96 lines
5.6 KiB
Python
96 lines
5.6 KiB
Python
import pandas as pd
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from .parts.utils import *
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from model import config
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from cadCAD.engine import ExecutionMode, ExecutionContext,Executor
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from cadCAD import configs
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def run():
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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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# Single
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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=configs)
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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, sim_ind=-1):
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'''
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Function for postprocessing the simulation results to extract key information from the network object.
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'''
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# subset to last substep of each simulation
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df= df[df.substep==df.substep.max()]
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sim_count = df.simulation.max()
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if sim_ind <0:
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sim_ind = sim_count+1+sim_ind
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df=df[df.simulation==sim_ind]
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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['participant_count'] = df.network.apply(lambda g: len([j for j in get_nodes_by_type(g, 'participant') if g.nodes[j]['type']=='participant']))
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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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# extract metrics
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percentageOfActive = []
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percentageOfCompleted = []
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percentageOfKilled = []
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for i in range(0,len(df.timestep)):
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percentageOfActive.append(df.fractionOfProposalStages.values[i]['percentageOfActive'])
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percentageOfCompleted.append(df.fractionOfProposalStages.values[i]['percentageOfCompleted'])
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percentageOfKilled.append(df.fractionOfProposalStages.values[i]['percentageOfKilled'])
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df['percentageOfActiveProposals'] = percentageOfActive
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df['percentageOfCompletedProposals'] = percentageOfCompleted
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df['percentageOfKilledProposals'] = percentageOfKilled
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percentageOfActiveFundsRequested = []
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percentageOfCompletedFundsRequested = []
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percentageOfKilledFundsRequested = []
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for i in range(0,len(df.timestep)):
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percentageOfActiveFundsRequested.append(df.fractionOfFundStages.values[i]['percentageOfActiveFundsRequested'])
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percentageOfCompletedFundsRequested.append(df.fractionOfFundStages.values[i]['percentageOfCompletedFundsRequested'])
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percentageOfKilledFundsRequested.append(df.fractionOfFundStages.values[i]['percentageOfKilledFundsRequested'])
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df['percentageOfActiveFundsRequested'] = percentageOfActiveFundsRequested
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df['percentageOfCompletedFundsRequested'] = percentageOfCompletedFundsRequested
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df['percentageOfKilledFundsRequested'] = percentageOfKilledFundsRequested
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return df |