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# -*- coding: utf-8 -*-
from __future__ import print_function, absolute_import
import math, os.path, sys
import numpy as np
# PHYSICAL CONSTANTS UNITS
GAS_CONSTANT = 8.3144621 # J / K / mol
J_TO_AU = 4.184 * 627.509541 * 1000.0 # UNIT CONVERSION
KCAL_TO_AU = 627.509541 # UNIT CONVERSION
class get_pes:
"""
Obtain relative thermochemistry between species and for reactions.
Routine that computes Boltzmann populations of conformer sets at each step of a reaction, obtaining
relative energetic and thermodynamic values for each step in a reaction pathway.
Determines reaction pathway from .yaml formatted file containing definitions for where files fit in pathway.
Attributes:
dec (int): decimal places to display after PES calculations.
units (str): units do display values in, choice of kcal/mol or kJ/mol.
boltz (str): allows for selectivity calculation to display to user.
path (list): list of strings defining each reaction pathway.
species (list): list of strings defining which files correspond to names given in reaction pathway.
spc_abs (list): list of relative single-point energy values.
e_abs (list): list of relative energy values.
zpe_abs (list): list of relative zero point energy values.
h_abs (list): list of relative enthalpy values.
qh_abs (list): list of relative quasi-harmonic enthalpy values.
s_abs (list): list of relative entropy values.
qs_abs (list): list of relative quasi-harmonic entropy values.
g_abs (list): list of relative Gibbs free energy values.
qhg_abs (list): list of relative quasi-harmonic Gibbs free energy values.
cosmo_qhg_abs (list): list of relative COSMO-RS solvation-corrected quasi-harmonic Gibbs free energy values.
spc_zero (list): list of single point energy "zero" species values to compare all other steps in pathway to.
e_zero (list): list of energy "zero" species values to compare all other steps in pathway to.
zpe_zero (list): list of zero point energy "zero" species values to compare all other steps in pathway to.
h_zero (list): list of enthalpy "zero" species values to compare all other steps in pathway to.
qh_zero (list): list of quasi-harmonic enthalpy "zero" species values to compare all other steps in pathway to.
ts_zero (list): list of T*entropy "zero" species values to compare all other steps in pathway to.
qhts_zero (list): list of quasi-harmonic T*entropy "zero" species values to compare all other steps in pathway to.
g_zero (list): list of Gibbs free energy "zero" species values to compare all other steps in pathway to.
qhg_zero (list): list of quasi-harmonic Gibbs free energy "zero" species values to compare all other steps in pathway to.
cosmo_qhg_zero (list): list of COSMO-RS solvation-corrected quasi-harmonic Gibbs free energy "zero" species values to compare all other steps in pathway to.
g_qhgvals (list): relative quasi-harmonic Gibbs free energy values used for graphing.
g_species_qhgzero (list):quasi-harmonic Gibbs free energy "zero" values used for graphing.
g_rel_val (list): relative Gibbs free energy values used for graphing.
