_magnetic_correlation()
jobdef.write_groundstate_classification()
jobdef.update_hamiltonian("scase")
jobdef.update_model("TBcanonical_s")
jobdef.update_input_rho(input_density_file)
results_dir = jobdef['results_dir']
mag_corr_file = os.path.join(results_dir, "mag_corr.txt")
classification_file = os.path.join(results_dir, "classification.txt")
mag_corr_array_filename = os.path.join(
results_dir, "mag_mom_corr_with_class_scase_global_minimum.csv")
execution_args = ['pylato/main.py', jobdef_file]
U_array = np.linspace(0.005, 10, num=100)
mag_corr_array = []
classification_array = []
for U in U_array:
input_density.update_U(U)
mag_corr, classification = calculate_mag_corr_result(
U,
0,
0,
model,
mag_corr_file,
execution_args,
classification_file=classification_file)
mag_corr_array.append(mag_corr)
classification_array.append(classification)
x_label = "U/|t|"
y_label = "C_avg"
extra_info_label = "classification"
save_1D_with_extra_info_raw_data(
x_vals=U_array,
results=mag_corr_array,
extra_info=classification_array,
labels=[x_label, y_label, extra_info_label],
filename=mag_corr_array_filename)
def generate_energy_data_pcase():
jobdef_file = "scripts/JobDef.json"
jobdef = JobDef(jobdef_file)
modelfile = "models/TBcanonical_p.json"
model = Model(modelfile)
with BackupFiles(jobdef_file, modelfile):
jobdef.write_total_energy()
jobdef.update_hamiltonian("pcase")
jobdef.update_model("TBcanonical_p")
results_dir = jobdef['results_dir']
energy_file = os.path.join(results_dir, "energy.txt")
execution_args = ['pylato/main.py', jobdef_file]
U_array = np.linspace(0.005, 10, num=20)
J_array = np.linspace(0.005, 2.5, num=20)
for num_electrons in range(1, 6):
model.update_num_electrons(num_electrons)
energy_array_filename = os.path.join(
results_dir,
"total_energy_array_pcase_{}_electrons_per_atom.csv".format(
num_electrons)
)
energy_result = {}
for U_index, U in enumerate(U_array):
for J_index, J in enumerate(J_array):
energy_result[(U_index, J_index)] = calculate_energy_result(
U, J, 0, model, energy_file, execution_args)
x_label = "U/|t|"
y_label = "J/|t|"
values_label = "energy/eV"
save_2D_raw_data(U_array, J_array, energy_result, x_label, y_label, values_label, energy_array_filename)