def test_feed():
xf, xp, xt = 0.0072, 0.05, 0.0025
feed, prod, tails = 15.1596, 1.5, 13.6596
swu = 11765.0
exp = feed
obs = enr.feed(xf, xp, xt, product=prod)
assert_almost_equal(obs, exp, places=4)
obs = enr.feed(xf, xp, xt, tails=tails)
assert_almost_equal(obs, exp, places=4)
def test_feed():
xf, xp, xt = 0.0072, 0.05, 0.0025
feed, prod, tails = 15.1596, 1.5, 13.6596
swu = 11765.0
exp = feed
obs = enr.feed(xf, xp, xt, product=prod)
assert_almost_equal(obs, exp, places=4)
obs = enr.feed(xf, xp, xt, tails=tails)
assert_almost_equal(obs, exp, places=4)
def fco_u_mined(series):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a simulation. This is written for simulations that use the
Bright-lite Fuel Fab (i.e., the U235 and U238 are given separately in the
FCO simulations, and the filtering is archetype-specific).
"""
tools.raise_no_pyne('U_Mined could not be computed', HAVE_PYNE)
mass = pd.merge(series[0].reset_index(), series[1].reset_index(),
on=['ResourceId'], how='inner').set_index(['ObjId',
'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc==922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(x_feed, x_prod, x_tails, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'UMined': u}, columns=['Year', 'UMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def fco_u_mined(series):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a 200-yr simulation. This is written for FCO databases that use the
Bright-lite Fuel Fab(i.e., the U235 and U238 are given separately in the
FCO simulations)."""
mass = pd.merge(series[0].reset_index(), series[1].reset_index(),
on=['ResourceId'], how='inner').set_index(['ObjId',
'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc==922350000:
mass235[obj] = value['Mass']
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(0.0072, x_prod, 0.0025, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'FcoUMined': u}, columns=['Year', 'FcoUMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def fco_u_mined(series):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a 200-yr simulation. This is written for FCO databases that use the
Bright-lite Fuel Fab(i.e., the U235 and U238 are given separately in the
FCO simulations)."""
mass = pd.merge(series[0].reset_index(),
series[1].reset_index(),
on=['ResourceId'],
how='inner').set_index(['ObjId', 'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc == 922350000:
mass235[obj] = value['Mass']
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(0.0072, x_prod, 0.0025, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={
'Year': m.TimeCreated.apply(lambda x: x // 12),
'FcoUMined': u
},
columns=['Year', 'FcoUMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def fco_u_mined(mats, trans):
"""FcoUMined metric returns the uranium mined in tonnes for each year
in a simulation. This is written for simulations that use the
Bright-lite Fuel Fab (i.e., the U235 and U238 are given separately in the
FCO simulations, and the filtering is archetype-specific).
"""
tools.raise_no_pyne('U_Mined could not be computed', HAVE_PYNE)
mass = pd.merge(mats, trans, on=['ResourceId'], how='inner')
mass = mass.set_index(['ObjId', 'TimeCreated', 'NucId'])
u = []
prods = {}
mass235 = {}
m = mass[mass['Commodity'] == 'LWR Fuel']
for (obj, _, nuc), value in m.iterrows():
if 922320000 <= nuc <= 922390000:
prods[obj] = prods.get(obj, 0.0) + value['Mass']
if nuc==922350000:
mass235[obj] = value['Mass']
x_feed = 0.0072
x_tails = 0.0025
for obj, m235 in mass235.items():
x_prod = m235 / prods[obj]
feed = enr.feed(x_feed, x_prod, x_tails, product=prods[obj]) / 1000
u.append(feed)
m = m.groupby(level=['ObjId', 'TimeCreated'])['Mass'].sum()
m = m.reset_index()
# sum by years (12 time steps)
u = pd.DataFrame(data={'Year': m.TimeCreated.apply(lambda x: x//12),
'UMined': u}, columns=['Year', 'UMined'])
u = u.groupby('Year').sum()
rtn = u.reset_index()
return rtn
def __call__(self, qty, enr, commod=None):
return enrichment.feed(0.0072, enr / 100., 0.0025, product=qty)