Zuza branch

data_generation/create_results.py

@@ -0,0 +1,104 @@
+from ema_workbench import (Model, RealParameter, ScalarOutcome)
+import pandas as pd
+from model.dps_lake_model import lake_model
+
+model = Model('lakeproblem', function=lake_model)
+
+#specify uncertainties
+model.uncertainties = [RealParameter('b', 0.1, 0.45),
+                       RealParameter('q', 2.0, 4.5),
+                       RealParameter('mean', 0.01, 0.05),
+                       RealParameter('stdev', 0.001, 0.005),
+                       RealParameter('delta', 0.93, 0.99)]
+
+# set levers
+model.levers = [RealParameter("c1", -2, 2),
+                RealParameter("c2", -2, 2),
+                RealParameter("r1", 0, 2),
+                RealParameter("r2", 0, 2),
+                RealParameter("w1", 0, 1)]
+
+#specify outcomes
+# note how we need to explicitely indicate the direction
+model.outcomes = [ScalarOutcome('max_P', kind=ScalarOutcome.MINIMIZE),
+                  ScalarOutcome('utility', kind=ScalarOutcome.MAXIMIZE),
+                  ScalarOutcome('inertia', kind=ScalarOutcome.MAXIMIZE),
+                  ScalarOutcome('reliability', kind=ScalarOutcome.MAXIMIZE)]
+
+
+
+
+#do the optimization-simulation
+from ema_workbench import MultiprocessingEvaluator, ema_logging
+from ema_workbench.em_framework.evaluators import BaseEvaluator
+
+ema_logging.log_to_stderr(ema_logging.INFO)
+
+with MultiprocessingEvaluator(model) as evaluator:
+    results1 = evaluator.optimize(nfe=5e5, searchover='levers',
+                                 epsilons=[0.1,]*len(model.outcomes))
+
+
+#save the results - only objective values
+results1.to_csv('results.csv')
+
+#get the policies - values of parameters for each of the rbfs
+policies = results1.iloc[:, :5]
+
+
+
+from model.dps_lake_model import get_antropogenic_release
+import numpy as np
+import math
+
+
+
+#create empty data frame fro the time series of release decisions
+release_decisions = pd.DataFrame()
+
+
+
+#for each policy, simulate the decisions over time
+for policy in range(len(policies)):
+    c1 = policies.iloc[policy, 0]
+    c2 = policies.iloc[policy, 1]
+    r1 = policies.iloc[policy, 2]
+    r2 = policies.iloc[policy, 3]
+    w1 = policies.iloc[policy, 4]
+
+    myears = 100
+
+    b=0.42
+    q=2.0
+    mean=0.02
+    stdev=0.001
+    delta=0.98
+    alpha=0.4
+    nsamples=100
+    myears=100
+    X = np.zeros((myears,))
+
+    X[0] = 0.0
+    decision = 0.1
+    decisions = np.zeros(myears,)
+    decisions[0] = decision
+    natural_inflows = np.random.lognormal(
+                math.log(mean**2 / math.sqrt(stdev**2 + mean**2)),
+                math.sqrt(math.log(1.0 + stdev**2 / mean**2)),
+                size=myears)
+
+    for t in range(1, myears):
+
+        # here we use the decision rule
+        decision = get_antropogenic_release(X[t-1], c1, c2, r1, r2, w1)
+        decisions[t] = decision
+
+        X[t] = (1-b)*X[t-1] + X[t-1]**q/(1+X[t-1]**q) + decision +\
+            natural_inflows[t-1]
+
+    release_decisions[str(policy)] = pd.DataFrame(decisions)
+
+
+#save release decisions as time series for each of the policy
+release_decisions.to_csv('release_decisions.csv')
+

data_generation/release_decisions.csv

@@ -0,0 +1,101 @@
+,0,1,2,3,4,5,6,7
+0,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+1,0.1,0.06557875165232617,0.03309879128122532,0.1,0.049565267100535965,0.02185296093697179,0.08569149270420635,0.1
+2,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+3,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+4,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+5,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+6,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+7,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+8,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+9,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+10,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+11,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+12,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+13,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+14,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+15,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+16,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+17,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+18,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+19,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+20,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+21,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+22,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+23,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+24,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+25,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+26,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+27,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+28,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+29,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+30,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+31,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+32,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+33,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+34,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+35,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+36,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+37,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+38,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+39,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+40,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+41,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+42,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+43,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+44,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+45,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+46,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+47,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+48,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+49,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+50,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+51,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+52,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+53,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+54,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+55,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+56,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+57,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+58,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+59,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+60,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+61,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+62,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+63,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+64,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+65,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+66,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+67,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+68,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+69,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+70,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+71,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+72,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+73,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+74,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+75,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+76,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+77,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+78,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+79,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+80,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+81,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+82,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+83,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+84,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+85,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+86,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+87,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+88,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+89,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+90,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+91,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+92,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+93,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+94,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+95,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+96,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+97,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+98,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1
+99,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1