"""
def __init__(self, file, thermo_data, log, temperature, gconf, QH, cosmo=None, cosmo_int=None):
# Default values
self.dec, self.units, self.boltz = 2, 'kcal/mol', False
with open(file) as f:
data = f.readlines()
folder, program, names, files, zeros, pes_list = None, None, [], [], [], []
for i, dline in enumerate(data):
if dline.strip().find('PES') > -1:
for j, line in enumerate(data[i + 1:]):
if line.strip().startswith('#'):
pass
elif len(line) <= 2:
pass
elif line.strip().startswith('---'):
break
elif line.strip() != '':
pathway, pes = line.strip().replace(':', '=').split("=")
# Auto-grab first species as zero unless specified
pes_list.append(pes)
zeros.append(pes.strip().lstrip('[').rstrip(']').split(',')[0])
# Look at SPECIES block to determine filenames
if dline.strip().find('SPECIES') > -1:
for j, line in enumerate(data[i + 1:]):
if line.strip().startswith('---'):
break
else:
if line.lower().strip().find('folder') > -1:
try:
folder = line.strip().replace('#', '=').split("=")[1].strip()
except IndexError:
pass
else:
try:
n, f = (line.strip().replace(':', '=').split("="))
# Check the specified filename is also one that GoodVibes has thermochemistry for:
if f.find('*') == -1 and f not in pes_list:
match = None
for key in thermo_data:
if os.path.splitext(os.path.basename(key))[0] in f.replace('[', '').replace(']', '').replace('+', ',').replace(' ', '').split(','):
match = key
if match:
names.append(n.strip())
files.append(match)
else:
log.write(" Warning! " + f.strip() + ' is specified in ' + file +
' but no thermochemistry data found\n')
elif f not in pes_list:
match = []
for key in thermo_data:
if os.path.splitext(os.path.basename(key))[0].find(f.strip().strip('*')) == 0:
match.append(key)
if len(match) > 0:
names.append(n.strip())
files.append(match)
else:
log.write(" Warning! " + f.strip() + ' is specified in ' + file +
' but no thermochemistry data found\n')
except ValueError:
if line.isspace():
pass
elif line.strip().find('#') > -1:
pass
elif len(line) > 2:
warn = " Warning! " + file + ' input is incorrectly formatted for line:\n\t' + line
log.write(warn)
# Look at FORMAT block to see if user has specified any formatting rules
if dline.strip().find('FORMAT') > -1:
for j, line in enumerate(data[i + 1:]):
if line.strip().find('dec') > -1:
try:
self.dec = int(line.strip().replace(':', '=').split("=")[1].strip())
except IndexError:
pass
if line.strip().find('zero') > -1:
zeros = []
try:
zeros.append(line.strip().replace(':', '=').split("=")[1].strip())
except IndexError:
pass
if line.strip().find('units') > -1:
try:
self.units = line.strip().replace(':', '=').split("=")[1].strip()
except IndexError:
pass
if line.strip().find('boltz') > -1:
try:
self.boltz = line.strip().replace(':', '=').split("=")[1].strip()
except IndexError:
pass
for i in range(len(files)):
if len(files[i]) == 1:
files[i] = files[i][0]
species = dict(zip(names, files))
self.path, self.species = [], []
self.spc_abs, self.e_abs, self.zpe_abs, self.h_abs, self.qh_abs, self.s_abs, self.qs_abs, self.g_abs, self.qhg_abs, self.cosmo_qhg_abs = [], [], [], [], [], [], [], [], [], []
self.spc_zero, self.e_zero, self.zpe_zero, self.h_zero, self.qh_zero, self.ts_zero, self.qhts_zero, self.g_zero, self.qhg_zero, self.cosmo_qhg_zero = [], [], [], [], [], [], [], [], [], []
self.g_qhgvals, self.g_species_qhgzero, self.g_rel_val = [], [], []
# Loop over .yaml file, grab energies, populate arrays and compute Boltzmann factors
with open(file) as f:
data = f.readlines()
for i, dline in enumerate(data):
if dline.strip().find('PES') > -1:
