defined with the same number of values when using such iter_type).
'''
-import os
-import multiprocessing
+from __future__ import absolute_import
+
+import itertools
import logging
-import traceback
+import multiprocessing
+import os
import time
-import itertools
+import traceback
+
+import six
+from six.moves import range
from yardstick.benchmark.runners import base
return -1 if start > stop else 1
param_iters = \
- [xrange(d['start'], d['stop'] + margin(d['start'], d['stop']),
- d['step']) for d in runner_cfg['iterators']]
+ [range(d['start'], d['stop'] + margin(d['start'], d['stop']),
+ d['step']) for d in runner_cfg['iterators']]
param_names = [d['name'] for d in runner_cfg['iterators']]
iter_type = runner_cfg.get("iter_type", "nested_for_loops")
loop_iter = itertools.product(*param_iters)
elif iter_type == 'tuple_loops':
# Combine each i;th index of respective parameter list
- loop_iter = itertools.izip(*param_iters)
+ loop_iter = six.moves.zip(*param_iters)
else:
LOG.warning("iter_type unrecognized: %s", iter_type)
- raise
+ raise TypeError("iter_type unrecognized: %s", iter_type)
# Populate options and run the requested method for each value combination
for comb_values in loop_iter: