def plot_circle_density(nodes, degrees, plot_width=800, plot_height=800):
print("Plotting circle density graph")
TOOLS="hover,pan,wheel_zoom,box_zoom,reset,click,previewsave"
plt.figure(plot_width=plot_width, plot_height=plot_height, tools=TOOLS)
theta = np.random.uniform(0, 2*np.pi, size=len(nodes))
max_d, min_d = np.max(degrees), np.min(degrees)
scale = 1.0/np.log(degrees) - 1.0/np.log(max_d)
xs = np.cos(theta)*scale
ys = np.sin(theta)*scale
source_dict = dict(
xs = xs,
ys = ys,
degrees = degrees,
nodes = nodes,
alphas = np.log(degrees)/np.log(max(degrees)),
)
source = ColumnDataSource(source_dict)
plt.hold(True)
plt.circle('xs', 'ys', source=source,
radius=0.0025,
fill_alpha='alphas',
x_axis_type=None, y_axis_type=None,
title="Density Distribution of Degrees")
plt.text([max(xs), max(xs)],
[.95*max(ys), .85*max(ys)],
["distance from center = 1 / log(deg)",
"angle = random"],
angle=0,
text_baseline="bottom", text_align="right")
hover = [t for t in plt.curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
('node', '@nodes'), ('degree', '@degrees')
])
plt.hold(False)
return plt.curplot()
def plot_3(data, ss):
"""t-SNE embedding of the parameters, colored by score
"""
scores = np.array([d['mean_test_score'] for d in data])
# maps each parameters to a vector of floats
warped = np.array([ss.point_to_moe(d['parameters']) for d in data])
# Embed into 2 dimensions with t-SNE
X = TSNE(n_components=2).fit_transform(warped)
e_scores = np.exp(scores)
mine, maxe = np.min(e_scores), np.max(e_scores)
color = (e_scores - mine) / (maxe - mine)
mapped_colors = map(rgb2hex, cm.get_cmap('RdBu_r')(color))
bk.figure(title='t-SNE (unsupervised)')
bk.hold()
df_params = nonconstant_parameters(data)
df_params['score'] = scores
bk.circle(
X[:, 0], X[:, 1], color=mapped_colors, radius=1,
source=ColumnDataSource(df_params), fill_alpha=0.6,
line_color=None, tools=TOOLS)
cp = bk.curplot()
hover = cp.select(dict(type=HoverTool))
format_tt = [(s, '@%s' % s) for s in df_params.columns]
hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
xax, yax = bk.axis()
xax.axis_label = 't-SNE coord 1'
yax.axis_label = 't-SNE coord 2'
def perf_std_discrete(ticks,
avgs,
stds,
legend=None,
std_width=0.3,
plot_width=1000,
plot_height=300,
color=BLUE,
alpha=1.0,
**kwargs):
plotting.rect(ticks,
avgs, [std_width for _ in stds],
2 * np.array(stds),
line_color=None,
fill_color=color,
fill_alpha=alpha * 0.5,
plot_width=plot_width,
plot_height=plot_height,
**kwargs)
plotting.hold(True)
plotting.line(ticks,
avgs,
line_color=color,
line_alpha=alpha,
legend=legend)
plotting.circle(ticks,
avgs,
line_color=None,
fill_color=color,
fill_alpha=alpha)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def get_plottag(script_path):
""" Saves js in script_path and returns tag for embedding in html
"""
import numpy as np
import pandas as pd
import os
y_data = np.random.randn(100)
x_data = pd.date_range('31-Aug-2014', periods=len(y_data))
figure(x_axis_type='datetime',
tools='pan,wheel_zoom,box_zoom,reset,previewsave,crosshair',
name='test plot')
hold()
line(x_data, y_data,
line_color="#D95B43", line_width=4, alpha=0.7,
legend='random',
background_fill= '#cccccc')
circle(x_data, y_data,
color='red', fill_color=None, size=6,
legend='random')
curplot().title = 'Test Plot'
xaxis().axis_label='date'
yaxis().axis_label='some random numbers'
grid().grid_line_color='white'
grid().grid_line_alpha = 0.5
plotid = curplot()._id
script_path = os.path.join(script_path, plotid+'.js')
js, tag = autoload_static(curplot(), CDN, script_path=script_path)
with open(script_path, 'w') as f:
f.write(js)
return tag
def radialchart(nodes, edgelist):
ids, times, relevances = nodes.T
times *= 2
node_x, node_y = _radial_layout(nodes)
nodepos = np.asarray((node_x, node_y)).T
hold()
circle(node_x, node_y, color="red", size=relevances * 4, alpha=relevances)
