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 kde1(datasets, axis, bandwidth = None, npoints = 1001, xmin = None, xmax = None, xrange_ = None, axes = None):
"""
"""
# Extract data
data = util.extract_data(datasets, axis)
titles = util.extract_title(datasets)
xmin, xmax = util.set_limits(data, xmin, xmax, xrange_, axis)
if bandwidth is None:
bandwidth = util.default_bandwidth(axis, npoints, xmin, xmax)
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis, )
xgrid = np.linspace(xmin, xmax, npoints)
for (d, t) in zip(data, titles):
den = kde.hat_linear(d, bandwidth, xmin, xmax, npoints)
ax.plot(xgrid, den, label = t)
ax.set_yscale(util.default_yscale(axis))
ax.set_xlabel(axis)
ax.set_ylabel('Density Estimate')
if len(data) > 1:
ax.legend()
else:
ax.set_title(titles[0])
return fig
def kde1(datasets,
axis,
bandwidth=None,
npoints=1001,
xmin=None,
xmax=None,
xrange_=None,
axes=None):
"""
"""
# Extract data
data = util.extract_data(datasets, axis)
titles = util.extract_title(datasets)
xmin, xmax = util.set_limits(data, xmin, xmax, xrange_, axis)
if bandwidth is None:
bandwidth = util.default_bandwidth(axis, npoints, xmin, xmax)
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis, )
xgrid = np.linspace(xmin, xmax, npoints)
for (d, t) in zip(data, titles):
den = kde.hat_linear(d, bandwidth, xmin, xmax, npoints)
ax.plot(xgrid, den, label=t)
ax.set_yscale(util.default_yscale(axis))
ax.set_xlabel(axis)
ax.set_ylabel('Density Estimate')
if len(data) > 1:
ax.legend()
else:
ax.set_title(titles[0])
return fig
def hist1(datasets,
axis,
bins=None,
xmin=None,
xmax=None,
xrange_=None,
axes=None):
"""One dimensional histograms.
"""
# Extract data
data = util.extract_data(datasets, axis)
titles = util.extract_title(datasets)
xmin, xmax = util.set_limits(data, xmin, xmax, xrange_, axis)
if bins is None:
bins = util.bin_default(axis, xmin, xmax)
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis, )
# Plotting preferences
alpha = util.alpha(len(data))
# We do not use the Matplotlib API for histograms because we want to have transparent plots
# Following example: http://matplotlib.org/examples/api/histogram_path_demo.html
max_value = float('-inf')
for (d, t) in zip(data, titles):
(hist, bin_edges) = np.histogram(d, bins=bins, range=(xmin, xmax))
left = np.array(bin_edges[:-1])
right = np.array(bin_edges[1:])
# FIXES a bug in MPLD3 0.3 regarding NaNs in coordinates
bottom = 1e-6 * np.ones(len(left))
top = bottom + hist
XY = np.array([[left, left, right, right], [bottom, top, top,
bottom]]).T
barpath = matplotlib.path.Path.make_compound_path_from_polys(XY)
# serves to get the current color
base_line, = ax.plot(hist, alpha=0)
patch = matplotlib.patches.PathPatch(barpath,
facecolor=base_line.get_color(),
edgecolor=base_line.get_color(),
alpha=alpha)
# Clear the unneeded line
base_line.remove()
patch.set_label(t)
ax.add_patch(patch)
max_value = max(max_value, top.max())
ax.set_xlim(xmin, xmax)
ax.set_ylim(1, max_value)
ax.set_xlabel(axis)
ax.set_yscale(util.default_yscale(axis))
if len(data) > 1:
ax.legend()
else:
ax.set_title(titles[0])
return fig
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 hist1(datasets, axis, bins = None, xmin = None, xmax = None, xrange_ = None, axes = None):
"""One dimensional histograms.
"""
# Extract data
data = util.extract_data(datasets, axis)
titles = util.extract_title(datasets)
xmin, xmax = util.set_limits(data, xmin, xmax, xrange_, axis)
if bins is None:
bins = util.bin_default(axis, xmin, xmax)
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis, )
# Plotting preferences
alpha = util.alpha(len(data))
# We do not use the Matplotlib API for histograms because we want to have transparent plots
# Following example: http://matplotlib.org/examples/api/histogram_path_demo.html
max_value = float('-inf')
for (d, t) in zip(data, titles ):
(hist, bin_edges) = np.histogram(d, bins = bins, range = (xmin, xmax))
left = np.array(bin_edges[:-1])
right = np.array(bin_edges[1:])
# FIXES a bug in MPLD3 0.3 regarding NaNs in coordinates
bottom = 1e-6*np.ones(len(left))
top = bottom + hist
XY = np.array([[left,left,right,right], [bottom, top, top, bottom]]).T
barpath = matplotlib.path.Path.make_compound_path_from_polys(XY)
# serves to get the current color
base_line, = ax.plot(hist, alpha = 0)
patch = matplotlib.patches.PathPatch(barpath, facecolor = base_line.get_color(),
edgecolor = base_line.get_color(), alpha = alpha)
# Clear the unneeded line
base_line.remove()
patch.set_label(t)
ax.add_patch(patch)
max_value = max(max_value, top.max())
ax.set_xlim(xmin, xmax)
ax.set_ylim(1, max_value )
ax.set_xlabel(axis)
ax.set_yscale(util.default_yscale(axis))
if len(data) > 1:
ax.legend()
else:
ax.set_title(titles[0])
return fig
def hist2(datasets, axis1, axis2, bins = None,
