diff options
Diffstat (limited to 'lib/utils.py')
| -rw-r--r-- | lib/utils.py | 22 |
1 files changed, 16 insertions, 6 deletions
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): |