def main(args=None):
"""The CLI main entry point function.
The optional argument args, can be used to
directly supply command line argument like
$ catmap <args>
otherwise args will be taken from STDIN.
"""
from glob import glob
options, args, parser = get_options(args, get_parser=True)
if not args[0] in usage.keys():
args[0] = match_keys(args[0], usage, parser)
elif args[0] == 'import':
if len(args) < 2:
parser.error('mkm filename expected.')
from catmap import ReactionModel
mkm_file = args[1]
global model
model = ReactionModel(setup_file=mkm_file)
sh(banner=
'Note: model = catmap.ReactionModel(setup_file=\'%s\')\n# do model.run()\nfor a fully initialized model.'
% args[1])
def main(args=None):
"""The CLI main entry point function.
The optional argument args, can be used to
directly supply command line argument like
$ catmap <args>
otherwise args will be taken from STDIN.
"""
from glob import glob
options, args, parser = get_options(args, get_parser=True)
if not args[0] in usage.keys():
args[0] = match_keys(args[0], usage, parser)
elif args[0] == 'import':
if len(args) < 2:
parser.error('mkm filename expected.')
from catmap import ReactionModel
mkm_file = args[1]
global model
model = ReactionModel(setup_file=mkm_file)
sh(banner=
'Note: model = catmap.ReactionModel(setup_file=\'%s\')\n# do model.run()\nfor a fully initialized model.'
% args[1])
elif args[0] == 'graphviz':
import catmap.analyze.mechanism
import catmap.model
if len(args) < 2:
parser.error('mkm filename expected.')
mkm_file = args[1]
seed = mkm_file.split('.')[0]
model = catmap.model.ReactionModel(setup_file=mkm_file)
mechanism = catmap.analyze.mechanism.MechanismAnalysis(model)
graph = mechanism.create_graph()
graph.render(seed)
from catmap import ReactionModel
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
scaler_variables = ['rxn_parameter', 'frequency', 'electronic_energy',
'free_energy', 'zero_point_energy', 'enthalpy', 'entropy']
# Warning: adding 'rate_control' or 'selectivity_control' to
# output_variables increases calculation time significantly
solver_variables = ['production_rate', 'rate_control', 'coverage', 'rate',
'consumption_rate', 'selectivity', 'rxn_direction',
'rate_constant', 'equilibrium_constant', 'carbon_selectivity',
'selectivity_control', 'rxn_order', 'apparent_activation_energy',
'interacting_energy', 'directional_rates', 'forward_rate', 'reverse_rate',
'forward_rate_constant', 'reverse_rate_constant']
two_dim_variables = ['rate_control', 'selectivity_control', 'rxn_order', 'frequency']
one_dim_variables = list(set(scaler_variables + solver_variables) - set(two_dim_variables))
model.output_variables += solver_variables + scaler_variables
model.run()
from catmap import analyze
vm = analyze.VectorMap(model)
vm.log_scale = True # not necessarily the right choice of parameters for all output_variables
vm.min = 1e-25
vm.max = 1e3
vm.threshold = 1e-25
vm.unique_only = False
for out_var in one_dim_variables:
vm.plot_variable = out_var
fig = vm.plot(save=out_var + '.pdf')
fig.clf()
import pylab as plt
from catmap import ReactionModel
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables += [
'production_rate', 'interaction_matrix', 'free_energy'
]
model.create_standalone = True
model.run()
from catmap import analyze
vm = analyze.VectorMap(model)
vm.plot_variable = 'production_rate' # tell the model which output to plot
vm.log_scale = True # rates should be plotted on a log-scale
vm.min = 1e-25 # minimum rate to plot
vm.max = 1e8 # maximum rate to plot
vm.threshold = 1e-25 # anything below this is considered to be 0
vm.subplots_adjust_kwargs = {'left': 0.2, 'right': 0.8, 'bottom': 0.15}
vm.plot(save='production_rate.pdf')
vm.plot_variable = 'coverage' # tell the model which output to plot
vm.log_scale = False # coverage should not be plotted on a log-scale
vm.min = 0 # minimum coverage
vm.max = 1 # maximum coverage
vm.subplots_adjust_kwargs = {
'left': 0.1,
'right': 0.85,
'wspace': 0.6,
'bottom': 0.15
}
#SBATCH --time=1:00:00 #default is 20 hours #SBATCH --nodes=1 ##SBATCH --mem-per-cpu=4000 #SBATCH --mail-type=END,FAIL #get emailed about job BEGIN, END, or FAIL #SBATCH [email protected] ##SBATCH --ntasks-per-node=16 #task to run per node; each node has 16 cores import catmap import numpy as np import os from catmap import ReactionModel from catmap import analyze from matplotlib import pyplot as plt #mkm_file = [f for f in os.listdir('.') if f.endswith('.mkm')][0] model = ReactionModel(setup_file='mechanism.mkm') model.output_variables.append('production_rate') model.output_variables.append('free_energy') #model.output_variables.append('interaction_matrix') model.run() vm = analyze.VectorMap(model) vm.model_name = 'CH3OH' vm.plot_variable = 'free_energy' vm.log_scale = False vm.plot(save=vm.plot_variable + '.pdf') vm.plot_variable = 'coverage' vm.min = 0 vm.max = 1 vm.plot(save=vm.plot_variable + '.pdf')
from catmap import ReactionModel
from catmap import analyze
import numpy as np
import cPickle as pickle
include_overbinding = False
include_rate_control = False
mkm_file = 'EtOH.mkm'
model = ReactionModel(setup_file=mkm_file)
