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filter_results.py
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filter_results.py
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#!/usr/bin/python
import argparse
import numpy as np
import numpy.typing as npt
from typing import List
from ase.atoms import Atoms
from ase.io.trajectory import Trajectory
def filter_significant_figures(atoms: List[Atoms], significant_figures: int) -> npt.NDArray:
"""
Remove similar local minima from the list based on the relevant number significant figures of the potential energy.
Parameters
----------
atoms: List[Atoms]
List of all local minima found
significant_figures: int
Number of significant figures to round the potential energy to to find the unique local minima
Returns
-------
atoms: ndarray
List of all unique local minima
"""
# Initialise atoms array first because ase.atoms.Atoms will be unpacked by numpy into an array of ase.atom.Atom
_atoms = np.empty(len(atoms), dtype=object)
# Sort atoms by ascending potential energy
_atoms[:] = sorted(atoms, key=lambda a: a.get_potential_energy())
# Get all potential energies
potential_energies = np.array([a.get_potential_energy() for a in _atoms])
# Remove values above zero
indexes = potential_energies < 0
_atoms = _atoms[indexes]
potential_energies = potential_energies[indexes]
# Rounding
# 1. Calculate order of magnitude in 10^i
order_of_magnitudes = np.floor(np.log10(np.abs(potential_energies))).astype(np.int32)
# 2. Calculate position of msd in relation to the decimal point
positions_msd = -(order_of_magnitudes + 1)
# 3. Calculate position for rounding to significant figures in relation to the decimal point
rounding = positions_msd + significant_figures
# 4. Round energies
rounded = np.array([round(energy, d) for (energy, d) in zip(potential_energies, rounding)])
# Only keep unique
_, indexes = np.unique(rounded, return_index=True)
_atoms = _atoms[indexes]
potential_energies = potential_energies[indexes]
return _atoms
def filter_difference(atoms: List[Atoms], difference: float) -> npt.NDArray:
"""
Remove similar local minima from the list based on the minimum difference between the potential energy.
Parameters
----------
atoms: List[Atoms]
List of all local minima found
difference: float
Minimum potential energy difference between unique local minima to check for uniqueness
Returns
-------
atoms: ndarray
List of all unique local minima
"""
# Sort atoms by ascending potential energy
atoms = sorted(atoms, key=lambda a: a.get_potential_energy())
# Always select first element
E = atoms[0].get_potential_energy()
new_atoms = list()
new_atoms.append(atoms[0])
# Append new elements if potential energy difference is greater than diff
for a in atoms[1:]:
potential_energy = a.get_potential_energy()
if potential_energy >= 0: break
if potential_energy - E >= difference:
E = potential_energy
new_atoms.append(a)
# Initialise atoms array first because ase.atoms.Atoms will be unpacked by numpy into an array of ase.atom.Atom
_atoms = np.empty(len(new_atoms), dtype=object)
_atoms[:] = new_atoms
return _atoms
def filter_trajectory(input: str, output: str=None, filter_type: str="s", significant_figures: int=2, difference: float=0.1):
"""
Remove similar local minima from a trajectory based on the relevant number significant figures of the potential energy.
Parameters
----------
input: str
File path to the input trajectory
output: str, None, optional
File path to the trajectory to store filtered local minima
If None, the input file will be replaced by the new trajectory with filtered local minima
filter_type: {'s', 'd'}, optional
Which filter type to use:
- s : significant figures filter
- d : difference filter \n
(default = 's')
significant_figures: int, optional
Number of significant figures to round the potential energy to to find the unique local minima
(default = 2)
difference: float
Minimum potential energy difference between unique local minima to check for uniqueness
(default = 0.1)
"""
if output is None:
output = input
# Load input trajectory
trajectory = Trajectory(input)
# Filter atoms
if filter_type == "s":
atoms = filter_significant_figures(trajectory[:], significant_figures)
elif filter_type == "d":
atoms = filter_difference(trajectory[:], difference)
else:
raise ValueError("Invalid value for filter_type")
# Close trajectory
trajectory.close()
# Load output trajectory
trajectory = Trajectory(output, 'w')
# Write atoms
for a in atoms:
trajectory.write(a)
# Close trajectory
trajectory.close()
def main(**kwargs):
filter_trajectory(kwargs.get('input'), kwargs.get('output', None), kwargs.get('filter_type', 's'), kwargs.get('significant_figures', 2), kwargs.get('difference', 0.1))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, required=True, help="Input file")
filter_types = parser.add_mutually_exclusive_group()
filter_types.add_argument("-fs", "--filter-significant-figures", action="store_const", dest="filter_type", const="s", default="s", help="Use significant figures filter")
filter_types.add_argument("-fd", "--filter-difference", action="store_const", dest="filter_type", const="d", help="Use difference filter")
parser.add_argument("-sf", "--significant-figures", type=int, default=2, help="Significant figures to round the potential energy to to check for uniqueness")
parser.add_argument("-d", "--difference", type=float, default=0.1, help="Minimum potential energy difference between unique local minima to check for uniqueness")
parser.add_argument("-o", "--output", type=str, default=None, help="Output file (default = input file)")
args = parser.parse_args()
main(**vars(args))