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Merge pull request #1765 from ReactionMechanismGenerator/logging
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Performance improvements to pdep logging
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@mliu49
mliu49 authored Oct 21, 2019
2 parents 447ff5e + 851ba94 commit ceadaf8
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Showing 2 changed files with 20 additions and 19 deletions.
17 changes: 9 additions & 8 deletions rmgpy/pdep/network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -401,8 +401,8 @@ def set_conditions(self, T, P, ymB=None):
# Update parameters that depend on temperature and pressure if necessary
if temperature_changed or pressure_changed:
self.calculate_collision_model()
logging.debug('Finished setting conditions for network {0}.'.format(self.label))
logging.debug('The network now has values of {0}'.format(repr(self)))
logging.debug('Finished setting conditions for network %s.', self.label)
logging.debug('The network now has values of %r', self)

def _get_energy_grains(self, Emin, Emax, grain_size=0.0, grain_count=0):
"""
Expand Down Expand Up @@ -781,17 +781,18 @@ def calculate_microcanonical_rates(self):
# If the k(E) values are invalid (in that they give the wrong
# kf(T) or kr(T) when integrated), then raise an exception
if error or warning:
logging.warning('For path reaction {0!s}:'.format(rxn))
logging.warning(' Expected kf({0:g} K) = {1:g}'.format(temperature, kf_expected))
logging.warning(' Actual kf({0:g} K) = {1:g}'.format(temperature, kf_actual))
logging.warning(' Expected Keq({0:g} K) = {1:g}'.format(temperature, Keq_expected))
logging.warning(' Actual Keq({0:g} K) = {1:g}'.format(temperature, Keq_actual))
level = logging.WARNING if error else logging.DEBUG
logging.log(level, 'For path reaction %s:', rxn)
logging.log(level, ' Expected kf(%g K) = %g', temperature, kf_expected)
logging.log(level, ' Actual kf(%g K) = %g', temperature, kf_actual)
logging.log(level, ' Expected Keq(%g K) = %g', temperature, Keq_expected)
logging.log(level, ' Actual Keq(%g K) = %g', temperature, Keq_actual)
if error:
raise InvalidMicrocanonicalRateError(
'Invalid k(E) values computed for path reaction "{0}".'.format(rxn), k_ratio, Keq_ratio)
else:
logging.warning('Significant corrections to k(E) to be consistent with high-pressure limit for '
'path reaction "{0}".'.format(rxn))
'path reaction "%s".', rxn)

# import pylab
# for prod in range(n_isom):
Expand Down
22 changes: 11 additions & 11 deletions rmgpy/pdep/reaction.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -105,7 +105,7 @@ def calculate_microcanonical_rate_coefficient(reaction,

# We've been provided with molecular degree of freedom data for the
# transition state, so let's use the more accurate RRKM theory
logging.debug('Calculating microcanonical rate coefficient using RRKM theory for {0}...'.format(reaction))
logging.debug('Calculating microcanonical rate coefficient using RRKM theory for %s...', reaction)
if reactant_states_known and (reaction.is_isomerization() or reaction.is_dissociation()):
kf = apply_rrkm_theory(reaction.transition_state, e_list, j_list, reac_dens_states)
kf *= c0_inv ** (len(reaction.reactants) - 1)
Expand All @@ -121,7 +121,7 @@ def calculate_microcanonical_rate_coefficient(reaction,
elif reaction.kinetics is not None:
# We've been provided with high-pressure-limit rate coefficient data,
# so let's use the less accurate inverse Laplace transform method
logging.debug('Calculating microcanonical rate coefficient using ILT method for {0}...'.format(reaction))
logging.debug('Calculating microcanonical rate coefficient using ILT method for %s...', reaction)
if reactant_states_known:
kinetics = reaction.kinetics if reaction.network_kinetics is None else reaction.network_kinetics
kf = apply_inverse_laplace_transform_method(reaction.transition_state, kinetics, e_list, j_list, reac_dens_states, T)
Expand Down Expand Up @@ -169,8 +169,8 @@ def calculate_microcanonical_rate_coefficient(reaction,
if reac_dens_states[r, s] != 0:
kf[r, s] = kr[r, s] * prod_dens_states[r, s] / reac_dens_states[r, s]
kf *= c0_inv ** (len(reaction.reactants) - len(reaction.products))
logging.debug('Finished finding microcanonical rate coefficients for path reaction {}'.format(reaction))
logging.debug('The forward and reverse rates are found to be {0} and {1} respectively.'.format(kf, kr))
logging.debug('Finished finding microcanonical rate coefficients for path reaction %s', reaction)
logging.debug('The forward and reverse rates are found to be %g and %g respectively.', kf, kr)

return kf, kr

Expand Down Expand Up @@ -231,8 +231,8 @@ def apply_rrkm_theory(transition_state,
for r in range(n_grains):
if sum_states[r, s] > 0 and dens_states[r, s] > 0:
k[r, s] = sum_states[r, s] / dens_states[r, s] * d_e
logging.debug('Finished applying RRKM for path transition state {}'.format(transition_state))
logging.debug('The rate constant is found to be {}'.format(k))
logging.debug('Finished applying RRKM for path transition state %s', transition_state)
logging.debug('The rate constant is found to be %s', k)
return k

@cython.boundscheck(False)
Expand Down Expand Up @@ -330,8 +330,8 @@ def apply_inverse_laplace_transform_method(transition_state,

else:
raise PressureDependenceError('Unable to use inverse Laplace transform method for non-Arrhenius kinetics or for n < 0.')
logging.debug('Finished applying inverse lapace transform for path transition state {}'.format(transition_state))
logging.debug('The rate constant is found to be {}'.format(k))
logging.debug('Finished applying inverse lapace transform for path transition state %s', transition_state)
logging.debug('The rate constant is found to be %s', k)

return k

Expand Down Expand Up @@ -396,7 +396,7 @@ def fit_interpolation_model(reaction, Tlist, Plist, K, model, Tmin, Tmax, Pmin,
log_rms += (log_k_model - log_k_data) * (log_k_model - log_k_data)
log_rms = sqrt(log_rms / len(Tlist) / len(Plist))
if log_rms > 0.5:
logging.warning('RMS error for k(T,P) fit = {0:g} for reaction {1}.'.format(log_rms, reaction))
logging.debug('Finished fitting model for path reaction {}'.format(reaction))
logging.debug('The kinetics fit is {0!r}'.format(kinetics))
logging.warning('RMS error for k(T,P) fit = %g for reaction %s.', log_rms, reaction)
logging.debug('Finished fitting model for path reaction %s', reaction)
logging.debug('The kinetics fit is %r', kinetics)
return kinetics

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