Metadata

cymetric/metadata.py

@@ -0,0 +1,99 @@
+import warnings
+import pandas as pd
+import numpy as np
+
+
+try:
+    from pyne import data
+    import pyne.enrichment as enr
+    from pyne import nucname
+    HAVE_PYNE = True
+except ImportError:
+    HAVE_PYNE = False
+
+
+from cymetric.tools import format_nucs, reduce, merge, add_missing_time_step
+
+def get_usage(evaler):
+
+    metadata = evaler.eval("Metadata")
+    agents_entry = evaler.eval('AgentEntry')
+    deployment = metadata[ metadata['Type'] == "deployment" ]
+    decom = metadata[ metadata['Type'] == "decommission" ]
+    timestep = metadata[ metadata['Type'] == "timestep" ]
+    throughput_meta = metadata[ metadata['Type'] == "throughput" ]
+
+    worklabel = metadata[ metadata['Keyword'] == "WORKLABEL" ]
+
+    # Deployement
+    dep_agent = agents_entry[agents_entry['AgentId'].isin(deployment['AgentId'])]
+    _tmp = pd.merge(deployment[['SimId', 'AgentId','Keyword', 'Value']], dep_agent, on=['SimId', 'AgentId'])
+
+
+    deployment_use = pd.DataFrame(data={'SimId': _tmp.SimId,
+                                        'AgentId': _tmp.AgentId,
+                                        'Time': _tmp.EnterTime,
+                                        'Keyword': _tmp.Keyword,
+                                        'Value':_tmp.Value.astype(float)},
+                                  columns=['SimId', 'AgentId', 'Time', 'Keyword', 'Value'])
+    rtn = deployment_use.copy()
+
+    # Decommision
+    decom_agent = agents_entry[agents_entry['AgentId'].isin(decom['AgentId'])]
+    decom_agent = decom_agent.reset_index(drop=True)
+    decom_agent['ExitTime'] = decom_agent['EnterTime'] + decom_agent['Lifetime']
+    _tmp = pd.merge(decom[['SimId', 'AgentId','Keyword', 'Value']], decom_agent, on=['SimId', 'AgentId'])
+    decom_use = pd.DataFrame(data={'SimId': _tmp.SimId,
+                                   'AgentId': _tmp.AgentId,
+                                   'Time': _tmp.ExitTime,
+                                   'Keyword': _tmp.Keyword,
+                                   'Value':_tmp.Value.astype(float)},
+                             columns=['SimId', 'AgentId', 'Time', 'Keyword', 'Value'])
+    rtn = pd.concat([rtn, decom_use], ignore_index=True)
+
+    # TimeStep
+    timestep_agent = agents_entry[agents_entry['AgentId'].isin(timestep['AgentId'])]
+    timestep_agent = timestep_agent.reset_index(drop=True)
+    timestep_agent['ExitTime'] = timestep_agent['EnterTime'] + timestep_agent['Lifetime']
+    timestep_tmp = pd.DataFrame(data={'SimId': _tmp.SimId,
+                                      'AgentId': _tmp.AgentId,
+                                      'EnterTime': _tmp.EnterTime,
+                                      'ExitTime': _tmp.ExitTime ,
+                                      'Keyword': _tmp.Keyword,
+                                      'Value':_tmp.Value.astype(float)},
+                              columns=['SimId', 'AgentId', 'EnterTime', 'ExitTime', 'Keyword', 'Value'])
+    time_step_data = []
+    for index, row  in timestep_tmp.iterrows():
+        for i in range(row['EnterTime'], row['ExitTime']):
+            time_step_data.append( (row['SimId'],
+                          row['AgentId'],
+                          i,
+                          row['Keyword'],
+                          row['Value']))
+    timestep_use = pd.DataFrame(time_step_data, columns=['SimId', 'AgentId', 'Time', 'Keyword', 'Value'])
+    rtn = pd.concat([rtn, timestep_use], ignore_index=True)
+
+    worklabel.drop_duplicates(inplace=True)
+    worktimeseries = []
+    for index, row in worklabel.iterrows():
+        if (row['AgentId'] in throughput_meta['AgentId']):
+            work_name = "TimeSeries" + row['Value']
+            timeseries = evaler.eval(work_name)
+            if timeseries is not None:
+                worktimeseries.append(timeseries[timeseries['AgentId'] == row['AgentId'] ])
+
+    def get_throughput_timeseries(throughput_df, throughput_meta):
+        if throughput_df is not None:
+            _tmp = pd.merge(throughput_meta[['SimId', 'AgentId','Keyword', 'Value']], throughput_df, on=['SimId', 'AgentId'])
+            _tmp['Value_y'] = _tmp.Value_x.astype(float)*_tmp.Value_y
+            _tmp.drop(columns=['Value_x'], inplace=True)
+            _tmp.rename(columns={"Value_y": "Value"}, inplace=True)
+            return _tmp[['SimId', 'AgentId', 'Time', 'Keyword', 'Value']]
+        else:
+            return pd.DataFrame()
+
+    for work in worktimeseries:
+        rtn = pd.concat([rtn, get_throughput_timeseries(work, throughput_meta)], ignore_index=True)
+
+
+    return rtn

cymetric/metrics.py

@@ -400,7 +400,7 @@ def annual_electricity_generated_by_agent(elec):
                               'AgentId': elec.AgentId,
                               'Year': elec.Time.apply(lambda x: x//12),
                               'Energy': elec.Value.apply(lambda x: x/12)},
-			columns=['SimId', 'AgentId', 'Year', 'Energy'])
+            columns=['SimId', 'AgentId', 'Year', 'Energy'])
     el_index = ['SimId', 'AgentId', 'Year']
     elec = elec.groupby(el_index).sum()
     rtn = elec.reset_index()

cymetric/root_metrics.py

@@ -100,5 +100,8 @@ def root_metric(obj=None, name=None, schema=None, *args, **kwargs):
 
 #toolkit-enabled tables
 time_series_power = root_metric(name='TimeSeriesPower')
+time_series_deployedisnt = root_metric(name='TimeSeriesDeployedInst')
+time_series_throughput = root_metric(name='TimeSeriesThroughput')
 time_series_enrichmentfeed = root_metric(name='TimeSeriesEnrichmentFeed')
 time_series_enrichmentswu = root_metric(name='TimeSeriesEnrichmentSWU')
+metadata = root_metric(name='Metadata')

cymetric/tools.py

@@ -117,8 +117,8 @@ def add_missing_time_step(df, ref_time):
 
 def merge(df, base_col, add_df, add_col):
     """
-    Merge some additionnal columns fram an additionnal Pandas Data Frame
-    onother one and then remove the second base column (keeping SimID
+    Merge some additionnal columns from an additionnal Pandas Data Frame
+    another one and then remove the second base column (keeping SimID
     information).
 
     Parameters