diff options
Diffstat (limited to 'bin/analyze-timing.py')
| -rwxr-xr-x | bin/analyze-timing.py | 20 |
1 files changed, 11 insertions, 9 deletions
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 10c8f1d..e8af0fb 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -80,10 +80,10 @@ import re import sys from dfatool import plotter from dfatool.loader import TimingData, pta_trace_to_aggregate -from dfatool.functions import gplearn_to_function, SplitInfo, AnalyticInfo +from dfatool.functions import gplearn_to_function, StaticFunction, AnalyticFunction from dfatool.model import AnalyticModel from dfatool.validation import CrossValidator -from dfatool.utils import filter_aggregate_by_param +from dfatool.utils import filter_aggregate_by_param, NpEncoder from dfatool.parameters import prune_dependent_parameters opt = dict() @@ -117,7 +117,7 @@ def model_quality_table(result_lists, info_list): for i, results in enumerate(result_lists): info = info_list[i] buf += " ||| " - if info is None or info(state_or_tran, key): + if info is None or type(info(state_or_tran, key)) is not StaticFunction: result = results["by_name"][state_or_tran][key] buf += format_quality_measures(result) else: @@ -387,9 +387,9 @@ if __name__ == "__main__": ].stats.arg_dependence_ratio(i), ) ) - if type(info) is AnalyticInfo: - for param_name in sorted(info.fit_result.keys(), key=str): - param_fit = info.fit_result[param_name]["results"] + if type(info) is AnalyticFunction: + for param_name in sorted(info.fit_by_param.keys(), key=str): + param_fit = info.fit_by_param[param_name]["results"] for function_type in sorted(param_fit.keys()): function_rmsd = param_fit[function_type]["rmsd"] print( @@ -406,13 +406,13 @@ if __name__ == "__main__": for trans in model.names: for attribute in ["duration"]: info = param_info(trans, attribute) - if type(info) is AnalyticInfo: + if type(info) is AnalyticFunction: print( "{:10s}: {:10s}: {}".format( - trans, attribute, info.function.model_function + trans, attribute, info.model_function ) ) - print("{:10s} {:10s} {}".format("", "", info.function.model_args)) + print("{:10s} {:10s} {}".format("", "", info.model_args)) if xv_method == "montecarlo": analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count) @@ -451,4 +451,6 @@ if __name__ == "__main__": extra_function=function, ) + # print(json.dumps(model.to_json(), cls=NpEncoder, indent=2)) + sys.exit(0) |