coords = nodepos[edgelist].reshape((len(edgelist), 4)).T
segment(coords[0], coords[1], coords[2], coords[3], line_alpha=0.35)
def radialchart(nodes, edgelist):
ids, times, relevances = nodes.T
times *= 2
node_x, node_y = _radial_layout(nodes)
nodepos = np.asarray((node_x, node_y)).T
hold()
circle(node_x, node_y, color="red", size=relevances*4, alpha=relevances)
coords = nodepos[edgelist].reshape((len(edgelist), 4)).T
segment(coords[0], coords[1], coords[2], coords[3],
line_alpha=0.35)
def build_punchcard(datamap_list, concept_list,
radii_list, fields_in_concept_list,
datamaps, concepts,
plot_width=1200, plot_height=800):
source = ColumnDataSource(
data=dict(
datamap=datamap_list, # x
concept=concept_list, # y
radii=radii_list,
fields_in_concept=fields_in_concept_list,
)
)
output_file('')
hold()
figure()
plot_properties = {
'title': None,
'tools': "hover,resize,previewsave",
'y_range': [get_datamap_label(datamap) for datamap in datamaps],
'x_range': concepts,
'plot_width': plot_width,
'plot_height': plot_height,
}
rect('concept', 'datamap', # x, y
1, 1, # height, width
source=source,
color='white', # put in background
**plot_properties)
circle('concept', 'datamap', # x, y
size='radii',
source=source,
color='black',
**plot_properties)
grid().grid_line_color = None
x = xaxis()
x.major_label_orientation = pi / 4
hover = [t for t in curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
("Datamap", "@datamap"),
("Concept", "@concept"),
("Fields", "@fields_in_concept"),
])
return curplot().create_html_snippet(
static_path=settings.STATIC_URL,
embed_save_loc=settings.BOKEH_EMBED_JS_DIR,
embed_base_url=reverse('bokeh'),
)
def coverage(
s_channels,
threshold,
s_vectors=(),
title="no title",
swap_xy=True,
x_range=None,
y_range=None,
color=C_COLOR,
c_alpha=1.0,
alpha=0.5,
**kwargs
):
x_range, y_range = ranges(s_channels, x_range=x_range, y_range=y_range)
try:
xv, yv = zip(*(s[:2] for s in s_vectors))
except ValueError:
xv, yv = [], []
if swap_xy:
x_range, y_range = y_range, x_range
xv, yv = yv, xv
plotting.circle(
xv,
yv,
radius=threshold,
x_range=x_range,
y_range=y_range,
fill_color=hexa(color, 0.35),
fill_alpha=c_alpha,
line_color=None,
title=title,
**kwargs
)
plotting.hold(True)
union = shapely.ops.unary_union([shapely.geometry.Point(*sv_i).buffer(threshold) for sv_i in s_vectors])
boundary = union.boundary
if isinstance(boundary, shapely.geometry.LineString):
boundary = [boundary]
for b in boundary:
x, y = b.xy
x, y = list(x), list(y)
if swap_xy:
x, y = y, x
plotting.patch(x, y, fill_color=None, line_color=hexa(color, 0.75))
plotting.hold(True)
plotting_axis()
plotting.hold(False)
def perf_std_discrete(ticks, avgs, stds, legend=None,
std_width=0.3, plot_width=1000, plot_height=300,
color=BLUE, alpha=1.0, **kwargs):
plotting.rect(ticks, avgs, [std_width for _ in stds], 2*np.array(stds),
line_color=None, fill_color=color, fill_alpha=alpha*0.5,
plot_width=plot_width, plot_height=plot_height, **kwargs)
plotting.hold(True)
plotting.line(ticks, avgs, line_color=color, line_alpha=alpha, legend=legend)
plotting.circle(ticks, avgs, line_color=None, fill_color=color, fill_alpha=alpha)
plotting.grid().grid_line_color = 'white'
plotting_axis()
plotting.hold(False)
def test_figure(self):
p = plt.figure()
self.assertEqual(plt.curplot(), p)
q = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), q)
self.assertNotEqual(p, q)
r = plt.figure()
self.assertEqual(plt.curplot(), r)
self.assertNotEqual(p, r)
self.assertNotEqual(q, r)
plt.hold()
s = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), s)
self.assertEqual(r, s)
def test_figure(self):
p = plt.figure()
self.assertEqual(plt.curplot(), p)
q = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), q)
self.assertNotEqual(p, q)
r = plt.figure()
self.assertEqual(plt.curplot(), r)
self.assertNotEqual(p, r)
self.assertNotEqual(q, r)
plt.hold()
s = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), s)
self.assertEqual(r, s)
def coverage(s_channels,
threshold,
s_vectors=(),
title='no title',
swap_xy=True,
x_range=None,
y_range=None,
color=C_COLOR,
c_alpha=1.0,
alpha=0.5,
**kwargs):
x_range, y_range = ranges(s_channels, x_range=x_range, y_range=y_range)
try:
xv, yv = zip(*(s[:2] for s in s_vectors))
except ValueError:
xv, yv = [], []
if swap_xy:
x_range, y_range = y_range, x_range
xv, yv = yv, xv
plotting.circle(xv,
yv,
radius=threshold,
x_range=x_range,
y_range=y_range,
fill_color=hexa(color, 0.35),
fill_alpha=c_alpha,
line_color=None,
title=title,
**kwargs)
plotting.hold(True)
union = shapely.ops.unary_union([
shapely.geometry.Point(*sv_i).buffer(threshold) for sv_i in s_vectors
])
boundary = union.boundary
if isinstance(boundary, shapely.geometry.LineString):
boundary = [boundary]
for b in boundary:
x, y = b.xy
x, y = list(x), list(y)
if swap_xy:
x, y = y, x
plotting.patch(x, y, fill_color=None, line_color=hexa(color, 0.75))
plotting.hold(True)
plotting_axis()
plotting.hold(False)
def crawlchart(nodes, edgelist):
""" edges is an Nx2 array of node ids
"""
ids, times, relevances = nodes.T
times *= 2
node_y = _rectilinear_layout(nodes)
hold()
#circle(times, node_y, color="gray", size=1)
# Draw the relevant points in a different color
circle(times, node_y, color="red", size=relevances * 6, alpha=relevances)
nodepos = np.asarray((times, node_y)).T
coords = nodepos[edgelist].reshape((len(edgelist), 4)).T
segment(coords[0], coords[1], coords[2], coords[3], line_alpha=0.35)
def bokeh_highlights(s_vectors, n=1, color="#DF4949", swap_xy=True, radius=2.5, alpha=0.5):
"""n is the number of effect per cell to pick"""
xv, yv = zip(*s_vectors)
if swap_xy:
xv, yv = yv, xv
plotting.circle(
xv,
yv,
fill_color=None,
line_color=color,
line_alpha=alpha,
line_width=0.5,
radius=radius,
radius_units="screen",
)
def test_merge(self):
d1 = document.Document()
d2 = document.Document()
p1 = circle([1], [2])
p2 = circle([1], [2])
d1.add(p1)
d2.add(p2)
json_objs = d1.dump()
json_objs = protocol.deserialize_json(protocol.serialize_json(json_objs))
d2.merge(json_objs)
assert d2.context._id == d1.context._id
assert len(d2.context.children) == 2
assert d2.context is d2._models[d2.context._id]
pcs = [x for x in d2._models.values() if x.__view_model__ == "PlotContext"]
assert len(pcs) == 1
def test_figure(self):
plt.figure()
self.assertEqual(plt.curplot(), None)
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(plt.curplot(), p)
plt.figure()
self.assertEqual(plt.curplot(), None)
def crawlchart(nodes, edgelist):
""" edges is an Nx2 array of node ids
"""
ids, times, relevances = nodes.T
times *= 2
node_y = _rectilinear_layout(nodes)
hold()
#circle(times, node_y, color="gray", size=1)
# Draw the relevant points in a different color
circle(times, node_y, color="red", size=relevances*6, alpha=relevances)
nodepos = np.asarray((times,node_y)).T
coords = nodepos[edgelist].reshape((len(edgelist), 4)).T
segment(coords[0], coords[1], coords[2], coords[3],
line_alpha=0.35)
def test_axis(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.axis()), 2)
expected = set(plt.axis())
ax = LinearAxis()
expected.add(ax)
p.above.append(ax)
self.assertEqual(set(plt.axis()), expected)
ax2 = LinearAxis()
expected.add(ax2)
p.below.append(ax2)
self.assertEqual(set(plt.axis()), expected)
ax3 = LinearAxis()
expected.add(ax3)
p.left.append(ax3)
self.assertEqual(set(plt.axis()), expected)
ax4 = LinearAxis()
expected.add(ax4)
p.right.append(ax4)
self.assertEqual(set(plt.axis()), expected)
def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns", ticker2 + "_returns",
title="%s vs %s" %(ticker1, ticker2),
source=self.source,
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,select"
)
def test_axis(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.axis()), 2)
