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

cymetric/metrics.py

@@ -504,4 +504,58 @@ def inventory_quantity_per_gwe(expinv,power):
     inv.Quantity = inv.Quantity/inv.Value
     inv=inv.drop(['Value'],axis=1)
     return inv
-
+
+# Cumulative power from time 0 to time t in TimeSeriesPower metric
+_cumpdeps = ['TimeSeriesPower']
+
+_cumpschema = [
+    ('SimId', ts.UUID),
+    ('AgentId', ts.INT),
+    ('Time', ts.INT),
+    ('Value', ts.DOUBLE)
+    ]
+
+@metric(name='CumulativeTimeSeriesPower', depends=_cumpdeps, schema=_cumpschema)
+def cumulative_timeseriespower(power):
+    """Cumulative Timeseriespower metric returns the TimeSeriesPower metric with a cumulative sum
+    of the power generated by each SimId from time 0 to time t.
+    """
+    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()
+    df1['Value'] = df1['Value'].cumsum()
+    return df1
+
+# Quantity per GigaWattElectric Per Cumulative Power in Inventory [kg/GWe]
+_invdeps = ['ExplicitInventory','CumulativeTimeSeriesPower']
+
+_invschema = [
+    ('SimId', ts.UUID),
+    ('AgentId', ts.INT),
+    ('Time', ts.INT),
+    ('InventoryName', ts.STRING),
+    ('NucId', ts.INT),
+    ('Quantity', ts.DOUBLE)
+    ]
+@metric(name='InventoryQuantityPerCumulativePower', depends=_invdeps, schema=_invschema)
+def inventory_quantity_per_cumulative_power(expinv,cumpower):
+    """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 cumulative sum of
+    the electricity generated in TimeSeriesPower metric from time 0
+    """
+    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,cumpower, on=['SimId','Time'],how='left')
+    inv.Quantity = inv.Quantity/inv.Value
+    inv=inv.drop(['Value'],axis=1)
+    return inv

tests/test_metrics.py

@@ -396,6 +396,59 @@ def test_inventory_quantity_per_gwe():
     obs = metrics.inventory_quantity_per_gwe.func(inv, tsp)
     assert_frame_equal(exp, obs)
 
+
+def test_cumulative_timeseriespower():
+    #exp is the expected output metrics
+    exp = pd.DataFrame(np.array([
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 300),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 700)
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'),  ('Time', '<i8'), ('Value', '<f8')]))
+        )    
+    #tsp is the TimeSeriesPower metrics
+    tsp = pd.DataFrame(np.array([
+        (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')]))
+        )
+    obs = metrics.cumulative_timeseriespower.func(tsp)
+    assert_frame_equal(exp, obs)
+
+
+def test_inventory_quantity_per_cumulative_power():
+    #exp is the expected output metrics
+    exp = pd.DataFrame(np.array([
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'core', 922350000, 1.0),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 1, 2, 'usedfuel', 922350000, 2.0),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'core', 922350000, 1.5),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'usedfuel', 922350000, 0.5)
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
+                ('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')]))
+        )
+    #ctsp is the CumulativeTimeSeriesPower metrics
+    ctsp = pd.DataFrame(np.array([
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 300),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 3, 700)
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'),  ('Time', '<i8'), ('Value', '<f8')]))
+        )    
+    #inv is the ExplicitInventory metrics
+    inv = pd.DataFrame(np.array([
+        (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, 1050),
+        (UUID('f22f2281-2464-420a-8325-37320fd418f8'), 2, 3, 'usedfuel', 922350000, 350)
+        ], dtype=ensure_dt_bytes([
+                ('SimId', 'O'), ('AgentId', '<i8'), ('Time', '<i8'),
+                ('InventoryName', 'O'), ('NucId', '<i8'), ('Quantity', '<f8')]))
+        )
+    obs = metrics.inventory_quantity_per_cumulative_power.func(inv, ctsp)
+    assert_frame_equal(exp, obs)
 
 if __name__ == "__main__":
     nose.runmodule()