def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma ** 2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist, bottom=np.zeros(len(hist)), left=edges[:-1], right=edges[1:],
fill_color="#333333", line_color="#E5E5E5", line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
def posture_signals(kin_env,
m_signals,
title='posture graphs',
color='#666666',
alpha=1.0,
radius_factor=1.0,
swap_xy=True,
x_range=[-1.0, 1.0],
y_range=[-1.0, 1.0],
**kwargs):
for m_signal in m_signals:
m_vector = kin_env.flatten_synergies(m_signal)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i + 1)])
ys.append(s_signal['y{}'.format(i + 1)])
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs.update({
'x_range': x_range,
'y_range': y_range,
'line_color': color,
'line_alpha': alpha,
'fill_color': color,
'fill_alpha': alpha,
'title': title
})
if swap_xy:
xs, ys = ys, xs
plotting.line(xs, ys, line_width=2.0 * radius_factor, **kwargs)
plotting.hold(True)
plotting.grid().grid_line_color = None
plotting.ygrid().grid_line_color = None
plotting_axis()
plotting.circle(xs[:1], ys[:1], radius=radius_factor * 0.015, **kwargs)
plotting.circle(xs[1:-1],
ys[1:-1],
radius=radius_factor * 0.008,
**kwargs)
plotting.circle(xs[-1:],
ys[-1:],
radius=radius_factor * 0.01,
color='red',
alpha=alpha)
plotting.hold(False)
def posture_signals(
kin_env,
m_signals,
title="posture graphs",
color="#666666",
alpha=1.0,
radius_factor=1.0,
swap_xy=True,
x_range=[-1.0, 1.0],
y_range=[-1.0, 1.0],
**kwargs
):
for m_signal in m_signals:
m_vector = kin_env.flatten_synergies(m_signal)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal["x{}".format(i + 1)])
ys.append(s_signal["y{}".format(i + 1)])
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs.update(
{
"x_range": x_range,
"y_range": y_range,
"line_color": color,
"line_alpha": alpha,
"fill_color": color,
"fill_alpha": alpha,
"title": title,
}
)
if swap_xy:
xs, ys = ys, xs
plotting.line(xs, ys, line_width=2.0 * radius_factor, **kwargs)
plotting.hold(True)
plotting.grid().grid_line_color = None
plotting.ygrid().grid_line_color = None
plotting_axis()
plotting.circle(xs[:1], ys[:1], radius=radius_factor * 0.015, **kwargs)
plotting.circle(xs[1:-1], ys[1:-1], radius=radius_factor * 0.008, **kwargs)
plotting.circle(xs[-1:], ys[-1:], radius=radius_factor * 0.01, color="red", alpha=alpha)
plotting.hold(False)
def distribution():
mu, sigma = 0, 0.5
measured = np.random.normal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=20)
x = np.linspace(-2, 2, 1000)
pdf = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-(x - mu)**2 /
(2 * sigma**2))
cdf = (1 + scipy.special.erf((x - mu) / np.sqrt(2 * sigma**2))) / 2
output_server("distribution_reveal")
hold()
figure(title="Interactive plots",
tools="pan, wheel_zoom, box_zoom, reset, previewsave",
background_fill="#E5E5E5")
quad(top=hist,
bottom=np.zeros(len(hist)),
left=edges[:-1],
right=edges[1:],
fill_color="#333333",
line_color="#E5E5E5",
line_width=3)
# Use `line` renderers to display the PDF and CDF
line(x, pdf, line_color="#348abd", line_width=8, alpha=0.7, legend="PDF")
line(x, cdf, line_color="#7a68a6", line_width=8, alpha=0.7, legend="CDF")
xgrid().grid_line_color = "white"
xgrid().grid_line_width = 3
ygrid().grid_line_color = "white"
ygrid().grid_line_width = 3
legend().orientation = "top_left"
return curplot(), cursession()
def bokeh_kin(kin_env, m_signal, color='#DF4949', alpha=1.0, **kwargs):
m_vector = tools.to_vector(m_signal, kin_env.m_channels)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i + 1)])
ys.append(s_signal['y{}'.format(i + 1)])
xs, ys = ys, xs # we swap x and y for a more symmetrical look
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths * kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs = {
'plot_height': int(350 * 1.60),
'plot_width': int(350 * 1.60),
'x_range': [-1.0, 1.0],
'y_range': [-1.0, 1.0],
'line_color': color,
'line_alpha': alpha,
'fill_color': color,
'fill_alpha': alpha,
'title': ''
}
plotting.hold()
plotting.line(xs, ys, **kwargs)
plotting.grid().grid_line_color = None
plotting.xaxis().major_tick_in = 0
plotting.ygrid().grid_line_color = None
plotting.yaxis().major_tick_in = 0
plotting.circle(xs[:1], ys[:1], radius=0.015, **kwargs)
plotting.circle(xs[1:-1], ys[1:-1], radius=0.008, **kwargs)
plotting.circle(xs[-1:], ys[-1:], radius=0.01, color='red')
plotting.hold(False)
def bokeh_kin(kin_env, m_signal, color='#DF4949', alpha=1.0, **kwargs):
m_vector = tools.to_vector(m_signal, kin_env.m_channels)
s_signal = kin_env._multiarm.forward_kin(m_vector)
xs, ys = [0.0], [0.0]
for i in range(kin_env.cfg.dim):
xs.append(s_signal['x{}'.format(i+1)])
ys.append(s_signal['y{}'.format(i+1)])
xs, ys = ys, xs # we swap x and y for a more symmetrical look
if isinstance(kin_env.cfg.lengths, numbers.Real):
total_length = kin_env.cfg.lengths*kin_env.cfg.dim
else:
total_length = sum(kin_env.cfg.lengths)
total_length += 0.0
kwargs ={'plot_height' : int(350*1.60),
'plot_width' : int(350*1.60),
'x_range' : [-1.0, 1.0],
'y_range' : [-1.0, 1.0],
'line_color' : color,
'line_alpha' : alpha,
'fill_color' : color,
'fill_alpha' : alpha,
'title':''
}
plotting.hold()
plotting.line(xs, ys, **kwargs)
plotting.grid().grid_line_color = None
plotting.xaxis().major_tick_in = 0
plotting.ygrid().grid_line_color = None
plotting.yaxis().major_tick_in = 0
plotting.circle(xs[ : 1], ys[ : 1], radius=0.015, **kwargs)
plotting.circle(xs[ 1:-1], ys[ 1:-1], radius=0.008, **kwargs)
plotting.circle(xs[-1: ], ys[-1: ], radius=0.01, color='red')
plotting.hold(False)
def test_ygrid(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.ygrid()), 1)
self.assertEqual(plt.ygrid()[0].dimension, 1)
def test_ygrid(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.ygrid()), 1)
self.assertEqual(plt.ygrid()[0].dimension, 1)