def posture_extrema(env, explorations, thetas=tuple(i*math.pi/4 for i in range(8)), **kwargs):
s_vectors = [env_tools.to_vector(e[1]['s_signal'], env.s_channels) for e in explorations]
if 'theta' in [c.name for c in env.s_channels]:
s_vectors, s_channels = depolarize(s_vectors, env.s_channels)
idxs = factored.spread_extrema(s_vectors, dirs=thetas)
m_signals = [explorations[idx][0]['m_signal'] for idx in idxs]
return posture_signals(env, m_signals, **kwargs)
def posture_extrema(env,
explorations,
thetas=tuple(i * math.pi / 4 for i in range(8)),
**kwargs):
s_vectors = [
tools.to_vector(e[1]['s_signal'], env.s_channels) for e in explorations
]
idxs = factored.spread_extrema(s_vectors, dirs=thetas)
m_signals = [explorations[idx][0]['m_signal'] for idx in idxs]
posture_signals(env, m_signals, **kwargs)
def posture_extrema(env,
explorations,
thetas=tuple(i * math.pi / 4 for i in range(8)),
**kwargs):
s_vectors = [
env_tools.to_vector(e[1]['s_signal'], env.s_channels)
for e in explorations
]
if 'theta' in [c.name for c in env.s_channels]:
s_vectors, s_channels = depolarize(s_vectors, env.s_channels)
idxs = factored.spread_extrema(s_vectors, dirs=thetas)
m_signals = [explorations[idx][0]['m_signal'] for idx in idxs]
return posture_signals(env, m_signals, **kwargs)
def posture_extrema(env, explorations, thetas=tuple(i * math.pi / 4 for i in range(8)), **kwargs):
s_vectors = [tools.to_vector(e[1]["s_signal"], env.s_channels) for e in explorations]
idxs = factored.spread_extrema(s_vectors, dirs=thetas)
m_signals = [explorations[idx][0]["m_signal"] for idx in idxs]
posture_signals(env, m_signals, **kwargs)
