Move codependent parameter logic to ParamStats / parameters.py

1 files changed, 2 insertions, 46 deletions

diff --git a/lib/dfatool.py b/lib/dfatool.py
index 3151c65..cc07026 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -16,7 +16,8 @@ from automata import PTA
 from functions import analytic
 from functions import AnalyticFunction
 from parameters import ParamStats
-from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, remove_index_from_tuple, is_power_of_two
+from utils import vprint, is_numeric, soft_cast_int, param_slice_eq, remove_index_from_tuple
+from utils import by_name_to_by_param
 
 arg_support_enabled = True
 
@@ -180,26 +181,6 @@ class KeysightCSV:
                 currents[i] = float(row[2]) * -1
         return timestamps, currents
 
-def by_name_to_by_param(by_name: dict):
-    """
-    Convert aggregation by name to aggregation by name and parameter values.
-    """
-    by_param = dict()
-    for name in by_name.keys():
-        for i, parameters in enumerate(by_name[name]['param']):
-            param_key = (name, tuple(parameters))
-            if param_key not in by_param:
-                by_param[param_key] = dict()
-                for key in by_name[name].keys():
-                    by_param[param_key][key] = list()
-                by_param[param_key]['attributes'] = by_name[name]['attributes']
-                # special case for PTA models
-                if 'isa' in by_name[name]:
-                    by_param[param_key]['isa'] = by_name[name]['isa']
-            for attribute in by_name[name]['attributes']:
-                by_param[param_key][attribute].append(by_name[name][attribute][i])
-    return by_param
-
 
 def _xv_partitions_kfold(length, num_slices):
     pairs = []
@@ -1403,31 +1384,6 @@ def _add_trace_data_to_aggregate(aggregate, key, element):
     for datakey, dataval in element['offline_aggregates'].items():
         aggregate[key][datakey].extend(dataval)
 
-def filter_aggregate_by_param(aggregate, parameters, parameter_filter):
-    """
-    Remove entries which do not have certain parameter values from `aggregate`.
-
-    :param aggregate: aggregated measurement data, must be a dict conforming to
-        aggregate[state or transition name]['param'] = (first parameter value, second parameter value, ...)
-        and
-        aggregate[state or transition name]['attributes'] = [list of keys with measurement data, e.g. 'power' or 'duration']
-    :param parameters: list of parameters, used to map parameter index to parameter name. parameters=['foo', ...] means 'foo' is the first parameter
-    :param parameter_filter: [[name, value], [name, value], ...] list of parameter values to keep, all others are removed. Values refer to normalizad parameter data.
-    """
-    for param_name_and_value in parameter_filter:
-        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 aggregate.keys():
-            indices_to_keep = list(map(lambda x: x[param_index] == param_value, aggregate[name]['param']))
-            aggregate[name]['param'] = list(map(lambda iv: iv[1], filter(lambda iv: indices_to_keep[iv[0]], enumerate(aggregate[name]['param']))))
-            for attribute in aggregate[name]['attributes']:
-                aggregate[name][attribute] = aggregate[name][attribute][indices_to_keep]
-                if len(aggregate[name][attribute]) == 0:
-                    names_to_remove.add(name)
-        for name in names_to_remove:
-            aggregate.pop(name)
-
 
 def pta_trace_to_aggregate(traces, ignore_trace_indexes = []):
     u"""