def load_reward_data_scatter(study_agent, relayout_data_store, rew_figure,
cumrew_figure, ref_agent, scenario,
agent_study_ts, relayoutStoreMacro_ts):
"""Compute and create figure with instant and cumulated rewards of the study and ref agent"""
rew_layout = rew_figure["layout"]
cumrew_layout = cumrew_figure["layout"]
if agent_study_ts is not None and relayoutStoreMacro_ts is not None:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"]
and relayoutStoreMacro_ts > agent_study_ts)
else:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
rew_new_axis_layout = get_axis_relayout(rew_figure, relayout_data)
cumrew_new_axis_layout = get_axis_relayout(cumrew_figure,
relayout_data)
if rew_new_axis_layout is not None or cumrew_new_axis_layout is not None:
if rew_new_axis_layout is not None:
rew_layout.update(rew_new_axis_layout)
if cumrew_new_axis_layout is not None:
cumrew_layout.update(cumrew_new_axis_layout)
return rew_figure, cumrew_figure
return make_rewards_ts(study_agent, ref_agent, scenario, rew_layout,
cumrew_layout)
def load_reward_ts(relayout_data_store, window, selected_timestamp, rew_figure,
cumrew_figure, study_agent, agent_ref, scenario):
rew_layout = rew_figure["layout"]
cumrew_layout = cumrew_figure["layout"]
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
rew_new_axis_layout = get_axis_relayout(rew_figure, relayout_data)
cumrew_new_axis_layout = get_axis_relayout(cumrew_figure, relayout_data)
if rew_new_axis_layout is not None or cumrew_new_axis_layout is not None:
if rew_new_axis_layout is not None:
rew_layout.update(rew_new_axis_layout)
if cumrew_new_axis_layout is not None:
cumrew_layout.update(cumrew_new_axis_layout)
return rew_figure, cumrew_figure
rew_figure, cumrew_figure = common_graph.make_rewards_ts(study_agent, agent_ref, scenario, rew_layout, cumrew_layout)
if window is not None:
rew_figure["layout"].update(
xaxis=dict(range=window, autorange=False)
)
cumrew_figure["layout"].update(
xaxis=dict(range=window, autorange=False)
)
return rew_figure, cumrew_figure
def load_environments_ts(equipments, relayout_data_store, figure, kind, scenario):
"""
Load selected kind of environment for chosen equipments in a scenario.
Triggered when user click on a equipment displayed in the
input_assets_selector in the overview layout.
"""
if relayout_data_store is not None and relayout_data_store['relayout_data']:
relayout_data = relayout_data_store["relayout_data"]
layout = figure["layout"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
if kind is None:
return figure
if isinstance(equipments, str):
equipments = [equipments] # to make pd.series.isin() work
figure['data'] = common_graph.environment_ts_data(
kind,
make_episode(best_agents[scenario]['agent'], scenario),
equipments
)
return figure
def update_agent_ref_graph(
relayout_data_store,
window,
figure_overflow,
figure_usage,
study_agent,
scenario,
):
if relayout_data_store is not None and relayout_data_store[
"relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout_usage = figure_usage["layout"]
new_axis_layout = get_axis_relayout(figure_usage, relayout_data)
if new_axis_layout is not None:
layout_usage.update(new_axis_layout)
figure_overflow["layout"].update(new_axis_layout)
return figure_overflow, figure_usage
if window is not None:
figure_overflow["layout"].update(
xaxis=dict(range=window, autorange=False))
figure_usage["layout"].update(
xaxis=dict(range=window, autorange=False))
return common_graph.agent_overflow_usage_rate_trace(
make_episode(study_agent, scenario), figure_overflow, figure_usage)
def load_flow_voltage_graph(
selected_objects,
choice,
relayout_data_store,
window,
figure,
study_agent,
scenario,
):
if relayout_data_store is not None and relayout_data_store[
"relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout = figure["layout"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
new_episode = make_episode(study_agent, scenario)
if selected_objects is not None:
if choice == "voltage":