n = 0
for j, line in enumerate(data[i + 1:]):
if line.strip().startswith('#') == True:
pass
elif len(line) <= 2:
pass
elif line.strip().startswith('---') == True:
break
elif line.strip() != '':
try:
self.e_zero.append([])
self.spc_zero.append([])
self.zpe_zero.append([])
self.h_zero.append([])
self.qh_zero.append([])
self.ts_zero.append([])
self.qhts_zero.append([])
self.g_zero.append([])
self.qhg_zero.append([])
self.cosmo_qhg_zero.append([])
min_conf = False
spc_zero, e_zero, zpe_zero, h_zero, qh_zero, s_zero, qs_zero, g_zero, qhg_zero = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
h_conf, h_tot, s_conf, s_tot, qh_conf, qh_tot, qs_conf, qs_tot, cosmo_qhg_zero = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
zero_structures = zeros[n].replace(' ', '').split('+')
# Routine for 'zero' values
for structure in zero_structures:
try:
if not isinstance(species[structure], list):
if hasattr(thermo_data[species[structure]], "sp_energy"):
spc_zero += thermo_data[species[structure]].sp_energy
e_zero += thermo_data[species[structure]].scf_energy
zpe_zero += thermo_data[species[structure]].zpe
h_zero += thermo_data[species[structure]].enthalpy
qh_zero += thermo_data[species[structure]].qh_enthalpy
s_zero += thermo_data[species[structure]].entropy
qs_zero += thermo_data[species[structure]].qh_entropy
g_zero += thermo_data[species[structure]].gibbs_free_energy
qhg_zero += thermo_data[species[structure]].qh_gibbs_free_energy
cosmo_qhg_zero += thermo_data[species[structure]].cosmo_qhg
else: # If we have a list of different kinds of structures: loop over conformers
g_min, boltz_sum = sys.float_info.max, 0.0
for conformer in species[
structure]: # Find minimum G, along with associated enthalpy and entropy
if cosmo:
if thermo_data[conformer].cosmo_qhg <= g_min:
min_conf = thermo_data[conformer]
g_min = thermo_data[conformer].cosmo_qhg
else:
if thermo_data[conformer].qh_gibbs_free_energy <= g_min:
min_conf = thermo_data[conformer]
g_min = thermo_data[conformer].qh_gibbs_free_energy
for conformer in species[structure]: # Get a Boltzmann sum for conformers
if cosmo:
g_rel = thermo_data[conformer].cosmo_qhg - g_min
else:
g_rel = thermo_data[conformer].qh_gibbs_free_energy - g_min
boltz_fac = math.exp(-g_rel * J_TO_AU / GAS_CONSTANT / temperature)
boltz_sum += boltz_fac
for conformer in species[
structure]: # Calculate relative data based on Gmin and the Boltzmann sum
if cosmo:
g_rel = thermo_data[conformer].cosmo_qhg - g_min
else:
g_rel = thermo_data[conformer].qh_gibbs_free_energy - g_min
boltz_fac = math.exp(-g_rel * J_TO_AU / GAS_CONSTANT / temperature)
boltz_prob = boltz_fac / boltz_sum
#if no contribution, skip further calculations
if boltz_prob == 0.0:
continue
if hasattr(thermo_data[conformer], "sp_energy") and thermo_data[
conformer].sp_energy != '!':
spc_zero += thermo_data[conformer].sp_energy * boltz_prob
if hasattr(thermo_data[conformer], "sp_energy") and thermo_data[
conformer].sp_energy == '!':
sys.exit(
"Not all files contain a SPC value, relative values will not be calculated.")
e_zero += thermo_data[conformer].scf_energy * boltz_prob
zpe_zero += thermo_data[conformer].zpe * boltz_prob
# Default calculate gconf correction for conformers, skip if no contribution
if gconf and boltz_prob > 0.0 and boltz_prob != 1.0:
h_conf += thermo_data[conformer].enthalpy * boltz_prob
s_conf += thermo_data[conformer].entropy * boltz_prob
s_conf += -GAS_CONSTANT / J_TO_AU * boltz_prob * math.log(boltz_prob)
qh_conf += thermo_data[conformer].qh_enthalpy * boltz_prob
