"""Module defining functions to run amset."""
from __future__ import annotations
import logging
import subprocess
import numpy as np
from pydash import get
logger = logging.getLogger(__name__)
_CONVERGENCE_PROPERTIES = ("mobility.overall", "seebeck")
[docs]
def run_amset() -> None:
"""Run amset in the current directory."""
# Run AMSET using the command line as calling from python can cause issues
# with multiprocessing
with open("std_out.log", "w") as f_std, open("std_err.log", "w") as f_err:
subprocess.call(["amset", "run"], stdout=f_std, stderr=f_err) # noqa: S603, S607
[docs]
def check_converged(
new_transport: dict,
old_transport: dict,
properties: tuple[str, ...] = _CONVERGENCE_PROPERTIES,
tolerance: float = 0.1,
) -> bool:
"""
Check if all transport properties (averaged) are converged within the tol.
Parameters
----------
new_transport : dict
The new transport data.
old_transport : dict
The old transport data.
properties : tuple of str
List of properties for which convergence is assessed. The calculation is only
flagged as converged if all properties pass the convergence checks. Options are:
"conductivity", "seebeck", "mobility.overall", "electronic thermal conductivity.
tolerance : float
Relative convergence tolerance. Default is ``0.1`` (i.e. 10 %).
Returns
-------
bool
Whether the new transport data is converged.
"""
converged = True
for prop in properties:
new_prop = get(new_transport, prop, None)
old_prop = get(old_transport, prop, None)
if new_prop is None or old_prop is None:
logger.info(f"'{prop}' not in new or old transport data, skipping...")
continue
new_avg = tensor_average(new_prop)
old_avg = tensor_average(old_prop)
diff = np.abs((new_avg - old_avg) / new_avg)
diff[~np.isfinite(diff)] = 0
# don't check convergence of very small numbers due to numerical noise
less_than_one = (np.abs(new_avg) < 1) & (np.abs(old_avg) < 1)
element_converged = less_than_one | (diff <= tolerance)
if not np.all(element_converged):
logger.info(f"{prop} is not converged - max diff: {np.max(diff) * 100} %")
converged = False
if converged:
logger.info("amset calculation is converged.")
return converged
[docs]
def tensor_average(tensor: list | np.ndarray) -> float | np.ndarray:
"""Calculate the average of the tensor eigenvalues.
Parameters
----------
tensor : list or numpy array
A tensor
Returns
-------
float or numpy array
The average of the eigenvalues.
"""
return np.average(np.linalg.eigvalsh(tensor), axis=-1)