xmin = None, xmax = None, ymin = None, ymax = None, range_ = None,
axes = None, transform = None):
datax = util.extract_data(datasets, axis1)
datay = util.extract_data(datasets, axis2)
titles = util.extract_title(datasets)
try:
xrange_ = range_[0]
yrange_ = range_[1]
except:
xrange_ = None
yrange_ = None
xmin, xmax = util.set_limits(datax, xmin, xmax, xrange_, axis1)
ymin, ymax = util.set_limits(datay, ymin, ymax, yrange_, axis2)
if not isinstance(transform, (list, tuple)):
transform = [transform, transform]
scaling = [None, None]
scaling[0], transform[0] = util.default_scaling(axis1, scaling = scaling[0], transform = transform[0])
scaling[1], transform[1] = util.default_scaling(axis2, scaling = scaling[1], transform = transform[1])
for index, d in enumerate(datax):
datax[index] = transform[0](d)
for index, d in enumerate(datay):
datay[index] = transform[1](d)
xmin_transformed, xmax_transformed = util.set_limits(datax)
ymin_transformed, ymax_transformed = util.set_limits(datay)
# Determine how many bins to use
if bins is None:
bins = [None, None]
if isinstance(bins, int):
bins = [bins, bins]
bins = list(bins)
bins[0] = util.bin_default(axis1, xmin, xmax, bins = bins[0])
bins[1] = util.bin_default(axis2, xmin, xmax, bins = bins[1])
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis1, axis2)
# We do not use the Matplotlib API for histograms because we want to have transparent plots
# Following example: http://matplotlib.org/examples/api/histogram_path_demo.html
den_ = []
range_ = ((xmin_transformed, xmax_transformed),(ymin_transformed, ymax_transformed))
for (dx, dy) in zip(datax, datay):
den, xedge, yedge = np.histogram2d(dx, dy, bins = bins, range = range_)
den_.append(den)
alpha = util.alpha(len(den_))
proxy = []
line_collections = []
levels = 10**np.arange(0,7)
for den in den_:
line, = ax.plot(0,0)
ln = ax.imshow(den.T, cmap = make_cmap(line.get_color()), origin = 'lower',
norm = matplotlib.colors.LogNorm(),
extent = [xmin, xmax, ymin, ymax],
interpolation = 'none',
aspect = 'auto')
line_collections.append(ln)
proxy.append( plt.Rectangle((0,0),1,1,fc = line.get_color(),alpha = alpha))
line.remove()
if len(datax) == 1:
ax.set_title(titles[0])
elif len(datax) > 1:
ax.legend(proxy, titles)
ax.set_xlabel(axis1)
ax.set_ylabel(axis2)
ax.set_xscale(scaling[0])
ax.set_yscale(scaling[1])
return fig
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 hist2(datasets,
axis1,
axis2,
bins=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
range_=None,
axes=None,
transform=None):
datax = util.extract_data(datasets, axis1)
datay = util.extract_data(datasets, axis2)
titles = util.extract_title(datasets)
try:
xrange_ = range_[0]
yrange_ = range_[1]
except:
xrange_ = None
yrange_ = None
xmin, xmax = util.set_limits(datax, xmin, xmax, xrange_, axis1)
ymin, ymax = util.set_limits(datay, ymin, ymax, yrange_, axis2)
if not isinstance(transform, (list, tuple)):
transform = [transform, transform]
scaling = [None, None]
scaling[0], transform[0] = util.default_scaling(axis1,
scaling=scaling[0],
transform=transform[0])
scaling[1], transform[1] = util.default_scaling(axis2,
scaling=scaling[1],
transform=transform[1])
for index, d in enumerate(datax):
datax[index] = transform[0](d)
for index, d in enumerate(datay):
datay[index] = transform[1](d)
xmin_transformed, xmax_transformed = util.set_limits(datax)
ymin_transformed, ymax_transformed = util.set_limits(datay)
# Determine how many bins to use
if bins is None:
bins = [None, None]
if isinstance(bins, int):
bins = [bins, bins]
bins = list(bins)
bins[0] = util.bin_default(axis1, xmin, xmax, bins=bins[0])
bins[1] = util.bin_default(axis2, xmin, xmax, bins=bins[1])
fig, ax = util.fig_ax(axes)
fig._flowml_axis = (axis1, axis2)
# We do not use the Matplotlib API for histograms because we want to have transparent plots
# Following example: http://matplotlib.org/examples/api/histogram_path_demo.html
den_ = []
range_ = ((xmin_transformed, xmax_transformed), (ymin_transformed,
ymax_transformed))
for (dx, dy) in zip(datax, datay):
den, xedge, yedge = np.histogram2d(dx, dy, bins=bins, range=range_)
den_.append(den)
alpha = util.alpha(len(den_))
proxy = []
line_collections = []
levels = 10**np.arange(0, 7)
for den in den_:
line, = ax.plot(0, 0)
ln = ax.imshow(den.T,
cmap=make_cmap(line.get_color()),
origin='lower',
norm=matplotlib.colors.LogNorm(),
extent=[xmin, xmax, ymin, ymax],
interpolation='none',
aspect='auto')
line_collections.append(ln)
proxy.append(
plt.Rectangle((0, 0), 1, 1, fc=line.get_color(), alpha=alpha))
line.remove()
if len(datax) == 1:
ax.set_title(titles[0])
elif len(datax) > 1:
ax.legend(proxy, titles)
ax.set_xlabel(axis1)
ax.set_ylabel(axis2)
ax.set_xscale(scaling[0])
ax.set_yscale(scaling[1])
return fig
