def test_materials():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 0.04),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 1.94),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 0.01),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Mass', '<f8')]))
)
res = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 9, 7, 'Material', 1, 2, 'kg', 5, 0, 0),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('Type', 'O'), ('TimeCreated', '<i8'), ('Quantity', '<i8'),
('Units', 'O'), ('QualId', '<i8'), ('Parent1', '<i8'),
('Parent2', '<i8')]))
)
comps = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 922350000, 0.02),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 922380000, 0.97),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 942390000, 0.005),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('NucId', '<i8'),
('MassFrac', '<f8')]))
)
s1 = res.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated'])['Quantity']
s2 = comps.set_index(['SimId', 'QualId', 'NucId'])['MassFrac']
series = [s1,s2]
obs = metrics.materials.func(series)
assert_frame_equal(exp, obs)
def test_explicit_inventory_by_nuc():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'core', 922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'inventory', 922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'inventory', 922380000, 2.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 'core', 922350000, 4.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 'core', 922380000, 2.0)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('Time', '<i8'), ('InventoryName', 'O'),
('NucId', '<i8'), ('Quantity', '<f8')]))
)
inv = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 'core', 922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'core', 922350000, 2.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'core', 922380000, 2.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 'inventory', 922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 'inventory', 922380000, 2.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 2, 'core', 922350000, 2.0),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')]))
)
series = [inv.set_index(['SimId', 'Time', 'InventoryName', 'NucId'])['Quantity']]
obs = metrics.explicit_inventory_by_nuc.func(series)
assert_frame_equal(exp, obs)
def test_fuel_cost():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 1, 'uox',
29641.600000000002, 46),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 11, 3, 'mox', 0, 9)
], dtype=ensure_dt_bytes([
('SimId','O'), ('TransactionId', '<i8'), ('ReceiverId','<i8'),
('Commodity', 'O'), ('Cost', '<f8'), ('Time', '<i8')]))
)
resources = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 12.56),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 5.5),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('ResourceId', '<i8'), ('Quantity',
'<f8'),]))
)
transactions = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 1, 27,
'uox', 46),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 11, 3, 13,
'mox', 9)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'),
('ReceiverId', '<i8'), ('ResourceId', '<i8'),
('Commodity', 'O'), ('Time', '<i8')]))
)
s1 = resources.set_index(['SimId', 'ResourceId'])['Quantity']
s2 = transactions.set_index(['SimId', 'TransactionId', 'ReceiverId',
'ResourceId', 'Commodity'])['Time']
series = [s1, s2]
obs = eco_metrics.fuel_cost.func(series)
assert_frame_equal(exp, obs)
def test_decommission_series():
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 10, 'LWR', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 'LWR', 2),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('ExitTime', '<i8'),
('Prototype', 'O'),
('Count', '<i8')])))
agent_entry = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 4, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 'FRx'),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Prototype', 'O')])))
agent_exit = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 10),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 20),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 20),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('ExitTime', '<i8')])))
obs = metrics.decommission_series.func(agent_entry, agent_exit)
assert_frame_equal(exp, obs)
def test_timeseries_inventories(db, fname, backend):
evaler = cym.Evaluator(db)
cal = ts.inventories(evaler)
exp_head = ['Time', 'Quantity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
cal = ts.inventories(evaler, facilities=['Reactor1'],
nucs=['94239'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 0.0444814879803),
(2, 0.0621806030246),
(3, 0.0798797180688),
(4, 0.0530973451327)
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Quantity', '<f8')
]))
)
assert_frame_equal(cal, refs)
cal = ts.inventories(evaler, facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 0.0460607124057),
(2, 0.0726093849721),
(3, 0.0991580575384),
(4, 0.119646017699)
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Quantity', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_timeseries_inventories_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.inventories_decayheat(evaler)
exp_head = ['Time', 'DecayHeat']
assert_equal(list(cal), exp_head) # Check we have the correct headers
cal = ts.inventories_decayheat(evaler,
facilities=['Reactor1'],
nucs=['94239'])
refs = pd.DataFrame(
np.array([(0, 0.0), (1, 7.98590335085e+32), (2, 1.11634819022e+33),
(3, 1.43410604536e+33), (4, 9.53273565408e+32)],
dtype=ensure_dt_bytes([('Time', '<i8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
cal = ts.inventories_decayheat(evaler,
facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(
np.array([(0, 0.0), (1, 7.98591200694e+32), (2, 1.11635390648e+33),
(3, 1.43411661226e+33), (4, 9.53310042276e+32)],
dtype=ensure_dt_bytes([('Time', '<i8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
def test_timeseries_transactions(db, fname, backend):
evaler = cym.Evaluator(db)
cal = ts.transactions(evaler)
exp_head = ['Time', 'Mass']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
# test single nuclide selection
cal = ts.transactions(evaler, nucs=['942390000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 0.0444814879803),
(2, 0.0889629759607),
(3, 0.0889629759607),
(4, 0.0889629759607),
],
dtype=ensure_dt_bytes([('Time', '<i8'), ('Mass', '<f8')])))
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions(evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 0.831724864011),
(2, 1.66344972802),
(3, 2.62344972802),
(4, 2.62344972802),
],
dtype=ensure_dt_bytes([('Time', '<i8'), ('Mass', '<f8')])))
assert_frame_equal(cal, refs)
def test_timeseries_inventories_activity(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.inventories_activity(evaler)
exp_head = ['Time', 'Activity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
cal = ts.inventories_activity(evaler,
facilities=['Reactor1'],
nucs=['94239'])
refs = pd.DataFrame(
np.array([(0, 0.0), (1, 2.44036364223e+13), (2, 3.41138054869e+13),
(3, 4.38239745515e+13), (4, 2.91305071939e+13)],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Activity', '<f8')])))
assert_frame_equal(cal, refs)
cal = ts.inventories_activity(evaler,
facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(
np.array([
(0, 0.0),
(1, 2.4403666094e+13),
(2, 3.41140014315e+13),
(3, 4.3824336769e+13),
(4, 2.91317575657e+13),
],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Activity', '<f8')])))
assert_frame_equal(cal, refs)
def test_timeseries_inventories(db, fname, backend):
evaler = cym.Evaluator(db)
cal = ts.inventories(evaler)
exp_head = ['Time', 'Quantity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
cal = ts.inventories(evaler, facilities=['Reactor1'], nucs=['94239'])
refs = pd.DataFrame(
np.array([(0, 0.0), (1, 0.0444814879803), (2, 0.0621806030246),
(3, 0.0798797180688), (4, 0.0530973451327)],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Quantity', '<f8')])))
assert_frame_equal(cal, refs)
cal = ts.inventories(evaler,
facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(
np.array([(0, 0.0), (1, 0.0460607124057), (2, 0.0726093849721),
(3, 0.0991580575384), (4, 0.119646017699)],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Quantity', '<f8')])))
assert_frame_equal(cal, refs)
def test_timeseries_transactions_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.transactions_decayheat(evaler)
exp_head = ['Time', 'DecayHeat']
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = ts.transactions_decayheat(evaler, nucs=['942390000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 3.34065303191e+30),