qs_conf += thermo_data[conformer].qh_entropy * boltz_prob
qs_conf += -GAS_CONSTANT / J_TO_AU * boltz_prob * math.log(boltz_prob)
elif gconf and boltz_prob == 1.0:
h_conf += thermo_data[conformer].enthalpy
s_conf += thermo_data[conformer].entropy
qh_conf += thermo_data[conformer].qh_enthalpy
qs_conf += thermo_data[conformer].qh_entropy
else:
h_zero += thermo_data[conformer].enthalpy * boltz_prob
s_zero += thermo_data[conformer].entropy * boltz_prob
g_zero += thermo_data[conformer].gibbs_free_energy * boltz_prob
qh_zero += thermo_data[conformer].qh_enthalpy * boltz_prob
qs_zero += thermo_data[conformer].qh_entropy * boltz_prob
qhg_zero += thermo_data[conformer].qh_gibbs_free_energy * boltz_prob
cosmo_qhg_zero += thermo_data[conformer].cosmo_qhg * boltz_prob
if gconf:
h_adj = h_conf - min_conf.enthalpy
h_tot = min_conf.enthalpy + h_adj
s_adj = s_conf - min_conf.entropy
s_tot = min_conf.entropy + s_adj
g_corr = h_tot - temperature * s_tot
qh_adj = qh_conf - min_conf.qh_enthalpy
qh_tot = min_conf.qh_enthalpy + qh_adj
qs_adj = qs_conf - min_conf.qh_entropy
qs_tot = min_conf.qh_entropy + qs_adj
if QH:
qg_corr = qh_tot - temperature * qs_tot
else:
qg_corr = h_tot - temperature * qs_tot
except KeyError:
log.write(
" Warning! Structure " + structure + ' has not been defined correctly as energy-zero in ' + file + '\n')
log.write(
" Make sure this structure matches one of the SPECIES defined in the same file\n")
sys.exit(" Please edit " + file + " and try again\n")
# Set zero vals here
conformers, single_structure, mix = False, False, False
for structure in zero_structures:
if not isinstance(species[structure], list):
single_structure = True
else:
conformers = True
if conformers and single_structure:
mix = True
if gconf and min_conf is not False:
if mix:
h_mix = h_tot + h_zero
s_mix = s_tot + s_zero
g_mix = g_corr + g_zero
qh_mix = qh_tot + qh_zero
qs_mix = qs_tot + qs_zero
qg_mix = qg_corr + qhg_zero
cosmo_qhg_mix = qg_corr + cosmo_qhg_zero
self.h_zero[n].append(h_mix)
self.ts_zero[n].append(s_mix)
self.g_zero[n].append(g_mix)
self.qh_zero[n].append(qh_mix)
self.qhts_zero[n].append(qs_mix)
self.qhg_zero[n].append(qg_mix)
self.cosmo_qhg_zero[n].append(cosmo_qhg_mix)
elif conformers:
self.h_zero[n].append(h_tot)
self.ts_zero[n].append(s_tot)
self.g_zero[n].append(g_corr)
self.qh_zero[n].append(qh_tot)
self.qhts_zero[n].append(qs_tot)
self.qhg_zero[n].append(qg_corr)
self.cosmo_qhg_zero[n].append(qg_corr)
else:
self.h_zero[n].append(h_zero)
self.ts_zero[n].append(s_zero)
self.g_zero[n].append(g_zero)
self.qh_zero[n].append(qh_zero)
self.qhts_zero[n].append(qs_zero)
self.qhg_zero[n].append(qhg_zero)
self.cosmo_qhg_zero[n].append(cosmo_qhg_zero)
self.spc_zero[n].append(spc_zero)
self.e_zero[n].append(e_zero)
self.zpe_zero[n].append(zpe_zero)
self.species.append([])
self.e_abs.append([])
self.spc_abs.append([])
self.zpe_abs.append([])
self.h_abs.append([])
self.qh_abs.append([])
self.s_abs.append([])
self.g_abs.append([])
self.qs_abs.append([])
self.qhg_abs.append([])
self.cosmo_qhg_abs.append([])
self.g_qhgvals.append([])
self.g_species_qhgzero.append([])
self.g_rel_val.append([]) # graphing
pathway, pes = line.strip().replace(':', '=').split("=")
pes = pes.strip()
points = [entry.strip() for entry in pes.lstrip('[').rstrip(']').split(',')]
self.path.append(pathway.strip())
# Obtain relative values for each species
for i, point in enumerate(points):
if point != '':
# Create values to populate
point_structures = point.replace(' ', '').split('+')
