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)