(2, 6.68130606382e+30),
(3, 6.68130606382e+30),
(4, 6.68130606382e+30),
],
dtype=ensure_dt_bytes([('Time', '<i8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions_decayheat(evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 3.34091395721e+30),
(2, 6.68182791443e+30),
(3, 6.68214609848e+30),
(4, 6.68214609848e+30),
],
dtype=ensure_dt_bytes([('Time', '<i8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
def test_timeseries_transactions_activity(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.transactions_activity(evaler)
exp_head = ['Time', 'Activity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = ts.transactions_activity(evaler, nucs=['942390000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 102084984531.0),
(2, 204169969062.0),
(3, 204169969062.0),
(4, 204169969062.0),
],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Activity', '<f8')])))
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions_activity(evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(
np.array([
(0, 0.000000000),
(1, 102094774891.0),
(2, 204189549782.0),
(3, 204201488588.0),
(4, 204201488588.0),
],
dtype=ensure_dt_bytes([('Time', '<i8'),
('Activity', '<f8')])))
assert_frame_equal(cal, refs)
def test_build_series():
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), -1, 'FRx', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), -1, 'LWR', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'FRx', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'LWR', 2),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 'FRx', 2),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('EnterTime', '<i8'),
('Prototype', 'O'),
('Count', '<i8')])))
agent_entry = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'FRx', -1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'LWR', -1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'LWR', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'FRx', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'LWR', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'FRx', 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 'FRx', 5),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('Prototype', 'O'),
('EnterTime', '<i8')])))
obs = metrics.build_series.func(agent_entry)
assert_frame_equal(exp, obs)
def test_decayheat():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 2.3944723480343003e-12),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 1.6505536389997207e-11),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 1.92784802432412e-08),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('DecayHeat', '<f8')]))
)
act = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 22949993169.28023),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Activity', '<f8')]))
)
series = [act.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', \
'NucId'])['Activity']]
obs = metrics.decay_heat.func(series)
assert_frame_equal(exp, obs)
def test_decayheat():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922350000, 9.3280119931e+25),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922380000, 6.42996948889e+26),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
942390000, 7.51020971553e+29),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'),
('NucId', '<i8'), ('DecayHeat', '<f8')
])))
act = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
942390000, 22949993169.28023),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'),
('NucId', '<i8'), ('Activity', '<f8')
])))
obs = metrics.decay_heat.func(act)
assert_frame_equal(exp, obs)
def test_fco_swu():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([(0, 0.002407), (1, 0.001473)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('SWU', '<f8')]))
)
mats = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 8.328354),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 325.004979),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 322.228861),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922380000, 322.228861),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'),
('Mass', '<f8')]))
)
trans = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'LWR Fuel'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'),
('Commodity', 'O')]))
)
s1 = mats.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', 'NucId'])['Mass']
s2 = trans.set_index(['SimId', 'TransactionId', 'ResourceId'])['Commodity']
series = [s1,s2]
obs = fco_metrics.fco_swu.func(series)
np.allclose(exp, obs)
def test_timeseries_inventories_activity(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.inventories_activity(evaler)
exp_head = ['Time', 'Activity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
cal = ts.inventories_activity(evaler, facilities=['Reactor1'],
nucs=['94239'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 2.44036364223e+13),
(2, 3.41138054869e+13),
(3, 4.38239745515e+13),
(4, 2.91305071939e+13)
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Activity', '<f8')
]))
)
assert_frame_equal(cal, refs)
cal = ts.inventories_activity(evaler, facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 2.4403666094e+13),
(2, 3.41140014315e+13),
(3, 4.3824336769e+13),
(4, 2.91317575657e+13),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Activity', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_materials():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 'kg', 0.04),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 'kg', 1.94),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 'kg', 0.01),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Units', 'O'), ('Mass', '<f8')]))
)
res = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 9, 7, 'Material', 1, 2, 'kg', 5, 0, 0),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('Type', 'O'), ('TimeCreated', '<i8'), ('Quantity', '<i8'),
('Units', 'O'), ('QualId', '<i8'), ('Parent1', '<i8'),
('Parent2', '<i8')]))
)
comps = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 922350000, 0.02),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 922380000, 0.97),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 942390000, 0.005),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('NucId', '<i8'),
('MassFrac', '<f8')]))
)
s1 = res.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', 'Units'])['Quantity']
s2 = comps.set_index(['SimId', 'QualId', 'NucId'])['MassFrac']
series = [s1,s2]
obs = metrics.materials.func(series)
assert_frame_equal(exp, obs)
def test_fco_fuel_loading():
exp = pd.DataFrame(np.array([(0, 0.666666), (1, 0.333333)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('FuelLoading', '<f8')]))
)
mats = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 8.328354),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 325.004979),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 322.228861),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922380000, 322.228861),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'),
('Mass', '<f8')]))
)
trans = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'FR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'FR Fuel'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'),
('Commodity', 'O')]))
)
s1 = mats.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', 'NucId'])['Mass']
s2 = trans.set_index(['SimId', 'TransactionId', 'ResourceId'])['Commodity']
series = [s1,s2]
obs = fco_metrics.fco_fuel_loading.func(series)
assert_frame_equal(exp, obs)
def test_fco_swu():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([(0, 0.002407), (1, 0.001473)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('SWU', '<f8')]))
)
mats = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 8.328354),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 325.004979),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 322.228861),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922380000, 322.228861),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'),
('Mass', '<f8')]))
)
trans = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'LWR Fuel'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'),
('Commodity', 'O')]))
)
obs = fco_metrics.fco_swu.func(mats, trans)
np.allclose(exp, obs)
def test_timeseries_inventories_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.inventories_decayheat(evaler)
exp_head = ['Time', 'DecayHeat']
assert_equal(list(cal), exp_head) # Check we have the correct headers
cal = ts.inventories_decayheat(evaler, facilities=['Reactor1'],
nucs=['94239'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 7.98590335085e+32),