e_abs, spc_abs, zpe_abs, h_abs, qh_abs, s_abs, g_abs, qs_abs, qhg_abs, cosmo_qhg_abs = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
qh_conf, qh_tot, qs_conf, qs_tot, h_conf, h_tot, s_conf, s_tot, g_corr, qg_corr = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
min_conf = False
rel_val = 0.0
self.g_qhgvals[n].append([])
self.g_species_qhgzero[n].append([])
try:
for j, structure in enumerate(point_structures): # Loop over structures, structures are species specified
zero_conf = 0.0
self.g_qhgvals[n][i].append([])
if not isinstance(species[structure], list): # Only one conf in structures
e_abs += thermo_data[species[structure]].scf_energy
if hasattr(thermo_data[species[structure]], "sp_energy"):
spc_abs += thermo_data[species[structure]].sp_energy
zpe_abs += thermo_data[species[structure]].zpe
h_abs += thermo_data[species[structure]].enthalpy
qh_abs += thermo_data[species[structure]].qh_enthalpy
s_abs += thermo_data[species[structure]].entropy
g_abs += thermo_data[species[structure]].gibbs_free_energy
qs_abs += thermo_data[species[structure]].qh_entropy
qhg_abs += thermo_data[species[structure]].qh_gibbs_free_energy
cosmo_qhg_abs += thermo_data[species[structure]].cosmo_qhg
zero_conf += thermo_data[species[structure]].qh_gibbs_free_energy
self.g_qhgvals[n][i][j].append(
thermo_data[species[structure]].qh_gibbs_free_energy)
rel_val += thermo_data[species[structure]].qh_gibbs_free_energy
else: # If we have a list of different kinds of structures: loop over conformers
g_min, boltz_sum = sys.float_info.max, 0.0
# Find minimum G, along with associated enthalpy and entropy
for conformer in species[structure]:
if cosmo:
if thermo_data[conformer].cosmo_qhg <= g_min:
min_conf = thermo_data[conformer]
g_min = thermo_data[conformer].cosmo_qhg
else:
if thermo_data[conformer].qh_gibbs_free_energy <= g_min:
min_conf = thermo_data[conformer]
g_min = thermo_data[conformer].qh_gibbs_free_energy
# Get a Boltzmann sum for conformers
for conformer in species[structure]:
if cosmo:
g_rel = thermo_data[conformer].cosmo_qhg - g_min
else:
g_rel = thermo_data[conformer].qh_gibbs_free_energy - g_min
boltz_fac = math.exp(-g_rel * J_TO_AU / GAS_CONSTANT / temperature)
boltz_sum += boltz_fac
# Calculate relative data based on Gmin and the Boltzmann sum
for conformer in species[structure]:
if cosmo:
g_rel = thermo_data[conformer].cosmo_qhg - g_min
else:
g_rel = thermo_data[conformer].qh_gibbs_free_energy - g_min
boltz_fac = math.exp(-g_rel * J_TO_AU / GAS_CONSTANT / temperature)
boltz_prob = boltz_fac / boltz_sum
if boltz_prob == 0.0:
continue
if hasattr(thermo_data[conformer], "sp_energy") and thermo_data[
conformer].sp_energy != '!':
spc_abs += thermo_data[conformer].sp_energy * boltz_prob
if hasattr(thermo_data[conformer], "sp_energy") and thermo_data[conformer].sp_energy == '!':
sys.exit("\n Not all files contain a SPC value, relative values will not be calculated.\n")
e_abs += thermo_data[conformer].scf_energy * boltz_prob
zpe_abs += thermo_data[conformer].zpe * boltz_prob
if cosmo:
zero_conf += thermo_data[conformer].cosmo_qhg * boltz_prob
rel_val += thermo_data[conformer].cosmo_qhg * boltz_prob
else:
zero_conf += thermo_data[
conformer].qh_gibbs_free_energy * boltz_prob
rel_val += thermo_data[
conformer].qh_gibbs_free_energy * boltz_prob
# Default calculate gconf correction for conformers, skip if no contribution
if gconf and boltz_prob > 0.0 and boltz_prob != 1.0:
h_conf += thermo_data[conformer].enthalpy * boltz_prob
s_conf += thermo_data[conformer].entropy * boltz_prob
s_conf += -GAS_CONSTANT / J_TO_AU * boltz_prob * math.log(boltz_prob)
