Source code for

"""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:["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:"'{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):"{prop} is not converged - max diff: {np.max(diff) * 100} %") converged = False if converged:"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)