(2, 1.11634819022e+33),
(3, 1.43410604536e+33),
(4, 9.53273565408e+32)
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('DecayHeat', '<f8')
]))
)
assert_frame_equal(cal, refs)
cal = ts.inventories_decayheat(evaler, facilities=['Reactor1'],
nucs=['94239', '92235'])
refs = pd.DataFrame(np.array([
(0, 0.0),
(1, 7.98591200694e+32),
(2, 1.11635390648e+33),
(3, 1.43411661226e+33),
(4, 9.53310042276e+32)
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('DecayHeat', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_decommission_series():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 10, 'LWR', 1),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 'LWR', 2),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('ExitTime', '<i8'), ('Prototype', 'O'),
('Count', '<i8')]))
)
agent_entry = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 4, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 'LWR'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 'FRx'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 'FRx'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Prototype', 'O')]))
)
agent_exit = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 10),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 20),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 20),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('ExitTime', '<i8')]))
)
obs = metrics.decommission_series.func(agent_entry, agent_exit)
assert_frame_equal(exp, obs)
def test_activity():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
942390000, 22949993169.28023),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'),
('NucId', '<i8'), ('Activity', '<f8')
])))
mass = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922350000, 0.04),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
922380000, 1.94),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1,
942390000, 0.01),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('QualId', '<i8'),
('ResourceId', '<i8'), ('ObjId',
'<i8'),
('TimeCreated', '<i8'),
('NucId', '<i8'), ('Mass', '<f8')])))
obs = metrics.activity.func(mass)
assert_frame_equal(exp, obs)
def test_fco_fuel_loading():
exp = pd.DataFrame(np.array([(0, 0.666666), (1, 0.333333)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('FuelLoading', '<f8')]))
)
mats = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 8.328354),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 325.004979),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 322.228861),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922380000, 322.228861),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'),
('Mass', '<f8')]))
)
trans = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'FR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'FR Fuel'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'),
('Commodity', 'O')]))
)
s1 = mats.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', 'NucId'])['Mass']
s2 = trans.set_index(['SimId', 'TransactionId', 'ResourceId'])['Commodity']
series = [s1,s2]
obs = metrics.fco_fuel_loading.func(series)
assert_frame_equal(exp, obs)
def test_decayheat():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 2.3944723480343003e-12),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 1.6505536389997207e-11),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 1.92784802432412e-08),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('DecayHeat', '<f8')]))
)
act = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 22949993169.28023),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Activity', '<f8')]))
)
series = [act.set_index(['SimId', 'QualId', 'ResourceId', 'ObjId', 'TimeCreated', \
'NucId'])['Activity']]
obs = metrics.decay_heat.func(series)
assert_frame_equal(exp, obs)
def test_transaction_quantity():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 3, 3, 10, 20, 'LWR Fuel', 'kg', 410),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 4, 3, 20, 30, 'FR Fuel', 'kg', 305),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 5, 12, 30, 40, 'Spent Fuel', 'kg', 9),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('SenderId', '<i8'),
('ReceiverId', '<i8'), ('Commodity', 'O'), ('Units', 'O'),
('Quantity', '<f8')]))
)
mats = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922350000, 'kg', 10),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3, 922380000, 'kg', 400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 942390000, 'kg', 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3, 922380000, 'kg', 300),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 942390000, 'kg', 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12, 922360000, 'kg', 4),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'),
('ObjId', '<i8'), ('TimeCreated', '<i8'), ('NucId', '<i8'),
('Units', 'O'), ('Mass', '<f8')]))
)
trans = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 10, 20, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 20, 30, 8, 'FR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 30, 40, 9, 'Spent Fuel'),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'), ('SenderId', '<i8'),
('ReceiverId', '<i8'), ('ResourceId', '<i8'), ('Commodity', 'O')]))
)
obs = metrics.transaction_quantity.func(mats, trans)
assert_frame_equal(exp, obs)
def test_materials():
exp = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922350000, "kg", 0.04),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922380000, "kg", 1.94),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 942390000, "kg", 0.01),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Units", "O"),
("Mass", "<f8"),
]
),
)
)
res = pd.DataFrame(
np.array(
[(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 9, 7, "Material", 1, 2, "kg", 5, 0, 0)],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("Type", "O"),
("TimeCreated", "<i8"),
("Quantity", "<i8"),
("Units", "O"),
("QualId", "<i8"),
("Parent1", "<i8"),
("Parent2", "<i8"),
]
),
)
)
comps = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 922350000, 0.02),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 922380000, 0.97),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 942390000, 0.005),
],
dtype=ensure_dt_bytes([("SimId", "O"), ("QualId", "<i8"), ("NucId", "<i8"), ("MassFrac", "<f8")]),
)
)
s1 = res.set_index(["SimId", "QualId", "ResourceId", "ObjId", "TimeCreated", "Units"])["Quantity"]
s2 = comps.set_index(["SimId", "QualId", "NucId"])["MassFrac"]
series = [s1, s2]
obs = metrics.materials.func(series)
assert_frame_equal(exp, obs)
def test_agents():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 22, 'Region', ':agents:NullRegion', 'USA', -1, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 23, 'Inst', ':agents:NullInst', 'utility', 22, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 24, 'Facility', ':agents:Source', 'MineU235', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 25, 'Facility', ':agents:Source', 'U238', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 26, 'Facility', ':agents:Source', 'DU', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 'Facility', ':agents:Source', 'DU2', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 28, 'Facility', ':Brightlite:FuelfabFacility', 'LWR Fuel FAb', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 29, 'Facility', ':Brightlite:ReprocessFacility', 'LWR Seperation', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 30, 'Facility', ':Brightlite:ReprocessFacility', 'FR Reprocess', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 31, 'Facility', ':agents:Sink', 'SINK', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 32, 'Facility', ':Brightlite:FuelfabFacility', 'FR Fuel Fab', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 33, 'Inst', ':cycamore:DeployInst', 'utility2', 22, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 35, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 36, 'Facility', ':Brightlite:ReactorFacility', 'FRx', 33, -1, 10, 120.0),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'),
('Kind', 'O'), ('Spec', 'O'), ('Prototype', 'O'),
('ParentId', '<i8'), ('Lifetime', '<i8'),
('EnterTime', '<i8'), ('ExitTime', '<f8')]))
)