qh_conf += thermo_data[conformer].qh_enthalpy * boltz_prob
qs_conf += thermo_data[conformer].qh_entropy * boltz_prob
qs_conf += -GAS_CONSTANT / J_TO_AU * boltz_prob * math.log(boltz_prob)
elif gconf and boltz_prob == 1.0:
h_conf += thermo_data[conformer].enthalpy
s_conf += thermo_data[conformer].entropy
qh_conf += thermo_data[conformer].qh_enthalpy
qs_conf += thermo_data[conformer].qh_entropy
else:
h_abs += thermo_data[conformer].enthalpy * boltz_prob
s_abs += thermo_data[conformer].entropy * boltz_prob
g_abs += thermo_data[conformer].gibbs_free_energy * boltz_prob
qh_abs += thermo_data[conformer].qh_enthalpy * boltz_prob
qs_abs += thermo_data[conformer].qh_entropy * boltz_prob
qhg_abs += thermo_data[
conformer].qh_gibbs_free_energy * boltz_prob
cosmo_qhg_abs += thermo_data[conformer].cosmo_qhg * boltz_prob
if cosmo:
self.g_qhgvals[n][i][j].append(thermo_data[conformer].cosmo_qhg)
else:
self.g_qhgvals[n][i][j].append(thermo_data[conformer].qh_gibbs_free_energy)
if gconf:
h_adj = h_conf - min_conf.enthalpy
h_tot = min_conf.enthalpy + h_adj
s_adj = s_conf - min_conf.entropy
s_tot = min_conf.entropy + s_adj
g_corr = h_tot - temperature * s_tot
qh_adj = qh_conf - min_conf.qh_enthalpy
qh_tot = min_conf.qh_enthalpy + qh_adj
qs_adj = qs_conf - min_conf.qh_entropy
qs_tot = min_conf.qh_entropy + qs_adj
if QH:
qg_corr = qh_tot - temperature * qs_tot
else:
qg_corr = h_tot - temperature * qs_tot
self.g_species_qhgzero[n][i].append(zero_conf) # Raw data for graphing
except KeyError:
log.write(" Warning! Structure " + structure + ' has not been defined correctly in ' + file + '\n')
sys.exit(" Please edit " + file + " and try again\n")
self.species[n].append(point)
self.e_abs[n].append(e_abs)
self.spc_abs[n].append(spc_abs)
self.zpe_abs[n].append(zpe_abs)
conformers, single_structure, mix = False, False, False
self.g_rel_val[n].append(rel_val)
for structure in point_structures:
if not isinstance(species[structure], list):
single_structure = True
else:
conformers = True
if conformers and single_structure:
mix = True
if gconf and min_conf is not False:
if mix:
h_mix = h_tot + h_abs
s_mix = s_tot + s_abs
g_mix = g_corr + g_abs
qh_mix = qh_tot + qh_abs
qs_mix = qs_tot + qs_abs
qg_mix = qg_corr + qhg_abs
cosmo_qhg_mix = qg_corr + cosmo_qhg_zero
self.h_abs[n].append(h_mix)
self.s_abs[n].append(s_mix)
self.g_abs[n].append(g_mix)
self.qh_abs[n].append(qh_mix)
self.qs_abs[n].append(qs_mix)
self.qhg_abs[n].append(qg_mix)
self.cosmo_qhg_abs[n].append(cosmo_qhg_mix)
elif conformers:
self.h_abs[n].append(h_tot)
self.s_abs[n].append(s_tot)
self.g_abs[n].append(g_corr)
self.qh_abs[n].append(qh_tot)
self.qs_abs[n].append(qs_tot)
self.qhg_abs[n].append(qg_corr)
self.cosmo_qhg_abs[n].append(qg_corr)
else:
self.h_abs[n].append(h_abs)
self.s_abs[n].append(s_abs)
self.g_abs[n].append(g_abs)
self.qh_abs[n].append(qh_abs)
self.qs_abs[n].append(qs_abs)
self.qhg_abs[n].append(qhg_abs)
self.cosmo_qhg_abs[n].append(cosmo_qhg_abs)
else:
self.species[n].append('none')
self.e_abs[n].append(float('nan'))
n = n + 1
except IndexError:
pass
def jitter(datasets, color, ax, nx, marker, edgecol='black'):
"""Scatter points that may overlap when graphing by randomly offsetting them."""
import numpy as np
for i, p in enumerate(datasets):
y = [p]
x = np.random.normal(nx, 0.015, size=len(y))
ax.plot(x, y, alpha=0.5, markersize=7, color=color, marker=marker, markeredgecolor=edgecol,
markeredgewidth=1, linestyle='None')
def graph_reaction_profile(graph_data, log, options, plt):
"""
Graph a reaction profile using quasi-harmonic Gibbs free energy values.
