optionally prune dependent parameters before analysis

1 files changed, 3 insertions, 0 deletions

diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py
index 465932b..1c27533 100755
--- a/bin/analyze-timing.py
+++ b/bin/analyze-timing.py
@@ -78,6 +78,7 @@ 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
+import utils
 
 opts = {}
 
@@ -205,6 +206,8 @@ if __name__ == '__main__':
     preprocessed_data = raw_data.get_preprocessed_data()
     by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
 
+    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])