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()
#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
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')
