172 lines
6.5 KiB
Python
172 lines
6.5 KiB
Python
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import numpy as np
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import pandas as pd
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import re
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from monty.re import regrep
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from tqdm import tqdm
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from .universal import Xyz
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from nptyping import NDArray, Shape, Number
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class SCFLog:
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""""""
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def __init__(self, eigenvalues=None, occupation=None, mol_orbs=None):
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""""""
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self.eigenvalues = eigenvalues
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self.occupations = occupation
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self.mol_orbs = mol_orbs
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@property
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def natoms(self):
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if self.mol_orbs is not None:
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return np.max(self.mol_orbs[0]['atom_ids']) + 1
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else:
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return ValueError('natoms might be calculated only if mol_orbs had been read')
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@property
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def nbands(self):
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if self.eigenvalues is not None:
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return len(self.eigenvalues[0])
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elif self.mol_orbs is not None:
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return len(self.mol_orbs[0].columns) - 3
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else:
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return ValueError('nbands might be calculated only if eigenvalues or mol_orbs had been read')
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@property
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def nsteps(self):
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if self.eigenvalues is not None:
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return len(self.eigenvalues)
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elif self.mol_orbs is not None:
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return len(self.mol_orbs)
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else:
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return ValueError('nbands might be calculated only if eigenvalues or mol_orbs had been read')
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@staticmethod
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def from_file(filepath):
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file = open(filepath, 'r')
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data = file.readlines()
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file.close()
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patterns = {'eigs': 'ORBITAL ENERGIES',
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'mos': 'MOLECULAR ORBITALS'}
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matches = regrep(filepath, patterns)
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occs = []
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eigs = []
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for match in tqdm(matches['eigs'], desc='Eigenvalues', total=len(matches['eigs'])):
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eigs_tmp = []
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occs_tmp = []
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i = match[1] + 4
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while data[i] != '\n' and data[i] != '------------------\n':
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line = data[i].split()
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occs_tmp.append(float(line[1]))
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eigs_tmp.append(float(line[3]))
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i += 1
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occs.append(occs_tmp)
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eigs.append(eigs_tmp)
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mos_arr = []
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for match in tqdm(matches['mos'], desc='Molecular Orbitals', total=len(matches['mos'])):
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df = pd.DataFrame()
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first_columns_appended = None
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last_batch_added = False
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i = match[1] + 2
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while data[i] != '\n' and data[i] != '------------------\n':
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if re.match(r'\s*\w+\s+\w+\s+([-+]?\d*\.\d*\s+)+', data[i]) is not None:
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last_batch_added = False
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line = data[i].split()
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if first_columns_appended is False:
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atom_number = re.match(r'\d+', line[0])
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mos_tmp[0].append(int(atom_number[0]))
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atom_symbol = line[0][len(atom_number[0]):]
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mos_tmp[1].append(atom_symbol)
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orbital = line[1]
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mos_tmp[2].append(orbital)
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for j, value in enumerate(line[2:]):
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mos_tmp[3 + j].append(float(value))
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i += 1
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elif first_columns_appended is True:
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for j, value in enumerate(line[2:]):
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mos_tmp[j].append(float(value))
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i += 1
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else:
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pass
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elif re.match(r'\s*(\d+\s+)+', data[i]) is not None:
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line = data[i].split()
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if first_columns_appended is False:
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first_columns_appended = True
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last_batch_added = True
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df['atom_ids'] = mos_tmp[0][1:]
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df['species'] = mos_tmp[1][1:]
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df['orbital'] = mos_tmp[2][1:]
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for j in range(3, len(mos_tmp)):
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df[mos_tmp[j][0]] = mos_tmp[j][1:]
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mos_tmp = [[] for _ in range(len(line))]
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for j, n_mo in enumerate(line):
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mos_tmp[j].append(int(n_mo))
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i += 1
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elif first_columns_appended is None:
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last_batch_added = True
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mos_tmp = [[] for j in range(len(line) + 3)]
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mos_tmp[0].append('')
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mos_tmp[1].append('')
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mos_tmp[2].append('')
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for j, n_mo in enumerate(line):
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mos_tmp[3 + j].append(int(n_mo))
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first_columns_appended = False
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i += 1
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elif first_columns_appended is True:
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last_batch_added = True
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for j in range(len(mos_tmp)):
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df[mos_tmp[j][0]] = mos_tmp[j][1:]
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mos_tmp = [[] for _ in range(len(line))]
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for j, n_mo in enumerate(line):
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mos_tmp[j].append(int(n_mo))
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i += 1
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else:
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i += 1
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if not last_batch_added:
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# df = pd.concat([df, pd.DataFrame(mos_tmp)], axis=1)
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for j in range(len(mos_tmp)):
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df[mos_tmp[j][0]] = mos_tmp[j][1:]
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mos_arr.append(df)
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return SCFLog(np.array(eigs), np.array(occs), mos_arr)
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class XyzTrajectory:
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def __init__(self,
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first_xyz: Xyz,
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trajectory: NDArray[Shape['Nsteps, Natoms, 3'], Number],
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energies_pot: NDArray[Shape['Nsteps'], Number]):
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self.first_xyz = first_xyz
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self.trajectory = trajectory
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self.energies_pot = energies_pot
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@staticmethod
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def from_file(filepath):
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first_xyz = Xyz.from_file(filepath)
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trajectory = []
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energies_pot = []
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with open(filepath, 'rt') as file:
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while True:
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try:
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natoms = int(file.readline().strip())
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except:
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break
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line = file.readline()
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energies_pot.append(float(line.split()[5]))
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#energies_pot.append(float(line.split()[8].split('=')[1]))
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coords = np.zeros((natoms, 3))
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for i in range(natoms):
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line = file.readline().split()
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coords[i] = [float(j) for j in line[1:]]
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trajectory.append(coords)
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return XyzTrajectory(first_xyz, np.array(trajectory), np.array(energies_pot))
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