figure["data"] = load_voltage_for_lines(
selected_objects, new_episode)
if "flow" in choice:
figure["data"] = load_flows_for_lines(selected_objects,
new_episode)
if "redispatch" in choice:
figure["data"] = load_redispatch(selected_objects, new_episode)
if window is not None:
figure["layout"].update(xaxis=dict(range=window, autorange=False))
return figure
def update_actions_graph(study_agent, relayout_data_store, figure, agent_ref,
scenario):
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout = figure["layout"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
return make_action_ts(study_agent, agent_ref, scenario, figure['layout'])
def load_reward_data_scatter(study_agent, relayout_data_store, figure,
ref_agent, scenario):
"""Compute and create figure with instant and cumulated rewards of the study and ref agent"""
layout = figure["layout"]
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
return make_rewards_ts(study_agent, ref_agent, scenario, layout)
def update_agent_ref_graph(ref_agent, scenario, relayout_data_store,
figure_overflow, figure_usage):
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout_usage = figure_usage["layout"]
new_axis_layout = get_axis_relayout(figure_usage, relayout_data)
if new_axis_layout is not None:
layout_usage.update(new_axis_layout)
figure_overflow["layout"].update(new_axis_layout)
return figure_overflow, figure_usage
return common_graph.agent_overflow_usage_rate_trace(
make_episode(ref_agent, scenario), figure_overflow, figure_usage)
def load_ts(
relayout_data_store,
window,
study_agent,
rew_figure,
cumrew_figure,
actions_figure,
agent_ref,
scenario,
):
figures = [rew_figure, cumrew_figure, actions_figure]
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
relayouted = False
for figure in figures:
axis_layout = get_axis_relayout(figure, relayout_data)
if axis_layout is not None:
figure["layout"].update(axis_layout)
relayouted = True
if relayouted:
return figures
rew_figure, cumrew_figure = common_graph.make_rewards_ts(
study_agent, agent_ref, scenario, rew_figure, cumrew_figure)
actions_figure = common_graph.make_action_ts(study_agent, agent_ref,
scenario,
actions_figure["layout"])
figures = [rew_figure, cumrew_figure, actions_figure]
if window is not None:
start_datetime = dt.datetime.strptime(window[0],
"%Y-%m-%dT%H:%M:%S")
end_datetime = dt.datetime.strptime(window[-1],
"%Y-%m-%dT%H:%M:%S")
for figure in figures:
figure["layout"].update(xaxis=dict(
range=[start_datetime, end_datetime], autorange=False))
return figures
def update_agent_log_graph(study_agent, relayout_data_store, figure_overflow,
figure_usage, scenario):
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout_usage = figure_usage["layout"]
new_axis_layout = get_axis_relayout(figure_usage, relayout_data)
if new_axis_layout is not None:
layout_usage.update(new_axis_layout)
figure_overflow["layout"].update(new_axis_layout)
return figure_overflow, figure_usage
new_episode = make_episode(study_agent, scenario)
figure_overflow["data"] = new_episode.total_overflow_trace
maintenance_trace = EpisodeTrace.get_maintenance_trace(
new_episode, ["total"])[0]
maintenance_trace.update({"name": "Nb of maintenances"})
figure_overflow["data"].append(maintenance_trace)
figure_usage["data"] = new_episode.usage_rate_trace
return figure_overflow, figure_usage
def load_reward_ts(relayout_data_store, window, selected_timestamp, figure,
study_agent, agent_ref, scenario):
layout = figure["layout"]
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
figure = common_graph.make_rewards_ts(study_agent, agent_ref, scenario,
layout)
if window is not None:
figure["layout"].update(xaxis=dict(range=window, autorange=False))
return figure
def update_actions_graph(study_agent, relayout_data_store, figure,
agent_ref, scenario, agent_study_ts,
relayoutStoreMacro_ts):
if agent_study_ts is not None and relayoutStoreMacro_ts is not None:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"]
and relayoutStoreMacro_ts > agent_study_ts)
else:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
layout = figure["layout"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
return make_action_ts(study_agent, agent_ref, scenario,
figure['layout'])
def update_agent_log_graph(study_agent, relayout_data_store,
figure_overflow, figure_usage, scenario,
agent_study_ts, relayoutStoreMacro_ts):
if agent_study_ts is not None and relayoutStoreMacro_ts is not None:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"]
and relayoutStoreMacro_ts > agent_study_ts)
else:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
layout_usage = figure_usage["layout"]
new_axis_layout = get_axis_relayout(figure_usage, relayout_data)
if new_axis_layout is not None:
layout_usage.update(new_axis_layout)
figure_overflow["layout"].update(new_axis_layout)
return figure_overflow, figure_usage
new_episode = make_episode(study_agent, scenario)
figure_overflow["data"] = new_episode.total_overflow_trace.copy()
maintenance_traces = EpisodeTrace.get_maintenance_trace(
new_episode, ["total"])
if len(maintenance_traces) != 0:
maintenance_traces[0].update({"name": "Nb of maintenances"})
figure_overflow["data"].append(maintenance_traces[0])
hazard_traces = EpisodeTrace.get_hazard_trace(new_episode,
["total"]).copy()
if len(hazard_traces) != 0:
hazard_traces[0].update({"name": "Nb of hazards"})
figure_overflow["data"].append(hazard_traces[0])
attacks_trace = EpisodeTrace.get_attacks_trace(new_episode).copy()
if len(attacks_trace) != 0:
attacks_trace[0].update({"name": "Attacks"})
figure_overflow["data"].append(attacks_trace[0])
figure_usage["data"] = new_episode.usage_rate_trace
return figure_overflow, figure_usage
def load_context_data(equipments, relayout_data_store, window, figure, kind,
scenario, agent_study):
if relayout_data_store is not None and relayout_data_store["relayout_data"]:
relayout_data = relayout_data_store["relayout_data"]
layout = figure["layout"]
new_axis_layout = get_axis_relayout(figure, relayout_data)
if new_axis_layout is not None:
layout.update(new_axis_layout)
return figure
if kind is None:
return figure
if isinstance(equipments, str):
equipments = [equipments] # to make pd.series.isin() work
episode = make_episode(agent_study, scenario)
figure['data'] = common_graph.environment_ts_data(kind, episode,
equipments)
if window is not None:
figure["layout"].update(xaxis=dict(range=window, autorange=False))
return figure
def update_synchronized_figures(
agent_study_ts,
ref_agent,
relayout_data_store,
rew_figure,
cumrew_figure,
overflow_figure,
usage_rate_figure,
actions_figure,
scenario,
study_agent,
relayoutStoreMacro_ts,
):
figures = [
rew_figure,
cumrew_figure,
overflow_figure,
usage_rate_figure,
actions_figure,
]
episode = make_episode(study_agent, scenario)
if agent_study_ts is not None and relayoutStoreMacro_ts is not None:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"]
and relayoutStoreMacro_ts > agent_study_ts)
else:
condition = (relayout_data_store is not None
and relayout_data_store["relayout_data"])
if condition:
relayout_data = relayout_data_store["relayout_data"]
relayouted = False
for figure in figures:
axis_layout = get_axis_relayout(figure, relayout_data)
if axis_layout is not None:
figure["layout"].update(axis_layout)
relayouted = True
if relayouted:
return figures
new_reward_fig, new_cumreward_fig = make_rewards_ts(
study_agent, ref_agent, scenario, rew_figure, cumrew_figure)
overflow_figure["data"] = episode.total_overflow_trace.copy()
for event in ["maintenance", "hazard", "attacks"]:
func = getattr(EpisodeTrace, f"get_{event}_trace")
traces = func(episode, ["total"])
if len(traces) > 0:
traces[0].update({"name": event.capitalize()})
overflow_figure["data"].append(traces[0])
usage_rate_figure["data"] = episode.usage_rate_trace
new_action_fig = make_action_ts(study_agent, ref_agent, scenario,
actions_figure["layout"])
return (
new_reward_fig,
new_cumreward_fig,
overflow_figure,
usage_rate_figure,
new_action_fig,
)
