_, y_std = window_func(x_data, y_data, window, np.std)
y_std *= bounds_scale
print('here 3.1')
y_low = np.clip(y_mean-y_std, clip_low, clip_high)
y_high = np.clip(y_mean+y_std, clip_low, clip_high)
print('here 4.1')
print('here 5')
plt.fill_between(x_trimmed, y_low, y_high, alpha=0.2)
plt.plot(x_trimmed, y_mean, **kwargs)
if __name__ == '__main__':
# Parse command line arguments.
parser = argparse.ArgumentParser()
parser.add_argument(
'directory', type=str, help='The directory of the experiment.')
args = parser.parse_args()
# Load the model if it's availeble, otherwise that latest checkpoint.
experiment_dir = get_latest_experiment_dir(args.directory)
data = load_results(experiment_dir)
# time variables
# plot_data(data, X_EPISODES, 'tip_distance', label="plot 1")
plt.plot([1,2],[2,1])
plt.ylabel("Tip Distance")
plt.xlabel("Episodes")
plt.legend()
plt.savefig("test.png")
import matplotlib.pyplot as plt
from stable_baselines.results_plotter import load_results, ts2xy, X_TIMESTEPS, X_EPISODES, X_WALLTIME
# Add the parent folder to the python path for imports.
from plot_episode import plot_data
import sys, os
sys.path.insert(0, os.path.abspath('..'))
from experiment_files import get_latest_experiment_dir
# Load the PPO data.
# Torque
directory = os.path.join(
os.environ['HOME'], 'Google Drive', 'DriveSync',
'DirectTorqueController_PegInsertionEnv_Hole25_new_PPO2_0')
directory = os.path.abspath(directory)
experiment_dir = get_latest_experiment_dir(directory)
torque_data = load_results(experiment_dir)
print(experiment_dir)
# PD
directory = os.path.join(
os.environ['HOME'], 'Google Drive', 'DriveSync',
'RelativePDController_PegInsertionEnv_Hole25_new_PPO2_0')
directory = os.path.abspath(directory)
experiment_dir = get_latest_experiment_dir(directory)
print(experiment_dir)
pd_data = load_results(experiment_dir)
# ID
directory = os.path.join(
os.environ['HOME'], 'Google Drive', 'DriveSync',
import os
import sys
import cloudpickle
from gym_kuka_mujoco.utils.load_model import load_params, load_model
sys.path.insert(0, os.path.abspath('..'))
from experiment_files import get_latest_experiment_dir, get_params, get_model, get_latest_checkpoint
# Get the model path.
log_dir = os.path.join(os.environ['OPENAI_LOGDIR'], 'stable', '2019-04-01', "18:45:39.122285")
log_dir = get_latest_experiment_dir(log_dir)
params_path = get_params(log_dir)
model_path = get_model(log_dir)
if model_path is None:
model_path = get_latest_checkpoint(log_dir)
# Load the model with stable-baselines.
params = load_params(params_path)
env, model = load_model(model_path, params)
# Load the model with tensorflow.
with open(model_path, "rb") as file:
data, params = cloudpickle.load(file)
for _ in range(10):
obs = env.reset()
action, _ = model.predict(obs, deterministic=True)
print(action)