In [1]:
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
import sys
sys.path.append('../')
import numpy as np
import deep_control as dc
import pandas
import seaborn as sns
Previously generated trajectories can be loaded with dc.data.load_trajectories
In [4]:
import glob
import pickle
from tqdm import tqdm
files = glob.glob('../data/rw/*pic')
total = 0
trajs = []
for f in tqdm(files, leave=True):
rw = pickle.load(open(f,'rb'))
for rwi in rw:
traj = np.hstack((rwi[0], rwi[1]))
df = pandas.DataFrame(data=traj)
col_names = ['t', 'x', 'y', 'z', 'vz', 'theta', 'm', 'u1', 'u2']
df.columns = col_names
trajs.append(df)
In [5]:
ini_ps = np.vstack([t.values[0,:] for t in trajs])
In [6]:
for i in range(3):
for j in range(2):
plt.subplot(2,3,i*2+j+1)
plt.hist(ini_ps[:,i*2+j+1],59)
plt.locator_params(nbins=5)
plt.tight_layout()
In [7]:
plot_idx_t = [(x,list(range(1,x)))for x in range(1,7)]
In [8]:
plot_idx = []
In [9]:
for p in plot_idx_t:
for pi in p[1]:
plot_idx.append((p[0],pi))
In [10]:
plt.rcParams['figure.figsize'] = [30,20]
In [11]:
for i in range(5):
for j in range(3):
plt.subplot(5,3,i*3+j+1)
plt.scatter(ini_ps[:,plot_idx[i*3+j][0]], ini_ps[:,plot_idx[i*3+j][1]], s=5, alpha=0.1)
plt.locator_params(nbins=4)