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New metrics to return Inventory table with quantity (kg/GWe) in terms of cumulative power from time 0 to time of the simulation #163

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New metrics to return Inventory table with quantity (kg/GWe) in terms of cumulative power from time 0 to time of the simulation #163

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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
3dcb82b
Created a new InventoryQuantityPerTotalGWe metric
louishartono Mar 6, 2020
9c24779
Added test for InventoryQuantityPerTotalGWe
louishartono Mar 6, 2020
58cd70f
Make some changes to add the power from time 0 to time t and modify t…
louishartono Mar 11, 2020
515c734
Change schema to CumulativeTimeSeriesPower instead of TimeSeriesPower…
louishartono Mar 11, 2020
4a172d0
Merge branch 'master' into integral_power
louishartono May 8, 2020
d20a095
Update test_metrics.py
louishartono May 8, 2020
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25 changes: 16 additions & 9 deletions cymetric/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

import numpy as np
import pandas as pd
import scipy.integrate as integrate

try:
from pyne import data
Expand Down Expand Up @@ -467,21 +468,22 @@ def timelist(info):

del _tldeps, _tlschema

# Quantity per GigaWattElectric in Inventory [kg/GWe]
# Quantity per GigaWattElectric (integration of total power) in Inventory [kg/GWe]
_invdeps = ['ExplicitInventory','TimeSeriesPower']

_invschema = [
('SimId', ts.UUID),
('AgentId', ts.INT),
('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):
"""Returns quantity per GWe in the inventory table
@metric(name='InventoryQuantityPerTotalGWe', depends=_invdeps, schema=_invschema)
def inventory_quantity_per_total_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 integration of total
electricity generated in TimeSeriesPower metric from time 0 to time of the simulation.
"""
power = pd.DataFrame(data={'SimId': power.SimId,
'AgentId': power.AgentId,
Expand All @@ -491,15 +493,20 @@ def inventory_quantity_per_gwe(expinv,power):
power_index = ['SimId','Time']
power = power.groupby(power_index).sum()
df1 = power.reset_index()
total_power = 0
for t in range(len(df1)):
realpower = total_power + df1.Value[t]
total_power += realpower
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@bam241 bam241 Mar 6, 2020 •
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Suggested change
total_power += realpower
total_power += df1.Value[t]

integral = integrate.quad(lambda t:realpower*(t**0),0,df1.Time[t])
df1.Value[t] = integral[0]
inv = pd.DataFrame(data={'SimId': expinv.SimId,
'AgentId': expinv.AgentId,
'Time': expinv.Time,
'Time': expinv.Time,
'InventoryName': expinv.InventoryName,
'NucId': expinv.NucId,
'Quantity': expinv.Quantity},
columns=['SimId','AgentId','Time','InventoryName','NucId','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)
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maybe don't drop but just return the required column ?
I recently learned this is possible and I feel like it is clearer about what is actually returned

return inv

24 changes: 13 additions & 11 deletions tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -365,38 +365,40 @@ def test_timelist():
assert_frame_equal(exp, obs)


def test_inventory_quantity_per_gwe():
def test_inventory_quantity_per_total_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),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'core', 922350000, 0.5),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'usedfuel', 922350000, 1.0),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'core', 922350000, 0.75),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'usedfuel', 922350000, 1.0)
], 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),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 100),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 2, 200),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 3, 300),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 100)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('Value', '<f8')]))
)
#inv is the ExplicitInventory metrics
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Suggested change
#inv is the ExplicitInventory metrics
# 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),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'core', 922350000, 300),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'usedfuel', 922350000, 600),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'core', 922350000, 900),
(UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'usedfuel', 922350000, 1200)
], dtype=ensure_dt_bytes([
('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')]))
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same here, maybe put all the pair on a line ?

)
obs = metrics.inventory_quantity_per_gwe.func(inv, tsp)
assert_frame_equal(exp, obs)


if __name__ == "__main__":
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PEP8 2 blank lines between methods

nose.runmodule()