diff options
| -rwxr-xr-x | lib/dfatool.py | 59 |
1 files changed, 29 insertions, 30 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index 91f6a8a..3d8c321 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -1062,6 +1062,33 @@ def _num_args_from_by_name(by_name): num_args[key] = len(value['args'][0]) return num_args +def get_fit_result(results, name, attribute): + """ + Parse and sanitize fit results for state/transition/... 'name' and model attribute 'attribute'. + + Filters out results where the best function is worse (or not much better than) static mean/median estimates. + + :param results: fit results as returned by `paramfit.results` + :param name: state/transition/... name, e.g. 'TX' + :param attribute: model attribute, e.g. 'duration' + """ + fit_result = dict() + for result in results: + if result['key'][0] == name and result['key'][1] == attribute and result['result']['best'] != None: + this_result = result['result'] + if this_result['best_rmsd'] >= min(this_result['mean_rmsd'], this_result['median_rmsd']): + vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})'.format( + name, attribute, result['key'][2], this_result['best_rmsd'], + this_result['mean_rmsd'], this_result['median_rmsd'])) + # See notes on depends_on_param + elif this_result['best_rmsd'] >= 0.8 * min(this_result['mean_rmsd'], this_result['median_rmsd']): + vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ({:.0f}, {:.0f})'.format( + name, attribute, result['key'][2], this_result['best_rmsd'], + this_result['mean_rmsd'], this_result['median_rmsd'])) + else: + fit_result[result['key'][2]] = this_result + return fit_result + class AnalyticModel: u""" Parameter-aware analytic energy/data size/... model. @@ -1255,21 +1282,7 @@ class AnalyticModel: if name in self._num_args: num_args = self._num_args[name] for attribute in self.by_name[name]['attributes']: - fit_result = {} - for result in paramfit.results: - if result['key'][0] == name and result['key'][1] == attribute and result['result']['best'] != None: - this_result = result['result'] - if this_result['best_rmsd'] >= min(this_result['mean_rmsd'], this_result['median_rmsd']): - vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})'.format( - name, attribute, result['key'][2], this_result['best_rmsd'], - this_result['mean_rmsd'], this_result['median_rmsd'])) - # See notes on depends_on_param - elif this_result['best_rmsd'] >= 0.8 * min(this_result['mean_rmsd'], this_result['median_rmsd']): - vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ({:.0f}, {:.0f})'.format( - name, attribute, result['key'][2], this_result['best_rmsd'], - this_result['mean_rmsd'], this_result['median_rmsd'])) - else: - fit_result[result['key'][2]] = this_result + fit_result = get_fit_result(paramfit.results, name, attribute) if len(fit_result.keys()): x = analytic.function_powerset(fit_result, self.parameters, num_args) @@ -1626,21 +1639,7 @@ class PTAModel: if arg_support_enabled and self.by_name[state_or_tran]['isa'] == 'transition': num_args = self._num_args[state_or_tran] for model_attribute in self.by_name[state_or_tran]['attributes']: - fit_results = {} - for result in paramfit.results: - if result['key'][0] == state_or_tran and result['key'][1] == model_attribute: - fit_result = result['result'] - if fit_result['best_rmsd'] >= min(fit_result['mean_rmsd'], fit_result['median_rmsd']): - vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})'.format( - state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], - fit_result['mean_rmsd'], fit_result['median_rmsd'])) - # See notes on depends_on_param - elif fit_result['best_rmsd'] >= 0.8 * min(fit_result['mean_rmsd'], fit_result['median_rmsd']): - vprint(self.verbose, '[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ({:.0f}, {:.0f})'.format( - state_or_tran, model_attribute, result['key'][2], fit_result['best_rmsd'], - fit_result['mean_rmsd'], fit_result['median_rmsd'])) - else: - fit_results[result['key'][2]] = fit_result + fit_results = get_fit_result(paramfit.results, state_or_tran, model_attribute) if (state_or_tran, model_attribute) in self.function_override: function_str = self.function_override[(state_or_tran, model_attribute)] |