# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
import os,subprocess,re,pandas
import numpy as np
import matplotlib.pyplot as pt
from pyiron.dft.job.generic import GenericDFTJob
from pyiron.base.generic.parameters import GenericParameters
from pyiron.atomistics.structure.atoms import Atoms
try:
from molmod.io.fchk import FCHKFile
from molmod.units import amu,angstrom,electronvolt,centimeter,kcalmol
from molmod.constants import lightspeed
from molmod.periodic import periodic
import tamkin
except ImportError:
pass
__author__ = "Jan Janssen, Sander Borgmans"
__copyright__ = "Copyright 2019, Max-Planck-Institut für Eisenforschung GmbH - " \
"- Computational Materials Design (CM) Department"
__version__ = "1.0"
__maintainer__ = ""
__email__ = ""
__status__ = "trial"
__date__ = "Aug 27, 2019"
[docs]class Gaussian(GenericDFTJob):
def __init__(self, project, job_name):
super(Gaussian, self).__init__(project, job_name)
self.__name__ = "Gaussian"
self._executable_activate(enforce=True)
self.input = GaussianInput()
[docs] def collect_output(self):
output_dict = collect_output(output_file=os.path.join(self.working_directory, 'input.fchk'))
with self.project_hdf5.open("output") as hdf5_output:
for k, v in output_dict.items():
hdf5_output[k] = v
[docs] def to_hdf(self, hdf=None, group_name=None):
super(Gaussian, self).to_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
self.structure.to_hdf(hdf5_input)
self.input.to_hdf(hdf5_input)
[docs] def from_hdf(self, hdf=None, group_name=None):
super(Gaussian, self).from_hdf(hdf=hdf, group_name=group_name)
with self.project_hdf5.open("input") as hdf5_input:
self.input.from_hdf(hdf5_input)
self.structure = Atoms().from_hdf(hdf5_input)
[docs] def log(self):
with open(os.path.join(self.working_directory, 'input.log')) as f:
print(f.read())
[docs] def calc_minimize(self, electronic_steps=None, ionic_steps=None, algorithm=None, ionic_forces=None):
'''
Function to setup the hamiltonian to perform ionic relaxations using DFT. The convergence goal can be set using
either the iconic_energy as an limit for fluctuations in energy or the iconic_forces.
**Arguments**
algorithm: SCF algorithm
electronic_steps (int): maximum number of electronic steps per electronic convergence
ionic_steps (int): maximum number of ionic steps
ionic_forces ('tight' or 'verytight'): convergence criterium for Berny opt (optional)
'''
settings = {}
opt_settings = []
if electronic_steps is not None:
if not 'SCF' in settings:
settings['SCF'] = []
settings['SCF'].append("MaxCycle={}".format(electronic_steps))
if ionic_steps is not None:
opt_settings.append("MaxCycles={}".format(ionic_steps))
if algorithm is not None:
if not 'SCF' in settings:
settings['SCF'] = []
settings['SCF'].append(algorithm)
if ionic_forces is not None:
assert isinstance(ionic_forces,str)
opt_settings.append(ionic_forces)
self.input['jobtype'] = 'opt' + '({})'.format(",".join(opt_settings))*(len(opt_settings)>0)
if not isinstance(self.input['settings'],dict):
self.input['settings'] = settings
else:
self.input['settings'].update(settings)
super(Gaussian, self).calc_minimize(
electronic_steps=electronic_steps,
ionic_steps=ionic_steps,
algorithm=algorithm,
ionic_forces=ionic_forces
)
[docs] def calc_static(self, electronic_steps=None, algorithm=None):
'''
Function to setup the hamiltonian to perform static SCF DFT runs
**Arguments**
algorithm (str): SCF algorithm
electronic_steps (int): maximum number of electronic steps, which can be used to achieve convergence
'''
settings = {}
if electronic_steps is not None:
if not 'SCF' in settings:
settings['SCF'] = []
settings['SCF'].append("MaxCycle={}".format(electronic_steps))
if algorithm is not None:
if not 'SCF' in settings:
settings['SCF'] = []
settings['SCF'].append(algorithm)
self.input['jobtype'] = 'sp'
if not isinstance(self.input['settings'],dict):
self.input['settings'] = settings
else:
self.input['settings'].update(settings)
super(Gaussian, self).calc_static(
electronic_steps=electronic_steps,
algorithm=algorithm
)
[docs] def calc_md(self, temperature=None, n_ionic_steps=1000, time_step=None, n_print=100):
raise NotImplementedError("calc_md() not implemented in Gaussian.")