agent_entry = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 22, 'Region', ':agents:NullRegion', 'USA', -1, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 23, 'Inst', ':agents:NullInst', 'utility', 22, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 24, 'Facility', ':agents:Source', 'MineU235', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 25, 'Facility', ':agents:Source', 'U238', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 26, 'Facility', ':agents:Source', 'DU', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 'Facility', ':agents:Source', 'DU2', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 28, 'Facility', ':Brightlite:FuelfabFacility', 'LWR Fuel FAb', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 29, 'Facility', ':Brightlite:ReprocessFacility', 'LWR Seperation', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 30, 'Facility', ':Brightlite:ReprocessFacility', 'FR Reprocess', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 31, 'Facility', ':agents:Sink', 'SINK', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 32, 'Facility', ':Brightlite:FuelfabFacility', 'FR Fuel Fab', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 33, 'Inst', ':cycamore:DeployInst', 'utility2', 22, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 35, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 36, 'Facility', ':Brightlite:ReactorFacility', 'FRx', 33, -1, 10),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'),
('Kind', 'O'), ('Spec', 'O'), ('Prototype', 'O'),
('ParentId', '<i8'), ('Lifetime', '<i8'),
('EnterTime', '<i8')]))
)
info = pd.DataFrame({'Duration': {0: 120},
'SimId': {0: UUID('f22f2281-2464-420a-8325-37320fd418f8')},
})
dur = info.set_index(['SimId'])
series = [raw_to_series(agent_entry, ['SimId', 'AgentId'], col) \
for col in ('Kind', 'Spec', 'Prototype', 'ParentId',
'Lifetime', 'EnterTime')]
series += [None, None, dur]
obs = metrics.agents.func(series)
assert_frame_equal(exp, obs)
def test_transaction_quantity():
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 3, 3, 10, 20,
'LWR Fuel', 'kg', 410),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 4, 3, 20, 30,
'FR Fuel', 'kg', 305),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 5, 12, 30, 40,
'Spent Fuel', 'kg', 9),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('TransactionId', '<i8'),
('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('SenderId', '<i8'),
('ReceiverId', '<i8'), ('Commodity', 'O'), ('Units', 'O'),
('Quantity', '<f8')
])))
mats = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3,
922350000, 'kg', 10),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3,
922380000, 'kg', 400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3,
942390000, 'kg', 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3,
922380000, 'kg', 300),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12,
942390000, 'kg', 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12,
922360000, 'kg', 4),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('QualId', '<i8'),
('ResourceId', '<i8'), ('ObjId',
'<i8'),
('TimeCreated', '<i8'),
('NucId', '<i8'), ('Units', 'O'),
('Mass', '<f8')])))
trans = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 10, 20, 7,
'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 20, 30, 8,
'FR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 30, 40, 9,
'Spent Fuel'),
],
dtype=ensure_dt_bytes([('SimId', 'O'),
('TransactionId', '<i8'),
('SenderId', '<i8'),
('ReceiverId', '<i8'),
('ResourceId', '<i8'),
('Commodity', 'O')])))
obs = metrics.transaction_quantity.func(mats, trans)
assert_frame_equal(exp, obs)
def test_agents():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 22, 'Region', ':agents:NullRegion', 'USA', -1, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 23, 'Inst', ':agents:NullInst', 'utility', 22, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 24, 'Facility', ':agents:Source', 'MineU235', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 25, 'Facility', ':agents:Source', 'U238', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 26, 'Facility', ':agents:Source', 'DU', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 'Facility', ':agents:Source', 'DU2', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 28, 'Facility', ':Brightlite:FuelfabFacility', 'LWR Fuel FAb', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 29, 'Facility', ':Brightlite:ReprocessFacility', 'LWR Seperation', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 30, 'Facility', ':Brightlite:ReprocessFacility', 'FR Reprocess', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 31, 'Facility', ':agents:Sink', 'SINK', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 32, 'Facility', ':Brightlite:FuelfabFacility', 'FR Fuel Fab', 23, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 33, 'Inst', ':cycamore:DeployInst', 'utility2', 22, -1, 0, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 35, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5, 120.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 36, 'Facility', ':Brightlite:ReactorFacility', 'FRx', 33, -1, 10, 120.0),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'),
('Kind', 'O'), ('Spec', 'O'), ('Prototype', 'O'),
('ParentId', '<i8'), ('Lifetime', '<i8'),
('EnterTime', '<i8'), ('ExitTime', '<f8')]))
)
agent_entry = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 22, 'Region', ':agents:NullRegion', 'USA', -1, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 23, 'Inst', ':agents:NullInst', 'utility', 22, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 24, 'Facility', ':agents:Source', 'MineU235', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 25, 'Facility', ':agents:Source', 'U238', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 26, 'Facility', ':agents:Source', 'DU', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 'Facility', ':agents:Source', 'DU2', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 28, 'Facility', ':Brightlite:FuelfabFacility', 'LWR Fuel FAb', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 29, 'Facility', ':Brightlite:ReprocessFacility', 'LWR Seperation', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 30, 'Facility', ':Brightlite:ReprocessFacility', 'FR Reprocess', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 31, 'Facility', ':agents:Sink', 'SINK', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 32, 'Facility', ':Brightlite:FuelfabFacility', 'FR Fuel Fab', 23, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 33, 'Inst', ':cycamore:DeployInst', 'utility2', 22, -1, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 34, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 35, 'Facility', ':Brightlite:ReactorFacility', 'LWR', 33, -1, 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 36, 'Facility', ':Brightlite:ReactorFacility', 'FRx', 33, -1, 10),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'),
('Kind', 'O'), ('Spec', 'O'), ('Prototype', 'O'),
('ParentId', '<i8'), ('Lifetime', '<i8'),
('EnterTime', '<i8')]))
)
info = pd.DataFrame({'Duration': {0: 120},
'SimId': {0: UUID('f22f2281-2464-420a-8325-37320fd418f8')},
})
dur = info.set_index(['SimId'])
series = [raw_to_series(agent_entry, ['SimId', 'AgentId'], col) \
for col in ('Kind', 'Spec', 'Prototype', 'ParentId',
'Lifetime', 'EnterTime')]
series += [None, None, dur]
obs = metrics.agents.func(series)
assert_frame_equal(exp, obs)
def test_decommissioning_cost():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 0, 19),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/49, 20),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/49*2, 21),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*3/7, 22),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*4/7, 23),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*5/7, 24),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*6/7, 25),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7, 26),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*6/7, 27),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*5/7, 28),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*4/7, 29),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/7*3/7, 30),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/49*2, 31),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 7500/49, 32),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 0, 33)
], dtype=ensure_dt_bytes([
('SimId','O'), ('AgentId', '<i8'), ('DecomPayment','<f8'),
('Time', '<i8')]))
)
decom = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 234),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 450)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('DecomTime',
'<i8'),]))
)
power = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 10.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27, 11.3)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Value',
