delta refinements

Author: dantzert

baseline_controllers/delta/compare_timeseries.py

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+from cProfile import label
+import pystorms
+import pyswmm
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import dill as pickle
+import datetime
+import networkx as nx
+import matplotlib.patches as mpatches
+from matplotlib.gridspec import GridSpec
+import os
+
+# DELTA SCENARIO
+version = "2"
+level = "1"
+# set the working directory to the directory of this script
+os.chdir(os.path.dirname(os.path.abspath(__file__)))
+print(os.getcwd())
+env = pystorms.scenarios.delta(version=version,level=level)
+env.env.sim.start()
+
+
+# choose parameters for the plot
+static_plus_rule_param = "0.0"
+prop_outflow_param = "0.0"
+
+static_plus_rule_data_log = pd.read_pickle(str("./v" + version + "/lev" + level + "/results/static-plus-rule_param=" + str(static_plus_rule_param) + "_data_log.pkl"))
+prop_outflow_data_log = pd.read_pickle(str("./v" + version + "/lev" + level + "/results/prop-outflow_param=" + str(prop_outflow_param) + "_data_log.pkl"))
+uncontrolled_data_log = pd.read_pickle(str("./v" + version + "/results/uncontrolled_data_log.pkl"))
+
+# print the costs
+print("Costs")
+print("uncontrolled: ", "{:.2E}".format(sum(uncontrolled_data_log['performance_measure'])))
+print("Static+Rule: ", "{:.2E}".format(sum(static_plus_rule_data_log['performance_measure'])))
+print("Prop Outflow: ", "{:.2E}".format(sum(prop_outflow_data_log['performance_measure'])))
+
+# load the actions and states
+uncontrolled_actions = pd.read_csv("./v" + version + "/results/actions_uncontrolled.csv",index_col=0,parse_dates=True)
+uncontrolled_states = pd.read_csv("./v" + version + "/results/states_uncontrolled.csv",index_col=0,parse_dates=True)
+static_plus_rule_actions = pd.read_csv(str("./v" + version + "/lev" + level + "/results/actions_static-plus-rule_param=" + static_plus_rule_param + ".csv"),index_col=0,parse_dates=True)
+static_plus_rule_states = pd.read_csv(str("./v" + version + "/lev" + level + "/results/states_static-plus-rule_param=" + static_plus_rule_param + ".csv"),index_col=0,parse_dates=True)
+prop_outflow_actions = pd.read_csv(str("./v" + version + "/lev" + level + "/results/actions_prop-outflow_param=" + prop_outflow_param + ".csv"),index_col=0,parse_dates=True)
+prop_outflow_states = pd.read_csv(str("./v" + version + "/lev" + level + "/results/states_prop-outflow_param=" + prop_outflow_param + ".csv"),index_col=0,parse_dates=True)
+
+operational_bounds_data = {
+    "Lower Limit": [5.32, 4.44, 5.20, 3.28, None],
+    "Upper Limit": [5.92, 5.04, 5.80, 3.80, 6.55]
+}
+
+operational_bounds_df = pd.DataFrame(operational_bounds_data, index=["N3", "N2", "N1", "C", "S"])
+
+exceedance_bounds_data = {
+    "Lower Limit": [5.28, 4.04, 2.11, 2.21, None],
+    "Upper Limit": [11.99, 6.59, 5.92, 5.70, 9.55]
+}
+
+exceedance_bounds_df = pd.DataFrame(exceedance_bounds_data, index=["N3", "N2", "N1", "C", "S"])
+    
+
+# record total outflows under each control scenario
+static_plus_rule_total_outflows = sum(static_plus_rule_data_log['flow']['conduit_Eout'])
+prop_outflow_total_outflows = sum(prop_outflow_data_log['flow']['conduit_Eout'])
+uncontrolled_total_outflows = sum(uncontrolled_data_log['flow']['conduit_Eout'])
+# print those total outflows
+print("Total outflows from system through Eout")
+print("Static+Rule: ", "{:.2E}".format(static_plus_rule_total_outflows))
+print("Prop Outflow: ", "{:.2E}".format(prop_outflow_total_outflows))
+print("Uncontrolled: ", "{:.2E}".format(uncontrolled_total_outflows))
+
+# print the final depths in each storage asset under each control scenario
+print("final depths in storage basins")
+print("Static+Rule: \n", static_plus_rule_states.iloc[-1,:])
+print("Prop Outflow: \n", prop_outflow_states.iloc[-1,:])
+print("Uncontrolled: \n", uncontrolled_states.iloc[-1,:])
+
+# print the last date
+print("last timestep")
+print("static+rule: ", static_plus_rule_data_log['simulation_time'][-1])
+print("prop outflow: ", prop_outflow_data_log['simulation_time'][-1])
+print("uncontrolled: ", uncontrolled_data_log['simulation_time'][-1])
+print("number of timesteps")
+print("static+rule: ", len(static_plus_rule_data_log['simulation_time']))
+print("prop outflow: ", len(prop_outflow_data_log['simulation_time']))
+print("uncontrolled: ", len(uncontrolled_data_log['simulation_time']))
+
+plots_high = max(len(env.config['action_space']) , len(env.config['states']))
+fig, axes = plt.subplots(plots_high, 2, figsize=(10,2*plots_high))
+
+axes[0,0].set_title("actions")
+axes[0,1].set_title("states")
+# plot the actions
+for idx in range(len(env.config['action_space'])):