[docs] def print_MO(self):
'''
Print a list of the MO's with the corresponding orbital energy and occupation.
'''
n_MO = self.get('output/structure/dft/scf_density').shape[0]
for n,index in enumerate(range(n_MO)):
# print orbital information
occ_alpha = int(self.get('output/structure/dft/n_alpha_electrons') > index)
occ_beta = int(self.get('output/structure/dft/n_beta_electrons') > index)
if self.get('output/structure/dft/beta_orbital_e') is None:
orbital_energy = self.get('output/structure/dft/alpha_orbital_e')[index]
print("#{}: \t Orbital energy = {:>10.5f} \t Occ. = {}".format(n,orbital_energy,occ_alpha+occ_beta))
else:
orbital_energy = [self.get('output/structure/dft/alpha_orbital_e')[index],self.get('output/structure/dft/beta_orbital_e')[index]]
print("#{}: \t Orbital energies (alpha,beta) = {:>10.5f},{:>10.5f} \t Occ. = {},{}".format(n,orbital_energy[0],orbital_energy[1],occ_alpha,occ_beta))
[docs] def visualize_MO(self,index,particle_size=0.5,show_bonds=True):
'''
Visualize the MO identified by its index.
**Arguments**
index index of the MO, as listed by print_MO()
particle_size
size of the atoms for visualization, lower value if orbital is too small to see
show_bonds connect atoms or not
**Notes**
This function should always be accompanied with the following commands (in a separate cell)
view[1].update_surface(isolevel=1, color='blue', opacity=.3)
view[2].update_surface(isolevel=-1, color='red', opacity=.3)
This makes sure that the bonding and non-bonding MO's are plotted and makes them transparent
'''
n_MO = self.get('output/structure/dft/scf_density').shape[0]
assert index >= 0 and index < n_MO
assert len(self.get('output/structure/numbers')) < 50 # check whether structure does not become too large for interactive calculation of cube file
# print orbital information
occ_alpha = int(self.get('output/structure/dft/n_alpha_electrons') > index)
occ_beta = int(self.get('output/structure/dft/n_beta_electrons') > index)
if self.get('output/structure/dft/beta_orbital_e') is None:
orbital_energy = self.get('output/structure/dft/alpha_orbital_e')[index]
print("Orbital energy = {:>10.5f} \t Occ. = {}".format(orbital_energy,occ_alpha+occ_beta))
else:
orbital_energy = [self.get('output/structure/dft/alpha_orbital_e')[index],self.get('output/structure/dft/beta_orbital_e')[index]]
print("Orbital energies (alpha,beta) = {:>10.5f},{:>10.5f} \t Occ. = {},{}".format(orbital_energy[0],orbital_energy[1],occ_alpha,occ_beta))
# make cube file
path = self.path+'_hdf5/'+self.name+'/input'
out = subprocess.check_output(
"ml load Gaussian/g16_E.01-intel-2019a;module use /apps/gent/CO7/haswell-ib/modules/all; cubegen 1 MO={} {}.fchk {}.cube".format(index+1,path,path),
stderr=subprocess.STDOUT,
universal_newlines=True,
shell=True,
)
# visualize cube file
try:
import nglview
except ImportError:
raise ImportError("The animate_nma_mode() function requires the package nglview to be installed")
atom_numbers = []
atom_positions = []
with open('{}.cube'.format(path),'r') as f:
for i in range(2):
f.readline()
n_atoms = int(f.readline().split()[0][1:])
for i in range(3):
f.readline()
for n in range(n_atoms):
line = f.readline().split()
atom_numbers.append(int(line[0]))
atom_positions.append(np.array([float(m) for m in line[2:]])/angstrom)
structure = Atoms(numbers=np.array(atom_numbers),positions=atom_positions)
view = nglview.show_ase(structure)
if not show_bonds:
view.add_spacefill(radius_type='vdw', scale=0.5, radius=particle_size)
view.remove_ball_and_stick()
else:
view.add_ball_and_stick()
view.add_component('{}.cube'.format(path))
view.add_component('{}.cube'.format(path))
return view
[docs] def read_NMA(self):
'''
Reads the NMA output from the Gaussian .log file.
