diff options
Diffstat (limited to 'bin/analyze-archive.py')
| -rwxr-xr-x | bin/analyze-archive.py | 30 |
1 files changed, 16 insertions, 14 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index 447c6f9..a111492 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -44,6 +44,7 @@ import re import sys import time import dfatool.cli +import dfatool.utils from dfatool import plotter from dfatool.loader import RawData, pta_trace_to_aggregate from dfatool.functions import ( @@ -55,13 +56,6 @@ from dfatool.functions import ( ) from dfatool.model import PTAModel from dfatool.validation import CrossValidator -from dfatool.utils import ( - filter_aggregate_by_param, - shift_param_in_aggregate, - detect_outliers_in_aggregate, - NpEncoder, - is_numeric, -) from dfatool.automata import PTA @@ -307,7 +301,7 @@ def get_kconfig(model): buf += f' prompt "{param_name}"\n' if unique_values == {0, 1}: buf += " bool\n" - elif all(map(is_numeric, unique_values)): + elif all(map(dfatool.utils.is_numeric, unique_values)): buf += " int\n" buf += f" range {min(unique_values)} {max(unique_values)}\n" else: @@ -605,13 +599,17 @@ if __name__ == "__main__": preprocessed_data, ignored_trace_indexes ) - filter_aggregate_by_param(by_name, parameters, args.filter_param) + if args.ignore_param: + args.ignore_param = args.ignore_param.split(",") + dfatool.utils.ignore_param(by_name, parameters, args.ignore_param) + + dfatool.utils.filter_aggregate_by_param(by_name, parameters, args.filter_param) if args.param_shift: param_shift = dfatool.cli.parse_param_shift(args.param_shift) - shift_param_in_aggregate(by_name, parameters, param_shift) + dfatool.utils.shift_param_in_aggregate(by_name, parameters, param_shift) - detect_outliers_in_aggregate( + dfatool.utils.detect_outliers_in_aggregate( by_name, z_limit=args.z_score, remove_outliers=args.remove_outliers ) @@ -908,7 +906,7 @@ if __name__ == "__main__": if args.export_raw_predictions: analytic_quality, raw_results = model.assess(param_model, return_raw=True) with open(args.export_raw_predictions, "w") as f: - json.dump(raw_results, f, cls=NpEncoder) + json.dump(raw_results, f, cls=dfatool.utils.NpEncoder) else: analytic_quality = model.assess(param_model) xv_analytic_models = None @@ -1046,7 +1044,9 @@ if __name__ == "__main__": "Note: v0 measurements do not embed the PTA used for benchmark generation. Estimating PTA from recorded observations." ) json_model = model.to_json() - json_model_str = json.dumps(json_model, indent=2, sort_keys=True, cls=NpEncoder) + json_model_str = json.dumps( + json_model, indent=2, sort_keys=True, cls=dfatool.utils.NpEncoder + ) for function_str, function_body in model.webconf_function_map(): json_model_str = json_model_str.replace(function_str, function_body) @@ -1066,7 +1066,9 @@ if __name__ == "__main__": ) json_model = model.to_json() with open(args.export_energymodel, "w") as f: - json.dump(json_model, f, indent=2, sort_keys=True, cls=NpEncoder) + json.dump( + json_model, f, indent=2, sort_keys=True, cls=dfatool.utils.NpEncoder + ) if args.export_dot: dfatool.cli.export_dot(model, args.export_dot) |