def action_distrubtion(episode, ref_episode):
actions_per_sub = actions_model.get_action_per_sub(episode)
actions_per_sub.append(actions_model.get_action_per_sub(ref_episode)[0])
y_max = None
if len(actions_per_sub[0]["y"]) == 0:
figure_subs = go.Figure(
layout=layout_no_data("No Actions on subs for this Agent"))
else:
figure_subs = go.Figure(layout=layout_def, data=actions_per_sub)
y_max = max(map(max_or_zero, [trace.y
for trace in actions_per_sub])) + 1
actions_per_lines = actions_model.get_action_per_line(episode)
actions_per_lines.append(actions_model.get_action_per_line(ref_episode)[0])
if len(actions_per_lines[0]["y"]) == 0:
figure_lines = go.Figure(
layout=layout_no_data("No Actions on lines for this Agent"))
else:
figure_lines = go.Figure(layout=layout_def, data=actions_per_lines)
if y_max is None:
y_max = max(
map(max_or_zero, [trace.y for trace in actions_per_lines])) + 1
if max(map(max_or_zero, [trace.y
for trace in actions_per_lines])) > y_max:
y_max = max(
map(max_or_zero, [trace.y for trace in actions_per_lines])) + 1
actions_redisp = actions_model.get_action_redispatch(episode)
actions_redisp.append(actions_model.get_action_redispatch(ref_episode)[0])
if len(actions_redisp[0]["y"]) == 0:
figure_redisp = go.Figure(
layout=layout_no_data("No redispatching actions for this Agent"))
else:
figure_redisp = go.Figure(layout=layout_def, data=actions_redisp)
if y_max is None:
y_max = max(map(max_or_zero, [trace.y
for trace in actions_redisp])) + 1
if max(map(max_or_zero, [trace.y
for trace in actions_redisp])) > y_max:
y_max = max(map(max_or_zero, [trace.y
for trace in actions_redisp])) + 1
if y_max:
figure_subs.update_yaxes(range=[0, y_max])
figure_lines.update_yaxes(range=[0, y_max])
figure_redisp.update_yaxes(range=[0, y_max])
return ActionsDistribution(on_subs=figure_subs,
on_lines=figure_lines,
redisp=figure_redisp)
def update_layout(predicate, msg):
if predicate:
figure_layout = layout_no_data(msg)
else:
figure_layout = layout_def
return figure_layout
def action_distrubtion(episode):
actions_per_sub = actions_model.get_action_per_sub(episode)
if len(actions_per_sub[0]["y"]) == 0:
figure_subs = go.Figure(
layout=layout_no_data("No Actions on subs for this Agent"))
else:
figure_subs = go.Figure(layout=layout_def, data=actions_per_sub)
actions_per_lines = actions_model.get_action_per_line(episode)
if len(actions_per_lines[0]["y"]) == 0:
figure_lines = go.Figure(
layout=layout_no_data("No Actions on lines for this Agent"))
else:
figure_lines = go.Figure(layout=layout_def, data=actions_per_lines)
return ActionsDistribution(on_subs=figure_subs, on_lines=figure_lines)
def update_interactive_graph(slider_value, active_tab, agent_study,
scenario, agent_ref):
episode = make_episode(
agent_study if active_tab == "tab-0" else agent_ref, scenario)
try:
act = episode.actions[slider_value]
except Grid2OpException as ex:
return (
dcc.Graph(figure=go.Figure(layout=layout_no_data(
"The agent is game over at this time step."))),
"",
)
if any(act.get_types()):
act_as_str = str(act)
else:
act_as_str = "NO ACTION"
return (
dcc.Graph(figure=make_network_agent_study(episode,
timestep=slider_value)),
act_as_str,
)
def indicator_line(scenario, study_agent, ref_agent):
episode = make_episode(study_agent, scenario)
ref_episode = make_episode(ref_agent, scenario)
figures_distribution = action_distrubtion(episode, ref_episode)
modified_lines = actions_model.get_modified_lines(episode)
line_values = [None] * episode.n_lines
for line in modified_lines.index:
line_values[np.where(episode.line_names == line)[0][0]] = line
network_graph = make_network(episode).plot_info(
observation=episode.observations[0],
line_values=line_values,
)
nb_actions = episode.action_data_table[[
'action_line', 'action_subs', 'action_redisp'
]].sum()
if nb_actions.sum() == 0:
pie_figure = go.Figure(
layout=layout_no_data("No Actions for this Agent"))
else:
pie_figure = go.Figure(layout=layout_def,
data=[
go.Pie(labels=[
"Actions on Lines",
"Actions on Substations",
"Redispatching Actions"
],
values=[
nb_actions["action_line"],
nb_actions["action_subs"],
nb_actions["action_redisp"]
])
])
return html.Div(
className="lineBlock card",
children=[
html.H4("Indicators"),
html.Div(
className="card-body row",
children=[
html.Div(
className="col-2",
children=[
html.H5("Agent to study Selection"),
dcc.Dropdown(id='agent_log_selector',
options=[{
'label': agent,
'value': agent
} for agent in agents],
value=study_agent,
placeholder="Agent log"),
html.Div(
className="m-2",
children=[
html.
