def main(dataset='measurements.npy'): X = np.load(dataset, mmap_mode='r') print 'loaded', dataset, X.shape trial = X[0, 0, 0] fig = plt.figure() ax = util.axes(fig) lines = [ax.plot([], [], [], '.-', c='#111111')[0] for s in C.SKELETON] def init(): for l in lines: l.set_data([], []) l.set_3d_properties([]) return lines def draw(f): frame = trial[f % len(trial)] markers = frame[17:].reshape((-1, 4)) for i, l in enumerate(lines): x, y, z = np.array( [util.identity(frame, markers[m, :3]) for m in C.SKELETON[i] if markers[m, 3] > 0]).T l.set_data(x, z) l.set_3d_properties(y) #ax.view_init(30, 0.3 * f) fig.canvas.draw() return lines a = anim.FuncAnimation( fig, draw, init_func=init, frames=240, interval=10, blit=False) #a.save('/tmp/trial.mp4', fps=15, extra_args=['-vcodec', 'libx264']) util.set_limits(ax, center=(0, 0, 0)) plt.show()
def main(dataset='measurements.npy'): data = np.load(dataset, mmap_mode='r') print 'loaded', dataset, data.shape fig = plt.figure() ax = util.axes(fig, 111) trial = data[15, 1, 5] for f in range(0, len(trial), 300): util.plot_skeleton(ax, trial[f], alpha=1) x, y, z = trial[:, TARGET].T ax.plot(x, z, y, 'o-', color='#111111', alpha=0.5) util.set_limits(ax, center=(0, 0, 1), span=1) ax.w_xaxis.set_pane_color((1, 1, 1, 1)) ax.w_yaxis.set_pane_color((1, 1, 1, 1)) ax.w_zaxis.set_pane_color((1, 1, 1, 1)) #plt.gcf().set_size_inches(12, 10) #plt.savefig('single-trial.pdf', dpi=600) plt.show()
def main(dataset='measurements.npy'): data = np.load(dataset, mmap_mode='r') print 'loaded', dataset, data.shape plots = list(range(N * N)) frames = [[] for _ in plots] for subj in data: for block in subj[1:]: for trial in block: if trial[0, C.col('trial-hand')] == C.right: for frame in trial: for i in plots: if within_region(frame, i): frames[i].append(frame) break u, v = np.mgrid[0:2 * np.pi:11j, 0:np.pi:7j] sphx = np.cos(u) * np.sin(v) sphy = np.sin(u) * np.sin(v) sphz = np.cos(v) fig = plt.figure() for i, postures in enumerate(frames): if not postures: continue if i != 2: continue postures = np.array(postures) for m in range(50): marker = postures[:, 17+m*4:17+(m+1)*4] drops = marker[:, 3] < 0 marker[drops, :3] = marker[~drops, :3].mean(axis=0) means = postures.mean(axis=0) stds = postures.std(axis=0) #ax = util.axes(fig, 111) #for frame in postures[::5]: # util.plot_skeleton(ax, frame, alpha=0.1) ax = util.axes(fig, 110 * N + i + 1) util.plot_skeleton(ax, means, alpha=1.0) for m in range(50): mx, my, mz = means[17+m*4:20+m*4] sx, sy, sz = stds[17+m*4:20+m*4] / 2 ax.plot_wireframe(sphx * sx + mx, sphz * sz + mz, sphy * sy + my, color=C.MARKER_COLORS[m], alpha=0.3) #tgtx, tgty, tgtz = postures.mean(axis=0)[ # C.cols('target-x', 'target-y', 'target-z')] #ax.plot([tgtx], [tgtz], [tgty], 'o', color='#111111') #for m in range(50): # marker = postures[:, 17 + 4 * m:17 + 4 * (m+1)] # position = marker.mean(axis=0) # size = marker.std(axis=0) # ax.plot_surface() util.set_limits(ax, center=(0, -0.5, 1), span=1) ax.w_xaxis.set_pane_color((1, 1, 1, 1)) ax.w_yaxis.set_pane_color((1, 1, 1, 1)) ax.w_zaxis.set_pane_color((1, 1, 1, 1)) ax.set_title(['Top Right', 'Top Left', 'Bottom Right', 'Bottom Left'][i]) #for m in range(50): # x, z, y = frame[m*4:m*4+3] # ax.text(x, y, z, str(m)) plt.gcf().set_size_inches(12, 10) #plt.savefig('reach-targets-with-variance.pdf', dpi=600) plt.show()