diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/dfatool.py | 8 | ||||
| -rw-r--r-- | lib/utils.py | 22 |
2 files changed, 20 insertions, 10 deletions
diff --git a/lib/dfatool.py b/lib/dfatool.py index c4529ce..38e140d 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -413,7 +413,7 @@ def _preprocess_measurement(measurement): class ParamStats: - def __init__(self, by_name, by_param, parameter_names, arg_count, use_corrcoef = False): + def __init__(self, by_name, by_param, parameter_names, arg_count, use_corrcoef = False, verbose = False): """ Compute standard deviation and correlation coefficient on parameterized data partitions. @@ -444,7 +444,7 @@ class ParamStats: for state_or_tran in by_name.keys(): self.stats[state_or_tran] = dict() for attribute in by_name[state_or_tran]['attributes']: - self.stats[state_or_tran][attribute] = compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_tran, attribute) + self.stats[state_or_tran][attribute] = compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_tran, attribute, verbose = verbose) def _generic_param_independence_ratio(self, state_or_trans, attribute): """ @@ -950,7 +950,7 @@ class AnalyticModel: self.parameters = sorted(parameters) self.verbose = verbose - self.stats = ParamStats(self.by_name, self.by_param, self.parameters, {}) + self.stats = ParamStats(self.by_name, self.by_param, self.parameters, {}, verbose = verbose) def _fit(self): paramfit = ParallelParamFit(self.by_param) @@ -1319,7 +1319,7 @@ class PTAModel: self._num_args = arg_count self._use_corrcoef = use_corrcoef self.traces = traces - self.stats = ParamStats(self.by_name, self.by_param, self._parameter_names, self._num_args, self._use_corrcoef) + self.stats = ParamStats(self.by_name, self.by_param, self._parameter_names, self._num_args, self._use_corrcoef, verbose = verbose) self.cache = {} np.seterr('raise') self._outlier_threshold = discard_outliers diff --git a/lib/utils.py b/lib/utils.py index 2cf31be..e86b85d 100644 --- a/lib/utils.py +++ b/lib/utils.py @@ -2,6 +2,15 @@ import numpy as np arg_support_enabled = True +def vprint(verbose, string): + """ + Print string if verbose. + + Prints string if verbose is a True value + """ + if verbose: + print(string) + def is_numeric(n): """Check if n is numeric (i.e., can be converted to int).""" if n == None: @@ -49,7 +58,7 @@ def param_slice_eq(a, b, index): return True return False -def compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_trans, attribute): +def compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_trans, attribute, verbose = False): """ Compute standard deviation and correlation coefficient for various data partitions. @@ -72,6 +81,7 @@ def compute_param_statistics(by_name, by_param, parameter_names, arg_count, stat arg_count -- dict providing the number of functions args ("local parameters") for each function. state_or_trans -- state or transition name, e.g. 'send' or 'TX' attribute -- model attribute, e.g. 'power' or 'duration' + verbose -- print warning if some parameter partitions are too small for fitting returns a dict with the following content: std_static -- static parameter-unaware model error: stddev of by_name[state_or_trans][attribute] @@ -99,16 +109,16 @@ def compute_param_statistics(by_name, by_param, parameter_names, arg_count, stat np.seterr('raise') for param_idx, param in enumerate(parameter_names): - ret['std_by_param'][param] = _mean_std_by_param(by_param, state_or_trans, attribute, param_idx) + ret['std_by_param'][param] = _mean_std_by_param(by_param, state_or_trans, attribute, param_idx, verbose) ret['corr_by_param'][param] = _corr_by_param(by_name, state_or_trans, attribute, param_idx) if arg_support_enabled and state_or_trans in arg_count: for arg_index in range(arg_count[state_or_trans]): - ret['std_by_arg'].append(_mean_std_by_param(by_param, state_or_trans, attribute, len(parameter_names) + arg_index)) + ret['std_by_arg'].append(_mean_std_by_param(by_param, state_or_trans, attribute, len(parameter_names) + arg_index, verbose)) ret['corr_by_arg'].append(_corr_by_param(by_name, state_or_trans, attribute, len(parameter_names) + arg_index)) return ret -def _mean_std_by_param(by_param, state_or_tran, attribute, param_index): +def _mean_std_by_param(by_param, state_or_tran, attribute, param_index, verbose = False): u""" Calculate the mean standard deviation for a static model where all parameters but param_index are constant. @@ -135,9 +145,9 @@ def _mean_std_by_param(by_param, state_or_tran, attribute, param_index): if len(param_partition) > 1: partitions.append(param_partition) elif len(param_partition) == 1: - print('[W] parameter value partition for {} contains only one element -- skipping'.format(param_value)) + vprint(verbose, '[W] parameter value partition for {} contains only one element -- skipping'.format(param_value)) else: - print('[W] parameter value partition for {} is empty'.format(param_value)) + vprint(verbose, '[W] parameter value partition for {} is empty'.format(param_value)) return np.mean([np.std(partition) for partition in partitions]) def _corr_by_param(by_name, state_or_trans, attribute, param_index): |