model.create_standalone = True
# We should probably omit the overbinding correction from the tutorial to simplify things.
if include_overbinding:
overbinding = 0.25
CO_energies = model.species_definitions['CO_s']['formation_energy']
CO_energies = [E + overbinding for E in CO_energies]
model.species_definitions['CO_s']['formation_energy'] = CO_energies
model.output_variables += ['production_rate', 'selectivity',
'zero_point_energy', 'enthalpy', 'entropy', 'free_energy']
model.output_variables += ['interaction_matrix', 'interacting_energy', 'equilibrium_constant']
if include_rate_control:
model.output_variables += ['rate_control']
model.interaction_strength = 0.1 # Otherwise it won't converge
# Tutorial should consider showing "integration" along interaction_strength (e.g. use the
# ouput of interaction_strength=0.1 as input to interaction_strength=0.2 and so on). It
# is ugly, but its the best option at the moment for stubborn ads-ads interaction models,
# and most ads-ads interaction models are stubborn.
model.run()
from catmap import ReactionModel
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables += ['production_rate'] # ,'rate_control']
model.run()
#model = ReactionModel(setup_file=mkm_file.replace('mkm','log'))
from catmap import analyze
vm = analyze.VectorMap(model)
vm.plot_variable = 'production_rate' # tell the model which output to plot
vm.log_scale = True # rates should be plotted on a log-scale
vm.min = 1e-25 # minimum rate to plot
vm.max = 1e3 # maximum rate to plot
vm.descriptor_labels = ['O reactivity [eV]', 'CO reactivity [eV]', ]
vm.threshold = 1e-25 # anything below this is considered to be 0
vm.subplots_adjust_kwargs = {'left': 0.2, 'right': 0.8, 'bottom': 0.15}
vm.plot(save='pretty_production_rate.pdf')
#mm = analyze.MatrixMap(model)
#mm.plot_variable = 'rate_control'
#mm.log_scale = False
#mm.min = -2
#mm.max = 2
# mm.plot(save='rate_control.pdf')
ma = analyze.MechanismAnalysis(model)
ma.energy_type = 'free_energy' # can also be free_energy/potential_energy
ma.include_labels = False # way too messy with labels
"""This mkm_job script generates the same results as the tutorialsoutput_variables/mkm_job.py script
however it demonstrates that values of output_variables do not need to be all hard-coded
FIXME: At this point we still need to hard-code two-dimensional variables, since this is difficult to glean from code inspection only.
FIXME: Output variables 'turnover_frequency' and 'interaction_matrix' currently crash, we should fix them or remove them from the parser.
"""
from catmap import ReactionModel
from catmap import analyze
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables = ['all'] # <==== Here is where the 'all' magic happens
two_dim_variables = [
'rate_control', 'selectivity_control', 'rxn_order', 'frequency',
'interaction_matrix'
]
# !!! However we still need to hard-code those variables that are supposed to go through MatrixMap instead of VectorMap
model.run()
vm = analyze.VectorMap(model)
mm = analyze.MatrixMap(model)
def set_defaults(vm, mm):
vm.log_scale = True
vm.min = 1e-25
vm.max = 1e3
vm.threshold = 1e-25
vm.unique_only = False
mm.log_scale = False
"""This mkm_job script generates the same results as the tutorialsoutput_variables/mkm_job.py script
however it demonstrates that values of output_variables do not need to be all hard-coded
FIXME: At this point we still need to hard-code two-dimensional variables, since this is difficult to glean from code inspection only.
FIXME: Output variables 'turnover_frequency' and 'interaction_matrix' currently crash, we should fix them or remove them from the parser.