P(id="indicator_score_output",
className=
"border-bottom h3 mb-0 text-right",
children=round(
episode.meta["cumulative_reward"])),
html.P(className="text-muted",
children="Score")
]),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_nb_maintenances",
className=
"border-bottom h3 mb-0 text-right",
children='{}/{}'.format(
episode.meta['nb_timestep_played'],
episode.
meta['chronics_max_timestep'])),
html.P(className="text-muted",
children="Agent's Survival")
]),
html.Div(
className="m-2",
children=[
html.P(id="indicator_nb_overflow",
className=
"border-bottom h3 mb-0 text-right",
children=episode.
total_overflow_ts["value"].sum()),
html.P(className="text-muted",
children="Number of Overflow")
]),
html.Div(
className="m-2",
children=[
html.P(id="indicator_nb_action",
className=
"border-bottom h3 mb-0 text-right",
children=episode.action_data_table[[
'action_line', 'action_subs'
]].sum(axis=1).sum()),
html.P(className="text-muted ",
children="Number of Action")
]),
html.Div(
className="m-2",
children=[
html.P(id="indicator_nb_maintenances",
className=
"border-bottom h3 mb-0 text-right",
children=episode.nb_maintenances),
html.P(className="text-muted",
children="Number of Maintenances")
]),
]),
html.Div(className="col-3",
children=[
html.H6(className="text-center",
children="Type Action Repartition"),
dcc.Graph(id="agent_study_pie_chart",
figure=pie_figure)
]),
html.Div(className="col-7",
children=[
html.H6(
className="text-center",
children="//WIP// Impacted grid assets"),
dcc.Graph(id="network_actions",
figure=network_graph)
]),
html.Div(className="col-12",
children=[
html.H2(className="text-center",
children="Actions Distributions")
]),
html.Div(className="col-4",
children=[
html.H6(className="text-center",
children="On Substations"),
dcc.Graph(
id="distribution_substation_action_chart",
figure=figures_distribution.on_subs)
]),
html.Div(className="col-4",
children=[
html.H6(className="text-center",
children="On Lines"),
dcc.Graph(
id="distribution_line_action_chart",
figure=figures_distribution.on_lines)
]),
html.Div(className="col-4",
children=[
html.H6(className="text-center",
children="Redispatching"),
dcc.Graph(
id="distribution_redisp_action_chart",
figure=figures_distribution.redisp)
]),
]),
])
def indicator_line(scenario, study_agent, ref_agent):
episode = make_episode(study_agent, scenario)
ref_episode = make_episode(ref_agent, scenario)
figures_distribution = action_distrubtion(episode, ref_episode)
network_graph = make_network_agent_overview(episode)
nb_actions = episode.action_data_table[[
"action_line", "action_subs", "action_redisp"
]].sum()
if nb_actions.sum() == 0:
pie_figure = go.Figure(
layout=layout_no_data("No Actions for this Agent"))
else:
pie_figure = go.Figure(
layout=layout_def,
data=[
go.Pie(
labels=[
"Actions on Lines",
"Actions on Substations",
"Redispatching Actions",
],
values=[
nb_actions["action_line"],
nb_actions["action_subs"],
nb_actions["action_redisp"],
],
)
],
)
return html.Div(
className="lineBlock card",
children=[
html.H4("Indicators"),
html.Div(
className="card-body row",
children=[
html.Div(
className="col-2",
children=[
html.H5("Study Agent Summary"),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_score_output",
className=
"border-bottom h3 mb-0 text-right",
children=round(
episode.meta["cumulative_reward"]),
),
html.P(className="text-muted",
children="Score"),
],
),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_survival_time",
className=
"border-bottom h3 mb-0 text-right",
children="{}/{}".format(
episode.meta["nb_timestep_played"],
episode.
