def test_big_nid_map_scalable(self):
"""check big nid mapping is scalable."""
model = Model()
model.parse("nid_map: nodes=foo[1-9999] nids=bar[1-9999]@tcp")
before = time.time()
NidMap.fromlist(model.get("nid_map"))
self.assertTrue(time.time() - before < 0.5)
def test_big_nid_map_scalable(self):
"""check big nid mapping is scalable."""
model = Model()
model.parse("nid_map: nodes=foo[1-9999] nids=bar[1-9999]@tcp")
before = time.time()
NidMap.fromlist(model.get('nid_map'))
self.assertTrue(time.time() - before < .5)
def testFromList(self):
"""construct from a list of modelnidmap"""
line1 = ModelNidMap()
line1.parse("nodes=foo[1-2] nids=foo[1-2]-ib0@o2ib4")
line2 = ModelNidMap()
line2.parse("nodes=bar[1-2] nids=bar[1-2]-ib0@o2ib4")
nm = NidMap.fromlist([line1, line2])
self.assertEqual(nm["foo1"], ["foo1-ib0@o2ib4"])
self.assertEqual(nm["foo2"], ["foo2-ib0@o2ib4"])
self.assertEqual(nm["bar1"], ["bar1-ib0@o2ib4"])
self.assertEqual(nm["bar2"], ["bar2-ib0@o2ib4"])
def testFromList(self):
"""construct from a list of modelnidmap"""
line1 = ModelNidMap()
line1.parse("nodes=foo[1-2] nids=foo[1-2]-ib0@o2ib4")
line2 = ModelNidMap()
line2.parse("nodes=bar[1-2] nids=bar[1-2]-ib0@o2ib4")
nm = NidMap.fromlist([line1, line2])
self.assertEqual(nm["foo1"], ["foo1-ib0@o2ib4"])
self.assertEqual(nm["foo2"], ["foo2-ib0@o2ib4"])
self.assertEqual(nm["bar1"], ["bar1-ib0@o2ib4"])
self.assertEqual(nm["bar2"], ["bar2-ib0@o2ib4"])
def __init__(self, filename):
self.backend = None
self.xmf_path = None
self.model = Model()
try:
self.model.load(filename)
except IOError:
raise ModelFileIOError("Could not read %s" % filename)
# Set nodes to nids mapping using the NidMap helper class
self.nid_map = NidMap.fromlist(self.get('nid_map'))
# Initialize the tuning model to None if no special tuning configuration
# is provided
self.tuning_model = TuningModel()
def __init__(self, filename):
self.backend = None
self.xmf_path = None
self.model = Model()
try:
self.model.load(filename)
except IOError:
raise ModelFileIOError("Could not read %s" % filename)
# Set nodes to nids mapping using the NidMap helper class
self.nid_map = NidMap.fromlist(self.get('nid_map'))
# Initialize the tuning model to None if no special tuning configuration
# is provided
self.tuning_model = TuningModel()