Use matplotlib package to graph a reaction pathway potential energy surface.
Parameters:
graph_data (get_pes object): potential energy surface object containing relative thermodynamic data.
log (Logger object): Logger to write status updates to user on command line.
options (dict): input options for GV.
plt (matplotlib): matplotlib library reference.
"""
import matplotlib.path as mpath
import matplotlib.patches as mpatches
log.write("\n Graphing Reaction Profile\n")
data = {}
# Get PES data
for i, path in enumerate(graph_data.path):
g_data = []
zero_val = graph_data.qhg_zero[i][0]
for j, e_abs in enumerate(graph_data.e_abs[i]):
species = graph_data.qhg_abs[i][j]
relative = species - zero_val
if graph_data.units == 'kJ/mol':
formatted_g = J_TO_AU / 1000.0 * relative
else:
formatted_g = KCAL_TO_AU * relative # Defaults to kcal/mol
g_data.append(formatted_g)
data[path] = g_data
# Grab any additional formatting for graph
with open(options.graph) as f:
yaml = f.readlines()
#defaults
ylim, color, show_conf, show_gconf, show_title = None, None, True, False, True
label_point, label_xaxis, dpi, dec, legend = False, True, False, 2, False,
colors, gridlines, title = None, False, 'Potential Energy Surface'
for i, line in enumerate(yaml):
if line.strip().find('FORMAT') > -1:
for j, line in enumerate(yaml[i + 1:]):
if line.strip().find('ylim') > -1:
try:
ylim = line.strip().replace(':', '=').split("=")[1].replace(' ', '').strip().split(',')
except IndexError:
pass
if line.strip().find('color') > -1:
try:
colors = line.strip().replace(':', '=').split("=")[1].replace(' ', '').strip().split(',')
except IndexError:
pass
if line.strip().find('title') > -1:
try:
title_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0]
if title_input == 'false' or title_input == 'False':
show_title = False
else:
title = title_input
except IndexError:
pass
if line.strip().find('dec') > -1:
try:
dec = int(line.strip().replace(':', '=').split("=")[1].strip().split(',')[0])
except IndexError:
pass
if line.strip().find('pointlabel') > -1:
try:
label_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if label_input == 'false':
label_point = False
except IndexError:
pass
if line.strip().find('show_conformers') > -1:
try:
conformers = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if conformers == 'false':
show_conf = False
except IndexError:
pass
if line.strip().find('show_gconf') > -1:
try:
gconf_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if gconf_input == 'true':
show_gconf = True
except IndexError:
pass
if line.strip().find('xlabel') > -1:
try:
label_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if label_input == 'false':
label_xaxis = False
except IndexError:
pass
if line.strip().find('dpi') > -1:
try:
dpi = int(line.strip().replace(':', '=').split("=")[1].strip().split(',')[0])
except IndexError:
pass
if line.strip().find('legend') > -1:
try:
legend_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if legend_input == 'false':
legend = False
except IndexError:
pass
if line.strip().find('gridlines') > -1:
try:
gridline_input = line.strip().replace(':', '=').split("=")[1].strip().split(',')[0].lower()
if gridline_input == 'true':
gridlines = True
except IndexError:
pass
# Do some graphing
Path = mpath.Path
fig, ax = plt.subplots()
for i, path in enumerate(graph_data.path):
for j in range(len(data[path]) - 1):
if colors is not None:
if len(colors) > 1:
color = colors[i]
else:
color = colors[0]
else:
color = 'k'
colors = ['k']
if j == 0:
path_patch = mpatches.PathPatch(
Path([(j, data[path][j]), (j + 0.5, data[path][j]), (j + 0.5, data[path][j + 1]),