meta["chronics_max_timestep"],
),
),
html.P(
className="text-muted",
children="Agent's Survival",
),
],
),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_nb_overflow",
className=
"border-bottom h3 mb-0 text-right",
children=episode.
total_overflow_ts["value"].sum(),
),
html.P(
className="text-muted",
children="Number of Overflow",
),
],
),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_nb_action",
className=
"border-bottom h3 mb-0 text-right",
children=episode.action_data_table[[
"action_line", "action_subs"
]].sum(axis=1).sum(),
),
html.P(
className="text-muted ",
children="Number of Action",
),
],
),
html.Div(
className="m-2",
children=[
html.P(
id="indicator_nb_maintenances",
className=
"border-bottom h3 mb-0 text-right",
children=episode.nb_maintenances,
),
html.P(
className="text-muted",
children="Number of Maintenances",
),
],
),
],
),
html.Div(
className="col-3",
children=[
html.H6(
className="text-center",
children="Type Action Repartition",
),
dcc.Graph(id="agent_study_pie_chart",
figure=pie_figure),
],
),
html.Div(
className="col-7",
children=[
html.H6(
className="text-center",
children=
"Impacted grid assets: attacks (dash orange) & overflow (red) and subs with action",
),
dcc.Graph(id="network_actions",
figure=network_graph),
],
),
html.Div(
className="col-12",
children=[
html.H2(
className="text-center",
children="Actions Distributions",
)
],
),
html.Div(
className="col-4",
children=[
html.H6(className="text-center",
children="On Substations"),
dcc.Graph(
id="distribution_substation_action_chart",
figure=figures_distribution.on_subs,
),
],
),
html.Div(
className="col-4",
children=[
html.H6(className="text-center",
children="On Lines"),
dcc.Graph(
id="distribution_line_action_chart",
figure=figures_distribution.on_lines,
),
],
),
html.Div(
className="col-4",
children=[
html.H6(className="text-center",
children="Redispatching"),
dcc.Graph(
id="distribution_redisp_action_chart",
figure=figures_distribution.redisp,
),
],
),
],
),
],
)
def indicator_line(scenario, study_agent):
episode = make_episode(study_agent, scenario)
nb_actions = episode.action_data_table[['action_line',
'action_subs']].sum()
if nb_actions["action_line"] == 0 and nb_actions["action_subs"] == 0:
pie_figure = go.Figure(
layout=layout_no_data("No Actions for this Agent"))
else:
pie_figure = go.Figure(
layout=layout_def,
data=[
go.Pie(labels=["Actions on Lines", "Actions on Substations"],
values=[
nb_actions["action_line"], nb_actions["action_subs"]
])
])
maintenances_data = hist_duration_maintenances(episode)
if not maintenances_data:
maintenance_figure = go.Figure(
layout=layout_no_data("No Maintenances for this scenario"))
else:
maintenance_figure = go.Figure(
layout=layout_def, data=[go.Histogram(x=maintenances_data)])
maintenance_figure["layout"]["xaxis"]["rangemode"] = "tozero"
return html.Div(
className="lineBlock card",
children=[
html.H4("Indicators"),
html.Div(
className="card-body row",
children=[
html.Div(
className="col-2",
children=[
dcc.Dropdown(id='agent_log_selector',
options=[{
'label': agent,
'value': agent
} for agent in agents],
value=study_agent,
placeholder="Agent log"),
html.Div(
className="m-2",
children=[
html.
P(id="indicator_score_output",
className=
"border-bottom h3 mb-0 text-right",
children=round(
episode.meta["cumulative_reward"])),
html.P(className="text-muted",
children="Score")
]),
html.Div(
className="m-2",
children=[
html.P(id="indicator_nb_overflow",
className=
"border-bottom h3 mb-0 text-right",
children=episode.
total_overflow_ts["value"].sum()),
html.P(className="text-muted",
children="Number of Overflow")
]),
html.Div(
className="m-2",
children=[
html.P(id="indicator_nb_action",
className=
"border-bottom h3 mb-0 text-right",
children=episode.action_data_table[[
'action_line', 'action_subs'
]].sum(axis=1).sum()),
html.P(className="text-muted ",
children="Number of Action")
])
]),
html.Div(className="col-3",
children=[
html.H6(className="text-center",
children="Type Action Repartition"),
dcc.Graph(id="agent_study_pie_chart",
figure=pie_figure)
]),
html.Div(
className="col-7",
children=[
html.
H6(className="text-center",
children=
"Action Maintenance Duration (on the whole episode)"
),
dcc.Graph(id="maintenance_duration",
figure=maintenance_figure)
])
]),
])