Upload app.py

app.py

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+import pandas as pd
+import numpy as np
+from random import normalvariate, random
+import plotly.express as px
+
+from cadCAD.configuration.utils import config_sim
+from cadCAD.configuration import Experiment
+from cadCAD.engine import ExecutionContext, Executor
+from cadCAD import configs
+import streamlit as st
+
+
+# Additional dependencies
+
+# For analytics
+import numpy as np
+# For visualization
+import plotly.express as px
+pd.options.plotting.backend = "plotly"
+
+st.header('CeSci Value Flow Model')
+
+def p_value_flow(params, substep, state_history, previous_state):
+    funding = 0
+    management_costs = 0
+    to_researcher = 0
+    to_journal = 0
+    salary = 0
+    losses = 0
+    if random() < params['probability_funding'] and (previous_state['funding_pool'] > funding):
+        funding = params['funding_round']
+        management_costs = funding * params['alpha']
+        to_researcher = funding - management_costs
+        losses = management_costs
+
+        research_value = funding * (1-params['epsilon'])
+        losses += to_researcher - research_value
+
+        salary = research_value * params['beta']
+        to_journal = research_value + params['cost_publishing']
+        if random() < params['probability_buying']:
+            salary = salary - params['cost_buying']
+            to_journal += params['cost_buying']
+    # losses = funding - to_journal
+    return {'update_researcher_funding': to_researcher,
+            'update_funding_pool': -funding,
+            'update_journal': to_journal,
+            'update_researcher_done': salary,
+            'update_losses': losses}
+
+def s_researcher_value(params, substep, state_history, previous_state, policy_input):
+    research_funding = policy_input['update_researcher_funding']
+    research_salary = policy_input['update_researcher_done']
+    research_value = previous_state['researcher_value']
+
+    if research_salary == 0:
+        updated_researcher_value = research_funding + research_value
+        return 'researcher_value', updated_researcher_value
+    else:
+        updated_researcher_value = research_salary + research_value
+        return 'researcher_value', updated_researcher_value
+
+def s_journal_value(params, substep, state_history, previous_state, policy_input):
+    to_journal = policy_input['update_journal']
+    journal_value = previous_state['journal_value']
+
+    updated_journal_value = to_journal + journal_value
+
+    return 'journal_value', updated_journal_value
+
+def s_funding_pool(params, substep, state_history, previous_state, policy_input):
+    funding_pool = previous_state['funding_pool']
+    updated_funding_pool = funding_pool + policy_input['update_funding_pool']
+    if updated_funding_pool < 0:
+        updated_funding_pool = 0
+    return 'funding_pool', updated_funding_pool
+
+def s_losses(params, substep, state_history, previous_state, policy_input):
+    losses = previous_state['losses']
+    updated_losses = losses + policy_input['update_losses']
+    return 'losses', updated_losses
+
+st.subheader('Initial Value Allocation')
+funding_pool = st.slider('Initial Funding Pool', min_value=1000, max_value=10000, value=1000, step=10)
+researcher_value = st.slider('Initial Researcher Tokens', min_value=0, max_value=1000, value=0, step=1)
+journal_value = st.slider('Initial Journal Tokens', min_value=0, max_value=1000, value=0, step=1)
+st.subheader('Simulation Parameters')
+st.write('Set the funding disbursed each round from the funding pool')
+funding_round = st.slider('Funding Round', min_value=100, max_value=1000, value=100, step=1)
+st.write('Set the relative value leakages in the model.')
+alpha = st.slider('Management Cost Weight', min_value=0., max_value=1., value=0.1, step=0.0001)
+epsilon = st.slider('Work Inefficiency Weight', min_value=0., max_value=1., value=0.1, step=0.0001)
+st.write('Set the portion of grant funding to be used as researcher salary.')
+beta = st.slider('Salary Weight', min_value=0., max_value=1., value=0.4, step=0.0001)
+st.write('Set the cost of publishing to a journal and the cost of getting access to papers.')
+cost_publishing = st.slider('Cost of Publishing', min_value=10., max_value=100., value=10., step=0.1)
+cost_buying = st.slider('Cost of Buying', min_value=10., max_value=100., value=10., step=0.1)
+st.write('Set the probability a researcher will buy access to a paper at each timestep.')
+probability_buying = st.slider('Researcher Probability of Buying', min_value=0., max_value=1., value=0.1, step=0.0001)
+st.write('Set the probability the grant funding agency will disburse funding each round.')
+probability_funding = st.slider('Probability of Disbursing Funding', min_value=0., max_value=1., value=0.9, step=0.0001)
+st.write('Set the number of timesteps in the simulation.')
+timesteps = st.slider('Timesteps', min_value=10, max_value=1000, value=100, step=1)
+
+initial_state = {
+    'funding_pool': funding_pool,
+    'researcher_value': researcher_value,
+    'journal_value': journal_value,
+    'losses': 0
+}
+
+system_params = {
+    'funding_pool': [funding_pool],
+    'funding_round': [funding_round],
+    'alpha': [alpha],
+    'beta': [beta],
+    'epsilon': [epsilon],
+    'cost_publishing': [cost_publishing],
+    'cost_buying': [cost_buying],
+    'probability_buying': [probability_buying],
+    'probability_funding': [probability_funding]
+}
+
+def generate_sim_config(monte_carlo_runs=1,
+                   timesteps=timesteps,
+                   system_params=system_params):
+    sim_config = config_sim({
+        'N': monte_carlo_runs, # the number of times we'll run the simulation ("Monte Carlo runs")
+        'T': range(timesteps), # the number of timesteps the simulation will run for
+        'M': system_params # the parameters of the system
+    })
+
+    return sim_config
+
+def configure_experiment(initial_state,
+                      partial_state_update_blocks,
+                      sim_config):
+    experiment = Experiment()
+    experiment.append_configs(
+        initial_state=initial_state,
+        partial_state_update_blocks=partial_state_update_blocks,
+        sim_configs=sim_config
+    )
+
+    return experiment
+
+partial_state_update_blocks = [
+    {
+        'policies': {
+            'p_value_flow': p_value_flow
+        },
+        'variables': {
+            'funding_pool': s_funding_pool,
+            'researcher_value': s_researcher_value,
+            'journal_value': s_journal_value,
+            'losses': s_losses
+        }
+    }
+]
+
+def execute_simulation(experiment):
+    exec_context = ExecutionContext()
+    configs = experiment.configs
+    simulation = Executor(exec_context=exec_context, configs=configs)
+    raw_result, tensor_field, sessions = simulation.execute()
+
+    return raw_result
+
+if st.button('Run Simulation'):
+    sim_config = generate_sim_config()
+    experiment = configure_experiment(initial_state, partial_state_update_blocks, sim_config)
+    raw_result = execute_simulation(experiment)
+    df = pd.DataFrame(raw_result)
+    fig1 = df.plot(kind='line', x='timestep', y=['funding_pool','researcher_value', 'journal_value'], width=1000)
+    fig2 = df.plot(kind='line', x='timestep', y=['funding_pool', 'losses'], width=1000)
+    st.subheader('Results')
+    st.plotly_chart(fig1)
+    st.plotly_chart(fig2)