(j + 1, data[path][j + 1])],
[Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4]),
label=path, fc="none", transform=ax.transData, color=color)
else:
path_patch = mpatches.PathPatch(
Path([(j, data[path][j]), (j + 0.5, data[path][j]), (j + 0.5, data[path][j + 1]),
(j + 1, data[path][j + 1])],
[Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4]),
fc="none", transform=ax.transData, color=color)
ax.add_patch(path_patch)
plt.hlines(data[path][j], j - 0.15, j + 0.15,colors=['k'])
plt.hlines(data[path][-1], len(data[path]) - 1.15, len(data[path]) - 0.85,colors=['k'])
if show_conf:
markers = ['o', 's', 'x', 'P', 'D']
for i in range(len(graph_data.g_qhgvals)): # i = reaction pathways
for j in range(len(graph_data.g_qhgvals[i])): # j = reaction steps
for k in range(len(graph_data.g_qhgvals[i][j])): # k = species
zero_val = graph_data.g_species_qhgzero[i][j][k]
points = graph_data.g_qhgvals[i][j][k]
points[:] = [((x - zero_val) + (graph_data.qhg_abs[i][j] - graph_data.qhg_zero[i][0]) + (
graph_data.g_rel_val[i][j] - graph_data.qhg_abs[i][j])) * KCAL_TO_AU for x in points]
if len(colors) > 1:
jitter(points, colors[i], ax, j, markers[k])
else:
jitter(points, color, ax, j, markers[k])
if show_gconf:
plt.hlines((graph_data.g_rel_val[i][j] - graph_data.qhg_zero[i][0]) * KCAL_TO_AU, j - 0.15,
j + 0.15, linestyles='dashed')
# Annotate points with energy level
if label_point:
for i, path in enumerate(graph_data.path):
for i, point in enumerate(data[path]):
if dec == 1:
ax.annotate("{:.1f}".format(point), (i, point - fig.get_figheight() * fig.dpi * 0.025),
horizontalalignment='center')
else:
ax.annotate("{:.2f}".format(point), (i, point - fig.get_figheight() * fig.dpi * 0.025),
horizontalalignment='center')
if ylim is not None:
ax.set_ylim(float(ylim[0]), float(ylim[1]))
if show_title:
if title is not None:
ax.set_title(title)
else:
ax.set_title("Reaction Profile")
ax.set_ylabel(r"$G_{rel}$ (kcal / mol)")
plt.minorticks_on()
ax.tick_params(axis='x', which='minor', bottom=False)
ax.tick_params(which='minor', labelright=True, right=True)
ax.tick_params(labelright=True, right=True)
if gridlines:
ax.yaxis.grid(linestyle='--', linewidth=0.5)
ax.xaxis.grid(linewidth=0)
ax_label = []
xaxis_text = []
newax_text_list = []
for i, path in enumerate(graph_data.path):
newax_text = []
ax_label.append(path)
for j, e_abs in enumerate(graph_data.e_abs[i]):
if i == 0:
xaxis_text.append(graph_data.species[i][j])
else:
newax_text.append(graph_data.species[i][j])
newax_text_list.append(newax_text)
# Label rxn steps
if label_xaxis:
if colors is not None:
plt.xticks(range(len(xaxis_text)), xaxis_text, color=colors[0])
else:
plt.xticks(range(len(xaxis_text)), xaxis_text, color='k')
locs, labels = plt.xticks()
newax = []
for i in range(len(ax_label)):
if i > 0:
y = ax.twiny()
newax.append(y)
for i in range(len(newax)):
newax[i].set_xticks(locs)
newax[i].set_xlim(ax.get_xlim())
if len(colors) > 1:
newax[i].tick_params(axis='x', colors=colors[i + 1])
else:
newax[i].tick_params(axis='x', colors='k')
newax[i].set_xticklabels(newax_text_list[i + 1])
newax[i].xaxis.set_ticks_position('bottom')
newax[i].xaxis.set_label_position('bottom')
newax[i].xaxis.set_ticks_position('none')
newax[i].spines['bottom'].set_position(('outward', 15 * (i + 1)))
newax[i].spines['bottom'].set_visible(False)
else:
plt.xticks(range(len(xaxis_text)))
ax.xaxis.set_ticklabels([])
if legend:
plt.legend()
if dpi is not False:
plt.savefig('Rxn_profile_' + options.graph.split('.')[0] + '.png', dpi=dpi)
plt.show()
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