+    #axes[idx,0].plot(weir_heads32.iloc[:,idx])
+    #axes[idx,0].set_ylabel("(weir head [ft])^(3/2)")
+    axes[idx,0].plot(static_plus_rule_actions.index, static_plus_rule_actions[env.config['action_space'][idx]], label='Static+Rule',color='blue',alpha=0.6)
+    axes[idx,0].plot(prop_outflow_actions.index, prop_outflow_actions[env.config['action_space'][idx]], label='Prop Outflow',color='red',alpha=0.6)
+    axes[idx,0].plot(uncontrolled_actions.index, uncontrolled_actions[env.config['action_space'][idx]], label='Uncontrolled',color='black',alpha=0.6)
+    axes[idx,0].set_ylabel("fraction open")
+    if idx == len(env.config['action_space']) - 1:
+        axes[idx,0].set_xlabel("time")
+        # plot only the first, middle, and last x-ticks
+        xticks = axes[idx,0].get_xticks()
+        xticks = [xticks[0],xticks[int(len(xticks)/2)],xticks[-1]]
+        axes[idx,0].set_xticks(xticks)
+    if idx != len(env.config['action_space']) - 1: # not the last row
+        axes[idx,0].set_xticklabels([])
+    axes[idx,0].annotate(str(env.config['action_space'][idx]), xy=(0.5, 0.8), xycoords='axes fraction', ha='center', va='center',fontsize='xx-large')
+# plot flows through Eout
+#axes[-1,0].plot(env.data_log['flow']['conduit_Eout'])
+axes[-1,0].plot(uncontrolled_data_log['simulation_time'],uncontrolled_data_log['flow']['conduit_Eout'],label="uncontrolled",color='black',alpha=0.6)
+axes[-1,0].plot(static_plus_rule_data_log['simulation_time'],static_plus_rule_data_log['flow']['conduit_Eout'],label="static+rule",color='blue',alpha=0.6)
+axes[-1,0].plot(prop_outflow_data_log['simulation_time'],prop_outflow_data_log['flow']['conduit_Eout'],label="prop outflow",color='red',alpha=0.6)
+axes[-1,0].set_ylabel("flow")
+axes[-1,0].set_xlabel("time")
+# add a dotted red line at threshold
+axes[-1,0].axhline(y=env.threshold, color='r', linestyle='dotted')
+
+
+# plot the states
+for idx, state in enumerate(env.config['states']):
+    axes[idx,1].plot(uncontrolled_states.index, uncontrolled_states[str(env.config['states'][idx])], label='Uncontrolled',color='black',alpha=0.6)
+    axes[idx,1].plot(static_plus_rule_states.index, static_plus_rule_states[str(env.config['states'][idx])], label='Static+Rule',color='blue',alpha=0.6)
+    axes[idx,1].plot(prop_outflow_states.index, prop_outflow_states[str(env.config['states'][idx])], label='Prop Outflow',color='red',alpha=0.6)
+
+    # add bound lines (dotted blue operational, solid red exceedance)
+    if '4' not in state[0]: # basin N4 isn't controlled and doesn't have bounds
+        if "S" not in state[0]: # basin S doesn't have lower bounds defined
+            axes[idx,1].axhline(y=operational_bounds_df.loc[state[0][6:],'Lower Limit']  , color='b', linestyle='dotted')
+            axes[idx,1].axhline(y=exceedance_bounds_df.loc[state[0][6:],'Lower Limit']  , color='r')
+                
+        axes[idx,1].axhline(y=operational_bounds_df.loc[state[0][6:],'Upper Limit']  , color='b', linestyle='dotted')
+        axes[idx,1].axhline(y=exceedance_bounds_df.loc[state[0][6:],'Upper Limit']  , color='r')
+
+    if idx == len(env.config['states']) - 1:
+        axes[idx,1].set_xlabel("time")
+        axes[idx,1].annotate(str(env.config['states'][idx]), xy=(0.5, 0.4), xycoords='axes fraction', ha='center', va='center',fontsize='xx-large')
+
+        # plot only the first, middle, and last x-ticks
+        xticks = axes[idx,1].get_xticks()
+        xticks = [xticks[0],xticks[int(len(xticks)/2)],xticks[-1]]
+        axes[idx,1].set_xticks(xticks)
+
+    if idx == 2:
+        # put the legend in middle of axes
+        axes[idx,1].legend()
+    
+    if idx != len(env.config['states']) - 1:
+        axes[idx,1].set_xticklabels([])
+        axes[idx,1].annotate(str(env.config['states'][idx]), xy=(0.5, 0.8), xycoords='axes fraction', ha='center', va='center',fontsize='xx-large')
+
+
+unc_perf = sum(uncontrolled_data_log['performance_measure'])
+prop_perf = sum(prop_outflow_data_log['performance_measure'])
+static_perf = sum(static_plus_rule_data_log['performance_measure'])
+
+perfstr = "Cost Difference from Uncontrolled\nStatic+Rule = {:+.1%}\nProp-Outflow = {:+.1%}".format((static_perf - unc_perf)/unc_perf, (prop_perf - unc_perf)/unc_perf)
+axes[-2,1].annotate(perfstr, xy=(0.5, 0.45), xycoords='axes fraction', ha='center', va='center',fontsize='large')
+
+
+plt.tight_layout()
+# only going to use one plot for level (at most) so don't worry about tracking parameters
+plt.savefig(str("./v" + version + "/lev" + level + "/actions_states.png")) 
+plt.savefig(str("./v" + version + "/lev" + level + "/actions_states.svg"))
+#plt.show()
+plt.close('all')