"""
from catmap import ReactionModel
from catmap import analyze
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables = ['all'] # <==== Here is where the 'all' magic happens
two_dim_variables = ['rate_control', 'selectivity_control',
'rxn_order', 'frequency', 'interaction_matrix']
# !!! However we still need to hard-code those variables that are supposed to go through MatrixMap instead of VectorMap
model.run()
vm = analyze.VectorMap(model)
mm = analyze.MatrixMap(model)
def set_defaults(vm, mm):
vm.log_scale = True
vm.min = 1e-25
vm.max = 1e3
vm.threshold = 1e-25
vm.unique_only = False
mm.log_scale = False
mm.min = -2
mm.max = 2
from catmap import ReactionModel
import sys
from string import Template
model = ReactionModel(setup_file='HER.mkm')
model.output_variables += [
'production_rate', 'free_energy', 'selectivity', 'directional_rates'
]
model.run()
from catmap import analyze
vm = analyze.VectorMap(model)
vm.plot_variable = 'rate'
vm.log_scale = True
vm.min = 1e-10
vm.max = 1e6
fig = vm.plot(save='rate.png')
vm.plot_variable = 'production_rate'
vm.log_scale = True
vm.min = 1e-10
vm.max = 1e6
fig = vm.plot(save='production_rate.png')
vm.plot_variable = 'coverage'
vm.min = 0
vm.max = 1
vm.log_scale = False
fig = vm.plot(save='coverage.png')
from catmap import ReactionModel
from catmap.parsers import TableParser
mkm_file = 'RWGS.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables += ['production_rate', 'rate_control']
model.run()
from catmap import analyze
mm = analyze.MatrixMap(model)
mm.plot_variable = 'rate_control'
mm.log_scale = False
mm.min = -2
mm.max = 2
mm.subplots_adjust_kwargs = {'wspace': 0.6, 'hspace': 0.6}
mm.plot(save='rate_control.pdf')
vm = analyze.VectorMap(model)
vm.plot_variable = 'rate' #tell the model which output to plot
vm.log_scale = True #rates should be plotted on a log-scale
vm.min = 1e-8 #minimum rate to plot
vm.max = 1e5 #maximum rate to plot
vm.plot(save='rate.pdf') #draw the plot and save it as "rate.pdf"
vm.unique_only = False
vm.subplots_adjust_kwargs = {
'left': 0.1,
'right': 0.9,
'bottom': 0.15,
'wspace': 0.5,
import pylab as plt
from catmap import ReactionModel
mkm_file = 'CO_oxidation.mkm'
model = ReactionModel(setup_file=mkm_file)
model.output_variables += ['production_rate','interaction_matrix','free_energy']
model.create_standalone = True
model.run()
from catmap import analyze
vm = analyze.VectorMap(model)
vm.plot_variable = 'production_rate' #tell the model which output to plot
vm.log_scale = True #rates should be plotted on a log-scale
vm.min = 1e-25 #minimum rate to plot
vm.max = 1e8 #maximum rate to plot
vm.threshold = 1e-25 #anything below this is considered to be 0
vm.subplots_adjust_kwargs = {'left':0.2,'right':0.8,'bottom':0.15}
vm.plot(save='production_rate.pdf')
vm.plot_variable = 'coverage' #tell the model which output to plot
vm.log_scale = False #coverage should not be plotted on a log-scale
vm.min = 0 #minimum coverage
vm.max = 1 #maximum coverage
vm.subplots_adjust_kwargs = {'left':0.1,'right':0.85,'wspace':0.6,'bottom':0.15}
vm.plot(save='coverage.pdf')
from catmap import ReactionModel
from catmap import analyze
import numpy as np
import cPickle as pickle
include_overbinding = False
include_rate_control = False
mkm_file = 'EtOH.mkm'
model = ReactionModel(setup_file=mkm_file)
model.create_standalone = True
if include_overbinding:
overbinding = 0.25
CO_energies = model.species_definitions['CO_s']['formation_energy']
CO_energies = [E+overbinding for E in CO_energies]
model.species_definitions['CO_s']['formation_energy'] = CO_energies
model.output_variables += ['production_rate','selectivity','zero_point_energy','enthalpy','entropy','free_energy']
model.output_variables += ['interaction_matrix','interacting_energy','equilibrium_constant']
if include_rate_control:
model.output_variables += ['rate_control']
model.run()
vm = analyze.VectorMap(model)
vm.log_scale = True #rates should be plotted on a log-scale
vm.min = 1e-15 #minimum rate to plot
vm.max = 1 #maximum rate to plot
vm.threshold = 1e-25 #do not plot rates below this
vm.descriptor_labels = ['Temperature [K]','log$_{10}$(Pressure [bar])']
vm.plot_variable = 'production_rate'
from catmap import ReactionModel
from catmap import analyze
import numpy as np
import cPickle as pickle
include_overbinding = False
include_rate_control = False
mkm_file = 'EtOH.mkm'
model = ReactionModel(setup_file=mkm_file)
model.create_standalone = True
if include_overbinding:
overbinding = 0.25
CO_energies = model.species_definitions['CO_s']['formation_energy']
CO_energies = [E + overbinding for E in CO_energies]
model.species_definitions['CO_s']['formation_energy'] = CO_energies
model.output_variables += [
'production_rate', 'selectivity', 'zero_point_energy', 'enthalpy',
'entropy', 'free_energy'
]
model.output_variables += [
'interaction_matrix', 'interacting_energy', 'equilibrium_constant'
]
if include_rate_control:
model.output_variables += ['rate_control']
model.run()
vm = analyze.VectorMap(model)
vm.log_scale = True #rates should be plotted on a log-scale