diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/analyze-archive.py | 17 | ||||
| -rwxr-xr-x | bin/analyze-timing.py | 16 |
2 files changed, 18 insertions, 15 deletions
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py index e04f3fe..a4af02a 100755 --- a/bin/analyze-archive.py +++ b/bin/analyze-archive.py @@ -61,6 +61,12 @@ Options: also defined for cases such as safe_inv(0) or safe_sqrt(-1). This allows a greater range of functions to be tried during fitting. +--filter-param=<parameter name>=<parameter value>[,<parameter name>=<parameter value>...] + Only consider measurements where <parameter name> is <parameter value> + All other measurements (including those where it is None, that is, has + not been set yet) are discarded. Note that this may remove entire + function calls from the model. + --hwmodel=<hwmodel.json> Load DFA hardware model from JSON @@ -73,7 +79,7 @@ import json import plotter import re import sys -from dfatool import PTAModel, RawData, pta_trace_to_aggregate +from dfatool import PTAModel, RawData, pta_trace_to_aggregate, filter_aggregate_by_param from dfatool import soft_cast_int, is_numeric, gplearn_to_function from dfatool import CrossValidator @@ -210,6 +216,7 @@ if __name__ == '__main__': optspec = ( 'plot-unparam= plot-param= show-models= show-quality= ' 'ignored-trace-indexes= discard-outliers= function-override= ' + 'filter-param= ' 'cross-validate= ' 'with-safe-functions hwmodel= export-energymodel=' ) @@ -242,6 +249,11 @@ if __name__ == '__main__': xv_method, xv_count = opts['cross-validate'].split(':') xv_count = int(xv_count) + if 'filter-param' in opts: + opts['filter-param'] = list(map(lambda x: x.split('='), opts['filter-param'].split(','))) + else: + opts['filter-param'] = list() + if 'with-safe-functions' in opts: safe_functions_enabled = True @@ -257,6 +269,9 @@ if __name__ == '__main__': preprocessed_data = raw_data.get_preprocessed_data() by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes) + + filter_aggregate_by_param(by_name, parameters, opts['filter-param']) + model = PTAModel(by_name, parameters, arg_count, traces = preprocessed_data, discard_outliers = discard_outliers, diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py index 39a915f..eecb70e 100755 --- a/bin/analyze-timing.py +++ b/bin/analyze-timing.py @@ -77,7 +77,7 @@ import re import sys from dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate from dfatool import soft_cast_int, is_numeric, gplearn_to_function -from dfatool import CrossValidator +from dfatool import CrossValidator, filter_aggregate_by_param import utils opts = {} @@ -208,19 +208,7 @@ if __name__ == '__main__': utils.prune_dependent_parameters(by_name, parameters) - for param_name_and_value in opts['filter-param']: - param_index = parameters.index(param_name_and_value[0]) - param_value = soft_cast_int(param_name_and_value[1]) - names_to_remove = set() - for name in by_name.keys(): - indices_to_keep = list(map(lambda x: x[param_index] == param_value, by_name[name]['param'])) - by_name[name]['param'] = list(map(lambda iv: iv[1], filter(lambda iv: indices_to_keep[iv[0]], enumerate(by_name[name]['param'])))) - for attribute in by_name[name]['attributes']: - by_name[name][attribute] = by_name[name][attribute][indices_to_keep] - if len(by_name[name][attribute]) == 0: - names_to_remove.add(name) - for name in names_to_remove: - by_name.pop(name) + filter_aggregate_by_param(by_name, parameters, opts['filter-param']) model = AnalyticModel(by_name, parameters, arg_count, use_corrcoef = opts['corrcoef'], function_override = function_override) |