def extract_usage(db_obj,
trace_id_rows,
fill_none=True,
factor=1.0,
mean=False):
"""Takes a list of lists of trace_is and produces a list of lists of results
corresponding to them.
Args:
- db_obj: DBManager object connted to a db where the results will be pulled
from.
"""
exp_rows = []
my = ResultTrace()
res_type = "usage"
if mean:
res_type = "usage_mean"
for row in trace_id_rows:
new_row = []
exp_rows.append(new_row)
for trace_id in row:
exp = ExperimentDefinition()
exp.load(db_obj, trace_id)
result = my._get_utilization_result()
if exp.is_analysis_done():
result.load(db_obj, trace_id, res_type)
else:
result._set("utilization", 0)
result._set("waste", 0)
result._set("corrected_utilization", 0)
result.apply_factor(factor)
new_row.append(result)
return exp_rows
def extract_results(db_obj,
trace_id_rows_colors,
result_type,
factor=None,
fill_none=True,
second_pass=False):
"""Takes a list of lists of trace_is and produces a list of lists of results
corresponding to them.
Args:
- db_obj: DBManager object connted to a db where the results will be pulled
from.
- trace_id_rows_colors: list of lists of integers as trace_ids of experiments.
- result_type: string indentifying which type of result are we pulling. It
correspond to the type of the NumericStats stored in db_obj.
Returns: a list of lists of
same dimension of trace_id_rows_colors, each element a NumericStats object
corresponding to the result of that component.
"""
exp_rows = []
for row in trace_id_rows_colors:
new_row = []
exp_rows.append(new_row)
for trace_id in row:
exp = ExperimentDefinition()
exp.load(db_obj, trace_id)
if exp.is_analysis_done(second_pass=second_pass):
key = result_type + "_stats"
result = NumericStats()
result.load(db_obj, trace_id, key)
if factor:
result.apply_factor(factor)
else:
result = NumericStats()
result.calculate([0, 0, 0])
if fill_none and result._get("median") is None:
result = NumericStats()
result.calculate([0, 0, 0])
new_row.append(result)
return exp_rows
