def show_stats(self):
et = time() - self.last_time
self.last_time = time()
r_f = np.linalg.norm(self.stats['r_f'], axis=1)
v_f = np.linalg.norm(self.stats['v_f'], axis=1)
f = '{:6.2f}'
print('Update Cnt = %d ET = %8.1f Stats: Mean, Std, Min, Max' %
(self.update_cnt, et))
for k in self.keys:
f = self.formats[k]
v = self.stats[k]
if k == 'thrust' or k == 'tracking_error' or k == 'norm_thrust' or k == 'attitude' or k == 'w':
v = np.concatenate(v)
v = np.asarray(v)
if len(v.shape) == 1:
v = np.expand_dims(v, axis=1)
s = '%-8s' % (k)
#print('foo: ',k,v)
s += envu.print_vector(' |', np.mean(v, axis=0), f)
s += envu.print_vector(' |', np.std(v, axis=0), f)
s += envu.print_vector(' |', np.min(v, axis=0), f)
s += envu.print_vector(' |', np.max(v, axis=0), f)
print(s)
def show_episode(self, idx=0):
traj = self.trajectory_list[idx]
t = np.asarray(traj['time'])
p = np.asarray(traj['position'])
v = np.asarray(traj['velocity'])
c = np.asarray(traj['thrust'])
f1 = '{:8.1f}'
f2 = '{:8.3f}'
for i in range(t.shape[0]):
s = 't: %6.1f' % (t[i])
s += envu.print_vector(' |', p[i], f1)
s += envu.print_vector(' |', v[i], f2)
s += envu.print_vector(' |', c[i], f2)
print(s)
def show_final_stats(self, type='final'):
if type == 'final':
print('Final Stats (mean,std,min,max)')
idx = -1
else:
print('Initial Stats (mean,std,min,max)')
idx = 0
stats = {}
keys = ['position', 'velocity', 'fuel', 'attitude', 'w', 'glideslope']
formats = {
'position': '{:8.1f}',
'velocity': '{:8.3f}',
'fuel': '{:6.2f}',
'attitude': '{:8.3f}',
'w': '{:8.3f}',
'glideslope': '{:8.3f}'
}
for k in keys:
stats[k] = []
for traj in self.trajectory_list:
for k in keys:
v = traj[k]
v = np.asarray(v)
if len(v.shape) == 1:
v = np.expand_dims(v, axis=1)
if k == 'attitude':
foo = self.attitude_parameterization.q2Euler321(v[idx])
else:
foo = v[idx]
stats[k].append(foo)
for k in keys:
f = formats[k]
v = stats[k]
s = '%-8s' % (k)
s += envu.print_vector(' |', np.mean(v, axis=0), f)
s += envu.print_vector(' |', np.std(v, axis=0), f)
s += envu.print_vector(' |', np.min(v, axis=0), f)
s += envu.print_vector(' |', np.max(v, axis=0), f)
print(s)
def show_cum_stats(self):
print('Cumulative Stats (mean,std,max,argmax)')
stats = {}
argmax_stats = {}
keys = ['thrust', 'glideslope', 'sc_margin']
formats = {
'thrust': '{:6.2f}',
'glideslope': '{:6.3f}',
'sc_margin': '{:6.3f}'
}
for k in keys:
stats[k] = []
argmax_stats[k] = []
for traj in self.trajectory_list:
for k in keys:
v = traj[k]
v = np.asarray(v)
if len(v.shape) == 1:
v = np.expand_dims(v, axis=1)
wc = np.max(np.linalg.norm(v, axis=1))
argmax_stats[k].append(wc)
stats[k].append(np.linalg.norm(v, axis=1))
for k in keys:
f = formats[k]
v = stats[k]
v = np.concatenate(v)
#v = np.asarray(v)
s = '%-8s' % (k)
#print('foo: ',k,v,v.shape)
s += envu.print_vector(' |', np.mean(v), f)
s += envu.print_vector(' |', np.std(v), f)
s += envu.print_vector(' |', np.min(v), f)
s += envu.print_vector(' |', np.max(v), f)
argmax_v = np.asarray(argmax_stats[k])
s += ' |%6d' % (np.argmax(argmax_v))
print(s)