Returns:
IR frequencies, intensities and corresponding eigenvectors (modes).
'''
# Read number of atoms
nrat = len(self.get('output/structure/numbers'))
# Read IR frequencies and intensities from log file
low_freqs = []
freqs = []
ints = []
modes = [[] for i in range(nrat)]
path = self.path+'_hdf5/'+self.name+'/input.log'
with open(path,'r') as f:
lines = f.readlines()
# Assert normal termination
assert "Normal termination of Gaussian" in lines[-1]
# Find zero frequencies
for n in range(len(lines)):
line = lines[n]
if 'Low frequencies' in line:
low_freqs += [float(i) for i in line[20:].split()]
if 'Frequencies --' in line:
freqs += [float(i) for i in line[15:].split()]
if 'IR Inten --' in line:
ints += [float(i) for i in line[15:].split()]
if 'Atom AN X Y Z' in line:
for m in range(nrat):
modes[m] += [float(i) for i in lines[n+m+1][10:].split()]
nma_zeros = 3*nrat-len(freqs)
freq_array = np.zeros(3*nrat)
freq_array[:nma_zeros] = np.array(low_freqs[:nma_zeros])
freq_array[nma_zeros:] = np.array(freqs)
freqs = freq_array * (lightspeed/centimeter) # put into atomic units
ints = np.array(ints)
modes = np.array(modes).reshape(len(ints),nrat,3)
return freqs,ints,modes
[docs] def bsse_to_pandas(self):
'''
Convert bsse output of all frames to a pandas Dataframe object.
Returns:
pandas.Dataframe: output as dataframe
'''
assert 'counterpoise' in [k.lower() for k in self.input['settings'].keys()] # check if there was a bsse calculation
tmp = {}
with self.project_hdf5.open('output/structure/bsse') as hdf:
for key in hdf.list_nodes():
tmp[key] = hdf[key] if isinstance(hdf[key],np.ndarray) else [hdf[key]]
df = pandas.DataFrame(tmp)
return df
# we could use theochem iodata, should be more robust than molmod.io
# but we require the latest iodata for this, not the conda version
[docs]def fchk2dict(fchk):
# probably still some data missing
# check job type, for now implement basics (SP=single point, FOpt = full opt, Freq = frequency calculation)
if not fchk.command.lower() in ['sp','fopt','freq']:
raise NotImplementedError
# Basic information
fchkdict = {}
fchkdict['jobtype'] = fchk.command.lower()
fchkdict['lot'] = fchk.lot
fchkdict['basis_set'] = fchk.basis
fchkdict['structure/numbers'] = fchk.fields.get('Atomic numbers')
fchkdict['structure/masses'] = fchk.fields.get('Real atomic weights')*amu
fchkdict['structure/charges'] = fchk.fields.get('Mulliken Charges')
fchkdict['structure/dipole'] = fchk.fields.get('Dipole Moment')
fchkdict['structure/dft/n_electrons'] = fchk.fields.get('Number of electrons')
fchkdict['structure/dft/n_alpha_electrons'] = fchk.fields.get('Number of alpha electrons')
fchkdict['structure/dft/n_beta_electrons'] = fchk.fields.get('Number of beta electrons')
fchkdict['structure/dft/n_basis_functions'] = fchk.fields.get('Number of basis functions')
# Orbital information
fchkdict['structure/dft/alpha_orbital_e'] = fchk.fields.get('Alpha Orbital Energies')
fchkdict['structure/dft/beta_orbital_e'] = fchk.fields.get('Beta Orbital Energies')
# Densities
fchkdict['structure/dft/scf_density'] = _triangle_to_dense(fchk.fields.get('Total SCF Density'))
fchkdict['structure/dft/spin_scf_density'] = _triangle_to_dense(fchk.fields.get('Spin SCF Density'))
if fchk.lot.upper() in ['MP2', 'MP3', 'CC', 'CI']:
# only one of the lots should be present, hence using the same key
fchkdict['structure/dft/post_scf_density'] = _triangle_to_dense(fchk.fields.get('Total {} Density'.format(fchk.lot)))
fchkdict['structure/dft/post_spin_scf_density'] = _triangle_to_dense(fchk.fields.get('Spin {} Density'.format(fchk.lot)))
# Specific job information
if fchkdict['jobtype'] == 'fopt':
if len(fchk.get_optimization_coordinates().shape) == 3:
fchkdict['structure/positions'] = fchk.get_optimization_coordinates()[-1]/angstrom
else:
fchkdict['structure/positions'] = fchk.get_optimization_coordinates()/angstrom
fchkdict['generic/positions'] = fchk.get_optimization_coordinates()/angstrom
fchkdict['generic/energy_tot'] = fchk.get_optimization_energies()/electronvolt
fchkdict['generic/forces'] = fchk.get_optimization_gradients()/(electronvolt/angstrom) * -1
if fchkdict['jobtype'] == 'freq':
fchkdict['structure/positions'] = fchk.fields.get('Current cartesian coordinates').reshape([1,-1, 3])/angstrom
fchkdict['generic/positions'] = fchk.fields.get('Current cartesian coordinates').reshape([1,-1, 3])/angstrom
fchkdict['generic/forces'] = fchk.fields.get('Cartesian Gradient').reshape([-1, 3])/(electronvolt/angstrom) *-1
fchkdict['generic/hessian'] = fchk.get_hessian()/(electronvolt/angstrom**2)
fchkdict['generic/energy_tot'] = fchk.fields.get('Total Energy')/electronvolt
if fchkdict['jobtype'] == 'sp':
fchkdict['structure/positions'] = fchk.fields.get('Current cartesian coordinates').reshape([1,-1, 3])/angstrom
fchkdict['generic/positions'] = fchk.fields.get('Current cartesian coordinates').reshape([1,-1, 3])/angstrom
fchkdict['generic/energy_tot'] = fchk.fields.get('Total Energy')/electronvolt
return fchkdict
[docs]def get_bsse_array(line,it):
numeric_const_pattern = '[-+]? (?: (?: \d* \. \d+ ) | (?: \d+ \.? ) )(?: [Ee] [+-]? \d+ ) ?'
rx = re.compile(numeric_const_pattern, re.VERBOSE)
cE_corr = float(rx.findall(line)[0]) * kcalmol/electronvolt
line = next(it) # go to next line
cE_raw = float(rx.findall(line)[0]) * kcalmol/electronvolt
line = next(it) # go to next line
sum_fragments = float(rx.findall(line)[0])/electronvolt
line = next(it) # go to next line
bsse_corr = float(rx.findall(line)[0])/electronvolt
line = next(it) # go to next line
E_tot_corr = float(rx.findall(line)[0])/electronvolt
return E_tot_corr,bsse_corr,sum_fragments,cE_raw,cE_corr
[docs]def read_bsse(output_file,output_dict):
# Check whether the route section contains the Counterpoise setting (if fchk module is update, route section can be loaded from dict)
cp = False
with open(output_file,'r') as f:
line = f.readline()
while line:
if 'route' in line.lower():
if 'counterpoise' in f.readline().lower(): # read next line
cp = True
break
line = f.readline()
if cp:
# the log file has the same path and name as the output file aside from the file extension
log_file = output_file[:output_file.rfind('.')] + '.log'
frames = 1 if isinstance(output_dict['generic/energy_tot'],float) else len(output_dict['generic/energy_tot'])
output_dict['structure/bsse/energy_tot_corrected'] = np.zeros(frames)
output_dict['structure/bsse/bsse_correction'] = np.zeros(frames)
output_dict['structure/bsse/sum_of_fragments'] = np.zeros(frames)
output_dict['structure/bsse/complexation_energy_raw'] = np.zeros(frames)
output_dict['structure/bsse/complexation_energy_corrected'] = np.zeros(frames)
it = _reverse_readline(log_file)
line = next(it)
for i in range(frames):
found = False
while not found:
line = next(it)
if 'complexation energy' in line:
E_tot_corr,bsse_corr,sum_fragments,cE_raw,cE_corr = get_bsse_array(line,it)
output_dict['structure/bsse/energy_tot_corrected'][i] = E_tot_corr
output_dict['structure/bsse/bsse_correction'][i] = bsse_corr
output_dict['structure/bsse/sum_of_fragments'][i] = sum_fragments
output_dict['structure/bsse/complexation_energy_raw'][i] = cE_raw
output_dict['structure/bsse/complexation_energy_corrected'][i] = cE_corr
found = True
if frames==1:
output_dict['structure/bsse/energy_tot_corrected'] = output_dict['structure/bsse/energy_tot_corrected'][0]
output_dict['structure/bsse/bsse_correction'] = output_dict['structure/bsse/bsse_correction'][0]
output_dict['structure/bsse/sum_of_fragments'] = output_dict['structure/bsse/sum_of_fragments'][0]