'<f8')]))
)
entry = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 27,
':cycamore:Reactor'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13,
':cycamore:Reactor'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5,
':cycamore:Sink')
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'),
('Spec', 'O')]))
)
s1 = decom.set_index(['SimId', 'AgentId'])['DecomTime']
s2 = power.set_index(['SimId', 'AgentId'])['Value']
s3 = entry.set_index(['SimId', 'AgentId'])['Spec']
series = [s1, s2, s3]
obs = eco_metrics.decommissioning_cost.func(series)
assert_frame_equal(exp, obs)
def test_capital_cost():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 9, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 10, 343.75),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 11, 687.5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 12, 1031.25),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 13, 1375.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 14, 1718.75),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 15, 2062.5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 16, 2406.25),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 17, 2750.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 18, 1375.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 19, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -8, 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -7, 750.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -6, 1500.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -5, 2250.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -4, 3000.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -3, 3750.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -2, 4500.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, -1, 5250.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 0, 6000.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 1, 3000.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 2, 0)
], dtype=ensure_dt_bytes([
('SimId','O'), ('AgentId', '<i8'), ('Time','<i8'),
('CashFlow', '<f8')]))
)
power = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 12, 3),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 12, 4),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 20, 12, 5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 5.5, 210),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 5.5, 211),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 5.5, 212),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 5.5, 213)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Value', '<f8'),
('Time', '<i8')]))
)
entry = pd.DataFrame(np.array([
(13, ':cycamore:Reactor', 210),
(20, ':cycamore:Reactor', 3),
(4, ':cycamore:Sink', 1)
], dtype=ensure_dt_bytes([('AgentId', '<i8'), ('Spec', 'O'),
('EnterTime', '<i8')]))
)
s1 = power.set_index(['SimId', 'AgentId', 'Value'])['Time']
s2 = entry.set_index(['AgentId', 'Spec'])['EnterTime']
series = [s1, s2]
obs = eco_metrics.capital_cost.func(series)
assert_frame_equal(exp, obs)
def test_inventories(db, fname, backend):
evaler = cym.Evaluator(db)
cal = filters.inventories(evaler)
exp_head = ['SimId', 'AgentId', 'Prototype',
'Time', 'InventoryName', 'NucId', 'Quantity', 'Units']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
cal = filters.inventories(evaler, facilities=['Reactor1'],
nucs=['94239'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(np.array([
(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327)
], dtype=ensure_dt_bytes([
('AgentId', '<i8'), ('Prototype', 'O'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8'),
('Units', '0')
]))
)
assert_frame_equal(cal, refs)
cal = filters.inventories(evaler, facilities=['Reactor1'],
nucs=['94239', '92235'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(np.array([
(15, 'Reactor1', 1, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'spent', 922350000, 0.00884955752212),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442),
(15, 'Reactor1', 3, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 3, 'spent', 922350000, 0.0176991150442),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885),
(15, 'Reactor1', 4, 'core', 922350000, 0.04),
(15, 'Reactor1', 4, 'spent', 922350000, 0.0265486725664),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327)
], dtype=ensure_dt_bytes([
('AgentId', '<i8'), ('Prototype', 'O'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8'),
('Units', '0')
]))
)
assert_frame_equal(cal, refs)
def test_timelist():
exp = pd.DataFrame(
np.array([(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 0),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 1)],
dtype=ensure_dt_bytes([('SimId', 'O'), ('TimeStep', '<i8')])))
info = pd.DataFrame(
np.array([(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 2)],
dtype=ensure_dt_bytes([('SimId', 'O'), ('Duration', '<i8')])))
obs = metrics.timelist.func(info)
assert_frame_equal(exp, obs)
def test_fco_electricity_generated():
exp = pd.DataFrame(
np.array([(0, 3), (1, 3)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('Energy', '<f8')])))
eg = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 1000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 2000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 3000),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Year', '<i8'), ('Energy', '<f8')])))
obs = fco_metrics.fco_electricity_generated.func(eg)
assert_frame_equal(exp, obs)
def test_inventories(db, fname, backend):
evaler = cym.Evaluator(db)
cal = filters.inventories(evaler)
exp_head = ['SimId', 'AgentId', 'Prototype',
'Time', 'InventoryName', 'NucId', 'Quantity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
cal = filters.inventories(evaler, facilities=['Reactor1'],
nucs=['94239'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(np.array([
(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327)
], dtype=ensure_dt_bytes([
('AgentId', '<i8'), ('Prototype', 'O'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')
]))
)
assert_frame_equal(cal, refs)
cal = filters.inventories(evaler, facilities=['Reactor1'],
nucs=['94239', '92235'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(np.array([
(15, 'Reactor1', 1, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 2, 'spent', 922350000, 0.00884955752212),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442),
(15, 'Reactor1', 3, 'core', 922350000, 0.00157922442534),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803),
(15, 'Reactor1', 3, 'spent', 922350000, 0.0176991150442),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885),
(15, 'Reactor1', 4, 'core', 922350000, 0.04),
(15, 'Reactor1', 4, 'spent', 922350000, 0.0265486725664),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327)
], dtype=ensure_dt_bytes([
('AgentId', '<i8'), ('Prototype', 'O'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_fco_electricity_gen():
exp = pd.DataFrame(np.array([(0, 3), (1, 10)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('Power', '<f8')]))
)
tsp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 1000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 2000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 12, 10000),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('Value', '<f8')]))
)
series = [tsp.set_index(['SimId', 'AgentId', 'Time'])['Value']]
obs = metrics.fco_electricity_gen.func(series)
assert_frame_equal(exp, obs)
def test_fco_electricity_gen():
exp = pd.DataFrame(np.array([(0, 3), (1, 10)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('Power', '<f8')]))
)
tsp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 1000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 2000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 12, 10000),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('Value', '<f8')]))
)
series = [tsp.set_index(['SimId', 'AgentId', 'Time'])['Value']]
obs = metrics.fco_electricity_gen.func(series)
assert_frame_equal(exp, obs)
def test_fco_electricity_generated():
exp = pd.DataFrame(np.array([(0, 3), (1, 10)], dtype=ensure_dt_bytes([("Year", "<i8"), ("Power", "<f8")])))
tsp = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 1, 3, 1000),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 2, 3, 2000),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 3, 12, 10000),
],
dtype=ensure_dt_bytes([("SimId", "O"), ("AgentId", "<i8"), ("Time", "<i8"), ("Value", "<f8")]),
)
)
series = [tsp.set_index(["SimId", "AgentId", "Time"])["Value"]]
obs = metrics.fco_electricity_generated.func(series)
assert_frame_equal(exp, obs)