data_generation/results.csv

@@ -0,0 +1,9 @@
+,c1,c2,r1,r2,w1,max_P,utility,inertia,reliability
+0,0.05422634381052014,-1.3829692124724173,0.0006180037012011863,1.3180615770463042,0.9487422654053417,2.060713668881164,1.7000436121480709,0.0519,0.5536
+1,-0.04342079540283349,-1.2131637037821896,0.12817785893471817,1.4825306991966192,0.9475426849035579,1.8799544352620934,1.5028891651180087,0.0197,0.5889
+2,-0.0165640435728814,1.1883445038586118,0.10214973885067827,1.4617018004259805,0.9459083436246151,1.3666683170869716,1.300086331187123,0.0197,0.7626
+3,0.054993827185723605,-0.6177370031804852,0.002308100662628938,1.211018235404881,0.9455220076474259,2.060616310880478,1.4432930857929192,0.2027,0.5536
+4,0.02008913171794783,-0.7874109771928711,0.06707445133161025,0.9763389017306744,0.9543930211452343,1.7463670088417103,1.4029019726736158,0.0197,0.6253
+5,-0.03847870004852087,-0.10088119825376962,0.12922382479171207,1.0906667232814582,0.822383921107195,0.1552007742978849,1.2298680723696087,0.0197,1.0
+6,0.0244456886843499,-0.6205744310780451,0.10036982209437494,1.1613880661849278,0.4841718944361656,1.9933616252886157,1.6284718578564088,0.0001,0.5629
+7,0.054993827185723605,-1.1593201946757956,0.0017663454196513453,1.1594794826157335,0.9482664000969719,2.060454410759735,1.643987930822998,0.1335,0.5536

plotly_dash.py

@@ -0,0 +1,122 @@
+import plotly.express as px
+from dash import Dash, dcc, html
+
+import pandas as pd
+from dash import Dash, dcc, html, Input, Output
+import pandas as pd
+
+from dash import Dash, dcc, html, Input, Output, dash_table
+
+import plotly.express as px
+
+import pandas as pd
+import plotly.graph_objects as go
+
+
+
+results = pd.read_csv('data_generation/results.csv').iloc[:, -4:]
+release_decisions = pd.read_csv('data_generation/release_decisions.csv').iloc[:,1:]
+fig = px.parallel_coordinates(results)
+
+fig_decisions = px.line(release_decisions)
+
+app = Dash()
+
+
+
+app.layout = html.Div([
+
+    dcc.Graph(
+
+        id='parallel_coordinates',
+
+        figure=fig
+
+    ),
+
+
+
+    dash_table.DataTable(
+
+        id='datatable',
+
+        columns=[{"name": i, "id": i, "deletable": True, "selectable": True} for i in results.columns],
+
+        data=results.to_dict('records'),
+
+        editable=True,
+
+        filter_action="native",
+
+        sort_action="native",
+
+        sort_mode="multi",
+
+        column_selectable="single",
+
+        row_selectable="multi",
+
+        row_deletable=True,
+
+        selected_columns=[],
+
+        selected_rows=[],
+
+        page_action="native",
+
+        page_current=0,
+
+        page_size=10,
+
+    ),
+        dcc.Graph(
+
+        id='time_series_decisions',
+
+        figure=fig_decisions
+
+    ),
+
+])
+
+
+
+# Handle row selections:
+
+
+
+
+@app.callback(
+    Output('parallel_coordinates', 'figure'),
+    [Input('datatable', 'selected_rows')]
+)
+
+
+def update_graph(selected_rows):
+    if sum(results.index.isin(selected_rows).astype(int)) == 0:
+        res = results 
+    else:
+        res = results[results.index.isin(selected_rows)]
+    fig = px.parallel_coordinates(
+        res
+    )
+    return fig
+
+@app.callback(
+    Output('time_series_decisions', 'figure'),
+    [Input('datatable', 'selected_rows')]
+)
+
+
+def update_graph_release(selected_rows):
+    if sum(results.index.isin(selected_rows).astype(int)) == 0:
+        res = release_decisions 
+    else:
+        res = release_decisions.iloc[:, selected_rows]
+    fig_decisions = px.line(res)
+    return fig_decisions
+
+
+
+if __name__ == '__main__':
+    app.run_server(debug=True, use_reloader=False)