from renormalizer.model import load_from_dict
from renormalizer.transport import ChargeTransport
from renormalizer.utils import log, Quantity, EvolveConfig, EvolveMethod, RungeKutta, CompressConfig, BondDimDistri
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if len(sys.argv) != 2:
print("No or more than one parameter file are provided, abort")
exit(1)
parameter_path = sys.argv[1]
with open(parameter_path) as fin:
param = yaml.safe_load(fin)
log.register_file_output(
os.path.join(param["output dir"], param["fname"] + ".log"), "w")
mol_list, temperature = load_from_dict(param, 3, False)
compress_config = CompressConfig(threshold=1e-4)
evolve_config = EvolveConfig(adaptive=True, evolve_dt=2)
ct = ChargeTransport(
mol_list,
temperature=temperature,
compress_config=compress_config,
evolve_config=evolve_config,
rdm=False,
)
# ct.stop_at_edge = True
# ct.memory_limit = 2 ** 30 # 1 GB
# ct.memory_limit /= 10 # 100 MB
ct.dump_dir = param["output dir"]
ct.job_name = param["fname"]
ct.custom_dump_info["comment"] = param["comment"]
import sys
import logging
import yaml
from renormalizer.model import load_from_dict
from renormalizer.transport import TransportKubo
from renormalizer.utils import log, EvolveConfig, CompressConfig
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if len(sys.argv) != 2:
print("No or more than one parameter file are provided, abort")
exit(1)
parameter_path = sys.argv[1]
with open(parameter_path) as fin:
param = yaml.safe_load(fin)
log.register_file_output(
os.path.join(param["output dir"], param["fname"] + ".log"), "w"
)
log.set_stream_level(logging.INFO)
model, temperature = load_from_dict(param, 3, False)
compress_config = CompressConfig(threshold=1e-4)
ievolve_config = EvolveConfig(adaptive=True, guess_dt=temperature.to_beta() / 1000j)
evolve_config = EvolveConfig(adaptive=True, guess_dt=2)
ct = TransportKubo(model, temperature=temperature, ievolve_config=ievolve_config,
compress_config=compress_config, evolve_config=evolve_config, dump_dir=param["output dir"],
job_name=param["fname"] + "_autocorr")
ct.evolve(param.get("evolve dt"), param.get("nsteps"), param.get("evolve time"))