def test_decayheat():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922350000, 2.3944723480343003e-12),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922380000, 1.6505536389997207e-11),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 942390000, 1.92784802432412e-08),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("DecayHeat", "<f8"),
]
),
)
)
act = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 942390000, 22949993169.28023),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Activity", "<f8"),
]
),
)
)
series = [act.set_index(["SimId", "QualId", "ResourceId", "ObjId", "TimeCreated", "NucId"])["Activity"]]
obs = metrics.decay_heat.func(series)
assert_frame_equal(exp, obs)
def test_timelist():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 0),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 1)
], dtype=ensure_dt_bytes([('SimId', 'O'), ('TimeStep', '<i8')]))
)
info = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2),
(UUID('f2952c34-0a0e-47df-b495-6f9afc351d1b'), 2)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('Duration', '<i8')]))
)
obs = metrics.timelist.func(info)
assert_frame_equal(exp, obs)
def test_fco_electricity_generated():
exp = pd.DataFrame(np.array([(0, 3),
(1, 3)
], dtype=ensure_dt_bytes([
('Year', '<i8'), ('Energy', '<f8')]))
)
eg = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 1000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 2000),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 3000),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Year', '<i8'),
('Energy', '<f8')]))
)
obs = fco_metrics.fco_electricity_generated.func(eg)
assert_frame_equal(exp, obs)
def test_activity():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 942390000, 22949993169.28023),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Activity", "<f8"),
]
),
)
)
mass = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922350000, 0.04),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 922380000, 1.94),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 9, 7, 1, 942390000, 0.01),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Mass", "<f8"),
]
),
)
)
series = [mass.set_index(["SimId", "QualId", "ResourceId", "ObjId", "TimeCreated", "NucId"])["Mass"]]
obs = metrics.activity.func(series)
assert_frame_equal(exp, obs)
def test_fco_swu():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array([(0, 0.002407), (1, 0.001473)], dtype=ensure_dt_bytes([("Year", "<i8"), ("SWU", "<f8")]))
)
mats = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 7, 3, 3, 922350000, 8.328354),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 7, 3, 3, 922380000, 325.004979),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 6, 8, 4, 3, 922350000, 11.104472),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 6, 8, 4, 3, 922380000, 322.228861),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 7, 9, 5, 12, 922350000, 11.104472),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 7, 9, 5, 12, 922380000, 322.228861),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Mass", "<f8"),
]
),
)
)
trans = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 1, 7, "LWR Fuel"),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 2, 8, "LWR Fuel"),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 3, 9, "LWR Fuel"),
],
dtype=ensure_dt_bytes(
[("SimId", "O"), ("TransactionId", "<i8"), ("ResourceId", "<i8"), ("Commodity", "O")]
),
)
)
s1 = mats.set_index(["SimId", "QualId", "ResourceId", "ObjId", "TimeCreated", "NucId"])["Mass"]
s2 = trans.set_index(["SimId", "TransactionId", "ResourceId"])["Commodity"]
series = [s1, s2]
obs = metrics.fco_swu.func(series)
np.allclose(exp, obs)
def test_fco_fuel_loading():
exp = pd.DataFrame(
np.array([(0, 0.666666), (1, 0.333333)], dtype=ensure_dt_bytes([("Year", "<i8"), ("FuelLoading", "<f8")]))
)
mats = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 7, 3, 3, 922350000, 8.328354),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 5, 7, 3, 3, 922380000, 325.004979),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 6, 8, 4, 3, 922350000, 11.104472),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 6, 8, 4, 3, 922380000, 322.228861),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 7, 9, 5, 12, 922350000, 11.104472),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 7, 9, 5, 12, 922380000, 322.228861),
],
dtype=ensure_dt_bytes(
[
("SimId", "O"),
("QualId", "<i8"),
("ResourceId", "<i8"),
("ObjId", "<i8"),
("TimeCreated", "<i8"),
("NucId", "<i8"),
("Mass", "<f8"),
]
),
)
)
trans = pd.DataFrame(
np.array(
[
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 1, 7, "LWR Fuel"),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 2, 8, "FR Fuel"),
(UUID("f22f2281-2464-420a-8325-37320fd418f8"), 3, 9, "FR Fuel"),
],
dtype=ensure_dt_bytes(
[("SimId", "O"), ("TransactionId", "<i8"), ("ResourceId", "<i8"), ("Commodity", "O")]
),
)
)
s1 = mats.set_index(["SimId", "QualId", "ResourceId", "ObjId", "TimeCreated", "NucId"])["Mass"]
s2 = trans.set_index(["SimId", "TransactionId", "ResourceId"])["Commodity"]
series = [s1, s2]
obs = metrics.fco_fuel_loading.func(series)
assert_frame_equal(exp, obs)
def test_inventory_quantity_per_gwe():
# exp is the expected output metrics
exp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 'core',
922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 'usedfuel',
922350000, 2.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 'core',
922350000, 2.0),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'),
('Quantity', '<f8')
])))
# tsp is the TimeSeriesPower metrics
tsp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 100),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Time', '<i8'), ('Value', '<f8')])))
# inv is the ExplicitInventory metrics
inv = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 'core',
922350000, 300),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 'usedfuel',
922350000, 600),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 'core',
922350000, 200),
],
dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'),
('Quantity', '<f8')
])))
obs = metrics.inventory_quantity_per_gwe.func(inv, tsp)
assert_frame_equal(exp, obs)
def test_monthly_electricity_generated_by_agent():
exp = pd.DataFrame(
np.array([(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 400)],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Month', '<i8'), ('Energy', '<f8')])))
tsp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 400),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Time', '<i8'), ('Value', '<f8')])))
obs = metrics.monthly_electricity_generated_by_agent.func(tsp)
assert_frame_equal(exp, obs)
def test_annual_electricity_generated_by_agent():
exp = pd.DataFrame(
np.array([(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 400)],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Year', '<i8'), ('Energy', '<f8')])))
tsp = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 1200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 2400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 12, 1200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 12, 2400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 12, 4800),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('AgentId', '<i8'),
('Time', '<i8'), ('Value', '<f8')])))
series = [tsp.set_index(['SimId', 'AgentId', 'Time'])['Value']]
obs = metrics.annual_electricity_generated_by_agent.func(series)
assert_frame_equal(exp, obs)
def test_activity():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 22949993169.28023),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Activity', '<f8')]))
)
mass = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 0.04),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 1.94),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 0.01),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Mass', '<f8')]))
)
obs = metrics.activity.func(mass)
assert_frame_equal(exp, obs)
def test_operation_maintenance():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 0, 232.3),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 2, 232.3),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 8, 400)
], dtype=ensure_dt_bytes([
('SimId','O'), ('AgentId', '<i8'), ('Time', '<i8'),
('O&MPayment','<f8')]))
)
power = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 2, 2.323),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 3, 2.323),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 13, 32, 2.323),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 100, 4)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('Value', '<f8')]))
