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New metrics to return Transaction table with quantity in units of kg/GWe #161

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New metrics to return Transaction table with quantity in units of kg/GWe #161

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4d7a738
Create TransactionQuantityPerGWe metrics and tests for transaction_qu…
louishartono Feb 19, 2020
0095610
Removed inventory_quantity_per_gwe in metrics.py and test_metrics.py
louishartono Feb 19, 2020
74722c1
Change Time to TimeCreated on test_transaction_quantity_per_gwe
louishartono Feb 26, 2020
a9dfe4b
Clarify the purpose of the metric
louishartono Mar 4, 2020
96ecee8
Merge branch 'master' into transaction_metrics
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bam241 Jul 27, 2020
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77 changes: 77 additions & 0 deletions cymetric/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -537,3 +537,80 @@ def timelist(info):


del _tldeps, _tlschema


# Quantity per GigaWattElectric in Inventory [kg/GWe]
_invdeps = ['ExplicitInventory','TimeSeriesPower']

_invschema = [
('SimId', ts.UUID),
('AgentId', ts.INT),
('Time', ts.INT),
('InventoryName', ts.STRING),
('NucId', ts.INT),
('Quantity', ts.DOUBLE)
]

@metric(name='InventoryQuantityPerGWe', depends=_invdeps, schema=_invschema)
def inventory_quantity_per_gwe(expinv,power):
"""Inventory Quantity per GWe metric returns the explicit inventory table with quantity
in units of kg/GWe, calculated by dividing the original quantity by the electricity generated
at the corresponding simulation and the specific time in TimeSeriesPower metric.
"""
power = pd.DataFrame(data={'SimId': power.SimId,
'AgentId': power.AgentId,
'Time': power.Time,
'Value': power.Value},
columns=['SimId','AgentID','Time', 'Value'])
power_index = ['SimId','Time']
power = power.groupby(power_index).sum()
df1 = power.reset_index()
inv = pd.DataFrame(data={'SimId': expinv.SimId,
'AgentId': expinv.AgentId,
'Time': expinv.Time,
'InventoryName': expinv.InventoryName,
'NucId': expinv.NucId,
'Quantity': expinv.Quantity},
columns=['SimId','AgentId','Time','InventoryName','NucId','Quantity'])
inv=pd.merge(inv,df1, on=['SimId','Time'],how='left')
inv.Quantity = inv.Quantity/inv.Value
inv=inv.drop(['Value'],axis=1)
return inv
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# Quantity per GigaWattElectric in TransactionQuantity [kg/GWe]
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_tranactsdeps = ['TransactionQuantity','TimeSeriesPower']

_tranactsschema = [
('SimId', ts.UUID),
('TransactionId', ts.INT),
('ResourceId', ts.INT),
('ObjId', ts.INT),
('Time', ts.INT),
('SenderId', ts.INT),
('ReceiverId', ts.INT),
('Commodity', ts.STRING),
('Units', ts.STRING),
('Quantity', ts.DOUBLE)
]


@metric(name='TransactionQuantityPerGWe',
depends=_tranactsdeps, schema=_tranactsschema)
def transaction_quantity_per_gwe(tranacts, power):
"""Transaction Quantity per GWe metric returns the transaction quantity
table with quantity in units of kg/GWe, calculated by dividing the
original quantity by the electricity generated at the corresponding
simulation and the specific time in TimeSeriesPower metric.
"""
power = power.groupby(['SimId', 'Time']).sum()
df1 = power.reset_index()
tranacts_index = ['SimId', 'TransactionId', 'ResourceId',
'ObjId', 'Time','SenderId', 'ReceiverId',
'Commodity', 'Units', 'Quantity']
tranacts = tranacts.rename(columns = {'TimeCreated' : 'Time'})
tranacts['Units'] = tranacts['Units'] + "/MWe"
tranacts=pd.merge(tranacts,df1, on=['SimId', 'Time'],how='left')
tranacts.Quantity = tranacts.Quantity/tranacts.Value
return tranacts[tranacts_index]

122 changes: 85 additions & 37 deletions tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -549,53 +549,101 @@ def test_timelist():
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')
]))
)
(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')
]))
)
], 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')
]))
)
(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)
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def test_transaction_quantity_per_gwe():
# exp is the expected output metrics
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exp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'),
1, 7, 3, 3, 10, 20, 'LWR Fuel', 'kg/MWe', 0.82),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'),
2, 8, 4, 3, 20, 30, 'FR Fuel', 'kg/MWe', 0.61),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'),
3, 9, 5, 12, 30, 40, 'Spent Fuel', 'kg/MWe', 0.09),
], dtype=ensure_dt_bytes([
('SimId', 'O'),
('TransactionId', '<i8'),
('ResourceId', '<i8'),
('ObjId', '<i8'),
('Time', '<i8'),
('SenderId', '<i8'),
('ReceiverId', '<i8'),
('Commodity', 'O'),
('Units', 'O'),
('Quantity', '<f8')]))
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)
# tsp is the TimeSeriesPower metrics
tsp = pd.DataFrame(np.array([
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 225),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 275),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 12, 100),
], dtype=ensure_dt_bytes([
('SimId', 'O'),
('AgentId', '<i8'),
('Time', '<i8'),
('Value', '<f8')]))
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)
# tranacts is the TransactionQuantity metrics
tranacts = 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')]))
)
obs = metrics.transaction_quantity_per_gwe.func(tranacts, tsp)
assert_frame_equal(exp, obs)


if __name__ == "__main__":
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nose.runmodule()