output_dict['structure/bsse/complexation_energy_raw'] = output_dict['structure/bsse/complexation_energy_raw'][0]
output_dict['structure/bsse/complexation_energy_corrected'] = output_dict['structure/bsse/complexation_energy_corrected'][0]
else:
# flip array sequence
output_dict['structure/bsse/energy_tot_corrected'] = output_dict['structure/bsse/energy_tot_corrected'][::-1]
output_dict['structure/bsse/bsse_correction'] = output_dict['structure/bsse/bsse_correction'][::-1]
output_dict['structure/bsse/sum_of_fragments'] = output_dict['structure/bsse/sum_of_fragments'][::-1]
output_dict['structure/bsse/complexation_energy_raw'] = output_dict['structure/bsse/complexation_energy_raw'][::-1]
output_dict['structure/bsse/complexation_energy_corrected'] = output_dict['structure/bsse/complexation_energy_corrected'][::-1]
[docs]def read_EmpiricalDispersion(output_file,output_dict):
# Get dispersion term from log file if it is there
# dispersion term is not retrieved from gaussian output in fchk
disp = None
with open(output_file,'r') as f:
while True:
line = f.readline()
if 'Route' in line:
line = f.readline()
if 'EmpiricalDispersion' in line:
idx = line.find('EmpiricalDispersion')
if 'GD3' in line[idx:]:
search_term = 'Grimme-D3 Dispersion energy='
else:
raise NotImplementedError
else:
return
break
# the log file has the same path and name as the output file aside from the file extension
log_file = output_file[:output_file.rfind('.')] + '.log'
it = _reverse_readline(log_file)
while True:
line = next(it)
if search_term in line:
disp = float(line[38:-9])/electronvolt # could be changed when new search terms are implemented
break
output_dict['generic/energy_tot'] += disp
[docs]def collect_output(output_file):
# Read output
fchk = FCHKFile(output_file)
# Translate to dict
output_dict = fchk2dict(fchk)
# Read BSSE output if it is present
read_bsse(output_file,output_dict)
# Correct energy if empirical dispersion contribution is present
read_EmpiricalDispersion(output_file,output_dict)
return output_dict
# function from theochem iodata
def _triangle_to_dense(triangle):
'''Convert a symmetric matrix in triangular storage to a dense square matrix.
Parameters
----------
triangle
A row vector containing all the unique matrix elements of symmetric
matrix. (Either the lower-triangular part in row major-order or the
upper-triangular part in column-major order.)
Returns
-------
ndarray
a square symmetric matrix.
'''
if triangle is None: return None
nrow = int(np.round((np.sqrt(1 + 8 * len(triangle)) - 1) / 2))
result = np.zeros((nrow, nrow))
begin = 0
for irow in range(nrow):
end = begin + irow + 1
result[irow, :irow + 1] = triangle[begin:end]
result[:irow + 1, irow] = triangle[begin:end]
begin = end
return result
def _reverse_readline(filename, buf_size=8192):
'''A generator that returns the lines of a file in reverse order'''
'''https://stackoverflow.com/questions/2301789/read-a-file-in-reverse-order-using-python'''
with open(filename) as fh:
segment = None
offset = 0
fh.seek(0, os.SEEK_END)
file_size = remaining_size = fh.tell()
while remaining_size > 0:
offset = min(file_size, offset + buf_size)
fh.seek(file_size - offset)
buffer = fh.read(min(remaining_size, buf_size))
remaining_size -= buf_size
lines = buffer.split('\n')
# The first line of the buffer is probably not a complete line so
# we'll save it and append it to the last line of the next buffer
# we read
if segment is not None:
# If the previous chunk starts right from the beginning of line
# do not concat the segment to the last line of new chunk.
# Instead, yield the segment first
if buffer[-1] != '\n':
lines[-1] += segment
else:
yield segment
segment = lines[0]
for index in range(len(lines) - 1, 0, -1):
if lines[index]:
yield lines[index]
# Don't yield None if the file was empty
if segment is not None:
yield segment