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_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_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)