60 lines
3.9 KiB
Python
60 lines
3.9 KiB
Python
import pandas as pd
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from .model.conviction_helper_functions import *
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from model import economyconfig
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from cadCAD.engine import ExecutionMode, ExecutionContext
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exec_mode = ExecutionMode()
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from cadCAD.engine import Executor
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from cadCAD import configs
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def run(input_config=configs):
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'''
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Definition:
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Run simulation
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Parameters:
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input_config: Optional way to pass in system configuration
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'''
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exec_mode = ExecutionMode()
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local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
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simulation = Executor(exec_context=local_mode_ctx, configs=input_config)
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raw_system_events, tensor_field, sessions = simulation.execute()
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# Result System Events DataFrame
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df = pd.DataFrame(raw_system_events)
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return df
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def postprocessing(df):
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'''
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'''
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# Extract information from dataframe
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df['conviction'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
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df['candidate_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
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df['candidate_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
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df['killed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='killed']))
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df['killed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='killed']))
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df['candidate_funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate']))
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df['active_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))
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df['active_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='active']))
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df['failed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='failed']))
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df['failed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='failed']))
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df['completed_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))
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df['completed_funds'] = df.network.apply(lambda g: np.sum([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='completed']))
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df['funds_requested'] = df.network.apply(lambda g: np.array([g.nodes[j]['funds_requested'] for j in get_nodes_by_type(g, 'proposal')]))
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df['share_of_funds_requested'] = df.candidate_funds_requested/df.funds
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df['share_of_funds_requested_all'] = df.funds_requested/df.funds
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df['triggers'] = df.network.apply(lambda g: np.array([g.nodes[j]['trigger'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate' ]))
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df['conviction_share_of_trigger'] = df.conviction/df.triggers
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df['age'] = df.network.apply(lambda g: np.array([g.nodes[j]['age'] for j in get_nodes_by_type(g, 'proposal') if g.nodes[j]['status']=='candidate' ]))
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df['age_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['age'] for j in get_nodes_by_type(g, 'proposal') ]))
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df['conviction_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['conviction'] for j in get_nodes_by_type(g, 'proposal') ]))
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df['triggers_all'] = df.network.apply(lambda g: np.array([g.nodes[j]['trigger'] for j in get_nodes_by_type(g, 'proposal') ]))
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df['conviction_share_of_trigger_all'] = df.conviction_all/df.triggers_all
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rdf= df[df.substep==df.substep.max()].copy()
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return df,rdf |