)
s1 = power.set_index(['SimId', 'AgentId', 'Time'])['Value']
series = [s1]
obs = eco_metrics.operation_maintenance.func(series)
assert_frame_equal(exp, obs)
def test_fco_u_mined():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(
np.array([(0, 3.780034), (1, 2.185349)],
dtype=ensure_dt_bytes([('Year', '<i8'), ('UMined', '<f8')])))
mats = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3,
922350000, 8.328354),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 7, 3, 3,
922380000, 325.004979),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3,
922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 6, 8, 4, 3,
922380000, 322.228861),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12,
922350000, 11.104472),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 7, 9, 5, 12,
922380000, 322.228861),
],
dtype=ensure_dt_bytes([('SimId', 'O'), ('QualId', '<i8'),
('ResourceId', '<i8'), ('ObjId',
'<i8'),
('TimeCreated', '<i8'),
('NucId', '<i8'), ('Mass', '<f8')])))
trans = pd.DataFrame(
np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 7, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 8, 'LWR Fuel'),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 9, 'LWR Fuel'),
],
dtype=ensure_dt_bytes([('SimId', 'O'),
('TransactionId', '<i8'),
('ResourceId', '<i8'),
('Commodity', 'O')])))
obs = fco_metrics.fco_u_mined.func(mats, trans)
assert_frame_equal(exp, obs)
def test_decayheat():
if not HAVE_PYNE:
raise SkipTest
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 9.3280119931e+25),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 6.42996948889e+26),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 7.51020971553e+29),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('DecayHeat', '<f8')]))
)
act = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922350000, 3197501.3876324706),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 922380000, 24126337.066086654),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 5, 9, 7, 1, 942390000, 22949993169.28023),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('QualId', '<i8'), ('ResourceId', '<i8'), ('ObjId', '<i8'),
('TimeCreated', '<i8'), ('NucId', '<i8'), ('Activity', '<f8')]))
)
obs = metrics.decay_heat.func(act)
assert_frame_equal(exp, obs)
def test_annual_electricity_generated_by_agent():
exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 0, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 1, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 0, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 1, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 1, 400)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Year', '<i8'),
('Energy', '<f8')]))
)
tsp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 1200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 2400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 12, 1200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 12, 2400),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 12, 4800),
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('Value', '<f8')]))
)
obs = metrics.annual_electricity_generated_by_agent.func(tsp)
assert_frame_equal(exp, obs)
def test_timeseries_transactions_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.transactions_decayheat(evaler)
exp_head = ['Time', 'DecayHeat']
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = ts.transactions_decayheat(
evaler, nucs=['942390000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 3.34065303191e+30),
(2, 6.68130606382e+30),
(3, 6.68130606382e+30),
(4, 6.68130606382e+30),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('DecayHeat', '<f8')
]))
)
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions_decayheat(
evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 3.34091395721e+30),
(2, 6.68182791443e+30),
(3, 6.68214609848e+30),
(4, 6.68214609848e+30),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('DecayHeat', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_timeseries_transactions_activity(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = ts.transactions_activity(evaler)
exp_head = ['Time', 'Activity']
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = ts.transactions_activity(
evaler, nucs=['942390000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 102084984531.0),
(2, 204169969062.0),
(3, 204169969062.0),
(4, 204169969062.0),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Activity', '<f8')
]))
)
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions_activity(
evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 102094774891.0),
(2, 204189549782.0),
(3, 204201488588.0),
(4, 204201488588.0),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Activity', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_timeseries_transactions(db, fname, backend):
evaler = cym.Evaluator(db)
cal = ts.transactions(evaler)
exp_head = ['Time', 'Mass']
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
# test single nuclide selection
cal = ts.transactions(evaler, nucs=['942390000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 0.0444814879803),
(2, 0.0889629759607),
(3, 0.0889629759607),
(4, 0.0889629759607),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Mass', '<f8')
]))
)
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = ts.transactions(
evaler, nucs=['942390000', '922380000'])
refs = pd.DataFrame(np.array([
(0, 0.000000000),
(1, 0.831724864011),
(2, 1.66344972802),
(3, 2.62344972802),
(4, 2.62344972802),
], dtype=ensure_dt_bytes([
('Time', '<i8'), ('Mass', '<f8')
]))
)
assert_frame_equal(cal, refs)
def test_transactions_nuc(db, fname, backend):
evaler = cym.Evaluator(db)
cal = filters.transactions_nuc(evaler)
exp_head = [
'SimId', 'ResourceId', 'NucId', 'Mass', 'ReceiverId',
'ReceiverPrototype', 'SenderId', 'SenderPrototype', 'TransactionId',
'Commodity', 'Time'
]
assert_equal(list(cal), exp_head) # Check we have the correct headers
if not HAVE_PYNE:
raise SkipTest
# test single nuclide selection
cal = filters.transactions_nuc(evaler, nucs=['942390000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 0.0444814879803, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('Mass', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
# refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = filters.transactions_nuc(evaler, nucs=['942390000', '922380000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(922380000, 0.7872433760310, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(922380000, 0.7872433760310, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(922380000, 0.7872433760310, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(922380000, 0.7872433760310, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 0.0444814879803, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(922380000, 0.7872433760310, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(922380000, 0.7872433760310, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 0.0444814879803, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(922380000, 0.7872433760310, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(942390000, 0.0444814879803, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(922380000, 0.9600000000000, 17, 'Reactor3', 13, 'UOX_Source',
'uox', 3),
(922380000, 0.9600000000000, 15, 'Reactor1', 13, 'UOX_Source',
'uox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('Mass', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
# refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
def test_inventories_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = filters.inventories_decayheat(evaler)
exp_head = [
'SimId', 'AgentId', 'Prototype', 'Time', 'InventoryName', 'NucId',
'Quantity', 'Activity', 'DecayHeat'
]
assert_equal(list(cal), exp_head) # Check we have the correct headers
cal = filters.inventories_decayheat(evaler,
facilities=['Reactor1'],
nucs=['94239'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442,
9.71016906463e+12, 3.17757855136e+32),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885,
1.94203381293e+13, 6.35515710272e+32),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327,
2.91305071939e+13, 9.53273565408e+32)],
dtype=ensure_dt_bytes([('AgentId', '<i8'), ('Prototype', 'O'),
('Time', '<i8'),
('InventoryName', 'O'),
('NucId', '<i8'), ('Quantity', '<f8'),
('Activity', '<f8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
cal = filters.inventories_decayheat(evaler,
facilities=['Reactor1'],
