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()
def p0x88_to_chess_notation(x):
icol = col(x)
irow = rank(x)
return chr(icol + 97) + str(irow + 1)
def p0x88_to_tuple(position):
return (col(position), rank(position))
def main(dataset='measurements.npy', plot_mean=0):
plot_mean = plot_mean > 0
X = np.load(dataset, mmap_mode='r')
print 'loaded', dataset, X.shape
series = collections.defaultdict(list)
for subject in X:
for block in subject:
for trial in block:
weight = trial[0, C.col('block-weight')]
if weight != C.UNWEIGHTED:
continue
hand = trial[0, C.col('block-hand')]
if hand != C.DOMINANT:
continue
speed = trial[0, C.col('trial-speed')]
for src, tgt in PAIRS:
d = distances(trial, src, tgt)
series[src, tgt].append((speed, d.mean(), d.std()))
for i, (src, tgt) in enumerate(PAIRS):
speed, mean, std = np.array(series[(src, tgt)]).T
dependent = [std, mean][plot_mean]
model = LinearRegression()
model.fit(speed[:, None], np.log(dependent))
idx = np.arange(len(speed))
np.random.shuffle(idx)
spines = []
if i == 0:
spines.append('left')
if not plot_mean:
spines.append('bottom')
ax = lmj.plot.axes((1, len(series), i + 1), spines=spines)
#ax.errorbar(speed, mean, yerr=std, fmt='o', alpha=0.9)
ax.plot(speed[idx[:100]], dependent[idx[:100]], 'o', color='#111111', alpha=0.7)
ax.plot([speed.min(), speed.max()],
np.exp(model.predict([[speed.min()], [speed.max()]])),
'-', color='#cc3333', lw=3)
ax.set_ylim((10, 1000))
ax.set_yscale('log')
if plot_mean:
ax.set_title('{} - {}'.format(src.capitalize(), tgt.capitalize()))
ax.set_xticks([])
ax.xaxis.set_ticks_position('none')
else:
ax.set_xlabel('Tracing Speed (m/s)')
if i == 0:
ax.set_ylabel('{} Distance (mm)'.format(['SD of', 'Mean'][plot_mean]))
else:
ax.set_yticks([])
ax.yaxis.set_ticks_position('none')
out = 'error-vs-speed-{}.pdf'.format(['std', 'mean'][plot_mean])
print 'saving', out
lmj.plot.gcf().set_size_inches(12, 3)
lmj.plot.savefig(out, dpi=600)
