plot.rst

grid2op.PlotGrid

Grid2Op Plotting capabilities (beta)

Objectives

This module contrains all the plotting utilities of grid2op. These utilities can be used in different manners to serve different purposes:

• PlotMatplot allows a more in depth study, with a better overall layout. It uses the well-known matplotlib library to represent the powergrid on the screen.
• PlotPlotly uses plotly library to represent the graph. As opposed to the others, plotly allows dynamic modifications such as zoom in / out. This makes this class particularly suited for in depth study of some powergrid state.

The class PlotMatplot is also used by EpisodeReplay that allows to look at the action taken by the agent pretty easily, and allows easy saving into gif format.

import os
import warnings
import grid2op
from grid2op.Episode import EpisodeReplay
from grid2op.Agent import GreedyAgent, RandomAgent
from grid2op.Runner import Runner
from tqdm import tqdm

path_agents = "agent_pseudo_random"
with warnings.catch_warnings():
    warnings.filterwarnings("ignore")
    env = grid2op.make("rte_case14_realistic")

class CustomRandom(RandomAgent):
    """
    This agent takes 1 random action every 10 time steps.
    """
    def __init__(self, action_space):
        RandomAgent.__init__(self, action_space)
        self.i = 0

    def my_act(self, transformed_observation, reward, done=False):
        if self.i % 10 != 0:
            res = 0
        else:
            res = self.action_space.sample()
        self.i += 1
        return res

# execute this agent on 1 scenario, saving the results
runner = Runner(**env.get_params_for_runner(), agentClass=CustomRandom)
path_agent = os.path.join(path_agents, "RandomAgent")
res = runner.run(nb_episode=1, path_save=path_agent, pbar=tqdm)
# and now reload it and display the "movie" of this scenario
plot_epi = EpisodeReplay(path_agent)
plot_epi.replay_episode(res[0][1], max_fps=2, gif_name="episode")

An possible output will look like this:

Detailed Documentation by class

grid2op.PlotGrid