nucs=['94239', '92235'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([(15, 'Reactor1', 1, 'core', 922350000, 0.00157922442534,
29671782.9213, 8.65609466244e+26),
(15, 'Reactor1', 1, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 2, 'core', 922350000, 0.00157922442534,
29671782.9213, 8.65609466244e+26),
(15, 'Reactor1', 2, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 2, 'spent', 922350000, 0.00884955752212,
166272852.378, 4.85064734329e+27),
(15, 'Reactor1', 2, 'spent', 942390000, 0.0176991150442,
9.71016906463e+12, 3.17757855136e+32),
(15, 'Reactor1', 3, 'core', 922350000, 0.00157922442534,
29671782.9213, 8.65609466244e+26),
(15, 'Reactor1', 3, 'core', 942390000, 0.0444814879803,
2.44036364223e+13, 7.98590335085e+32),
(15, 'Reactor1', 3, 'spent', 922350000, 0.0176991150442,
332545704.756, 9.70129468658e+27),
(15, 'Reactor1', 3, 'spent', 942390000, 0.0353982300885,
1.94203381293e+13, 6.35515710272e+32),
(15, 'Reactor1', 4, 'core', 922350000, 0.04, 751553292.748,
2.19249259917e+28),
(15, 'Reactor1', 4, 'spent', 922350000, 0.0265486725664,
498818557.134, 1.45519420299e+28),
(15, 'Reactor1', 4, 'spent', 942390000, 0.0530973451327,
2.91305071939e+13, 9.53273565408e+32)],
dtype=ensure_dt_bytes([('AgentId', '<i8'), ('Prototype', 'O'),
('Time', '<i8'),
('InventoryName', 'O'),
('NucId', '<i8'), ('Quantity', '<f8'),
('Activity', '<f8'),
('DecayHeat', '<f8')])))
assert_frame_equal(cal, refs)
def test_transactions_decayheat(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = filters.transactions_decayheat(evaler)
exp_head = [
'SimId', 'ResourceId', 'NucId', 'DecayHeat', 'ReceiverId',
'ReceiverPrototype', 'SenderId', 'SenderPrototype', 'TransactionId',
'Commodity', 'Time'
]
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = filters.transactions_decayheat(evaler, nucs=['942390000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 3.34065303191e+30, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('DecayHeat', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = filters.transactions_decayheat(evaler,
nucs=['942390000', '922380000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(922380000, 2.609253035160e26, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(922380000, 2.609253035160e26, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(922380000, 2.609253035160e26, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(922380000, 2.609253035160e26, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 3.34065303191e+30, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(922380000, 2.609253035160e26, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(922380000, 2.609253035160e26, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 3.34065303191e+30, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(922380000, 2.609253035160e26, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(942390000, 3.34065303191e+30, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(922380000, 3.18184057182e+26, 17, 'Reactor3', 13, 'UOX_Source',
'uox', 3),
(922380000, 3.18184057182e+26, 15, 'Reactor1', 13, 'UOX_Source',
'uox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('DecayHeat', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
assert_frame_equal(cal, refs)
def test_transactions(db, fname, backend):
evaler = cym.Evaluator(db)
cal = filters.transactions(evaler)
exp_head = [
'SimId', 'ReceiverId', 'ReceiverPrototype', 'SenderId',
'SenderPrototype', 'TransactionId', 'ResourceId', 'Commodity', 'Time'
]
assert_equal(list(cal), exp_head) # Check we have the correct headers
# SimId et al. change at each test need to drop it
drop_cols = [
'SimId', 'TransactionId', 'ResourceId', 'ReceiverId', 'SenderId'
]
cal = cal.drop(drop_cols, axis=1)
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 2),
('Reactor2', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 4),
('Reactor3', 'UOX_Source', 'uox', 3),
('Reactor3', 'MOX_Source', 'mox', 4),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test single sender
cal = filters.transactions(evaler, senders=['UOX_Source'])
cal = cal.drop(drop_cols, axis=1)
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor3', 'UOX_Source', 'uox', 3),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test multiple sender
cal = filters.transactions(evaler, senders=['UOX_Source', 'MOX_Source'])
cal = cal.drop(drop_cols, axis=1)
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 2),
('Reactor2', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 4),
('Reactor3', 'UOX_Source', 'uox', 3),
('Reactor3', 'MOX_Source', 'mox', 4),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test single receiver
cal = filters.transactions(evaler, receivers=['Reactor1'])
cal = cal.drop(drop_cols, 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test multiple sender
cal = filters.transactions(evaler, receivers=['Reactor1', 'Reactor3'])
cal = cal.drop(drop_cols, 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
('Reactor3', 'UOX_Source', 'uox', 3),
('Reactor3', 'MOX_Source', 'mox', 4),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test multiple sender and multiple receiver
cal = filters.transactions(evaler,
senders=['UOX_Source', 'MOX_Source'],
receivers=['Reactor1', 'Reactor2'])
cal = cal.drop(drop_cols, 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 2),
('Reactor2', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 4),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test single filters.odity
cal = filters.transactions(evaler, commodities=['uox'])
cal = cal.drop(drop_cols, 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor3', 'UOX_Source', 'uox', 3),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
# test multiple sender
cal = filters.transactions(evaler, commodities=['uox', 'mox'])
cal = cal.drop(drop_cols, 1) # SimId change at each test need to drop it
refs = pd.DataFrame(
np.array([
('Reactor1', 'UOX_Source', 'uox', 4),
('Reactor1', 'MOX_Source', 'mox', 1),
('Reactor1', 'MOX_Source', 'mox', 2),
('Reactor1', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 2),
('Reactor2', 'MOX_Source', 'mox', 3),
('Reactor2', 'MOX_Source', 'mox', 4),
('Reactor3', 'UOX_Source', 'uox', 3),
('Reactor3', 'MOX_Source', 'mox', 4),
],
dtype=ensure_dt_bytes([('ReceiverPrototype', 'O'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
refs.index = refs.index.astype('str')
assert_frame_equal(cal, refs)
def test_transactions_activity(db, fname, backend):
if not HAVE_PYNE:
raise SkipTest
evaler = cym.Evaluator(db)
cal = filters.transactions_activity(evaler)
exp_head = [
'SimId', 'ResourceId', 'NucId', 'Activity', 'ReceiverId',
'ReceiverPrototype', 'SenderId', 'SenderPrototype', 'TransactionId',
'Commodity', 'Time'
]
assert_equal(list(cal), exp_head) # Check we have the correct headers
# test single nuclide selection
cal = filters.transactions_activity(evaler, nucs=['942390000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 102084984531.0, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('Activity', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
assert_frame_equal(cal, refs)
# test multiple nuclide selection
cal = filters.transactions_activity(evaler,
nucs=['942390000', '922380000'])
cal = cal.drop('SimId', 1) # SimId change at each test need to drop it
# SimId change at each test need to drop it
cal = cal.drop('TransactionId', 1)
# SimId change at each test need to drop it
cal = cal.drop('ResourceId', 1)
refs = pd.DataFrame(
np.array([
(922380000, 9790360.331530, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 1),
(922380000, 9790360.331530, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 2),
(922380000, 9790360.331530, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 2),
(922380000, 9790360.331530, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(942390000, 102084984531.0, 15, 'Reactor1', 14, 'MOX_Source',
'mox', 3),
(922380000, 9790360.331530, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 3),
(922380000, 9790360.331530, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(942390000, 102084984531.0, 16, 'Reactor2', 14, 'MOX_Source',
'mox', 4),
(922380000, 9790360.331530, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(942390000, 102084984531.0, 17, 'Reactor3', 14, 'MOX_Source',
'mox', 4),
(922380000, 11938805.97080, 17, 'Reactor3', 13, 'UOX_Source',
'uox', 3),
(922380000, 11938805.97080, 15, 'Reactor1', 13, 'UOX_Source',
'uox', 4),
],
dtype=ensure_dt_bytes([('NucId', '<i8'), ('Activity', '<f8'),
('ReceiverId', '<i8'),
('ReceiverPrototype', 'O'),
('SenderId', '<i8'),
('SenderPrototype', 'O'),
('Commodity', 'O'), ('Time', '<i8')])))
assert_frame_equal(cal, refs)