expected = set(plt.axis())
ax = LinearAxis()
expected.add(ax)
p.above.append(ax)
self.assertEqual(set(plt.axis()), expected)
ax2 = LinearAxis()
expected.add(ax2)
p.below.append(ax2)
self.assertEqual(set(plt.axis()), expected)
ax3 = LinearAxis()
expected.add(ax3)
p.left.append(ax3)
self.assertEqual(set(plt.axis()), expected)
ax4 = LinearAxis()
expected.add(ax4)
p.right.append(ax4)
self.assertEqual(set(plt.axis()), expected)
def test_add(self):
d = document.Document()
p = circle([1], [2])
d.add(p)
self.assertListEqual(d.context.children, [p])
self.assertEqual(len(d._models), len(p.references())+1)
self.assertTrue(d.context._dirty)
def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns",
ticker2 + "_returns",
title="%s vs %s" % (ticker1, ticker2),
source=self.source,
plot_width=400,
plot_height=400,
tools="pan,wheel_zoom,select")
def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns", ticker2 + "_returns",
title="%s vs %s" %(ticker1, ticker2),
source=self.source,
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,select"
)
session().plotcontext.children=[self]
session().plotcontext._dirty = True
def bokeh_highlights(s_vectors,
n=1,
color='#DF4949',
swap_xy=True,
radius=2.5,
alpha=0.5):
"""n is the number of effect per cell to pick"""
xv, yv = zip(*s_vectors)
if swap_xy:
xv, yv = yv, xv
plotting.circle(xv,
yv,
fill_color=None,
line_color=color,
line_alpha=alpha,
line_width=0.5,
radius=radius,
radius_units='screen')
def build_scatter_tooltip(x, y, tt, add_line=True, radius=3, title='My Plot',
xlabel='Iteration number', ylabel='Score'):
bk.figure(title=title)
bk.hold()
bk.circle(
x, y, radius=radius, source=ColumnDataSource(tt),
fill_alpha=0.6, line_color=None, tools=TOOLS)
if add_line:
bk.line(x, y, line_width=2)
xax, yax = bk.axis()
xax.axis_label = xlabel
yax.axis_label = ylabel
cp = bk.curplot()
hover = cp.select(dict(type=HoverTool))
format_tt = [(s, '@%s' % s) for s in tt.columns]
hover.tooltips = OrderedDict([("index", "$index")] + format_tt)
def make_plots(self, ticker1, ticker2):
self.plot = circle(ticker1 + "_returns",
ticker2 + "_returns",
title="%s vs %s" % (ticker1, ticker2),
source=self.source,
plot_width=400,
plot_height=400,
tools="pan,wheel_zoom,select")
session().plotcontext.children = [self]
session().plotcontext._dirty = True
def make_plot():
sess = session()
data = pd.DataFrame({'a' : np.random.randn(100), 'b' : np.random.randn(100)})
source = ColumnDataSource(data=data)
scatter_plot = circle(source=source, x='a', y='b', plot_width=500,
plot_height=500)
app = App(data_source=source,
scatter_plot=scatter_plot,
stats=str(data.describe())
)
return app
def line_plot(self, ticker, x_range=None):
plot = circle('date', ticker,
title=ticker,
size=2,
x_range=x_range,
x_axis_type='datetime',
source=self.source,
title_text_font_size="10pt",
plot_width=1000, plot_height=200,
nonselection_alpha=0.02,
tools="pan,wheel_zoom,select")
return plot
def make_plot():
sess = session()
data = pd.DataFrame({'a': np.random.randn(100), 'b': np.random.randn(100)})
source = ColumnDataSource(data=data)
scatter_plot = circle(source=source,
x='a',
y='b',
plot_width=500,
plot_height=500)
app = App(data_source=source,
scatter_plot=scatter_plot,
stats=str(data.describe()))
return app
def line_plot(self, ticker, x_range=None):
plot = circle(
'date', ticker,
title=ticker,
size=2,
x_range=x_range,
x_axis_type='datetime',
source=self.source,
title_text_font_size="10pt",
plot_width=1000, plot_height=200,
nonselection_alpha=0.02,
tools="pan,wheel_zoom,select"
)
return plot
def make_plots(self):
ticker1 = self.ticker1
ticker2 = self.ticker2
self.plot = circle(ticker1 + "_returns", ticker2 + "_returns",
size=2,
title="%s vs %s" %(ticker1, ticker2),
source=self.source,
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,select",
title_text_font_size="10pt",
nonselection_alpha=0.02
)
self.line_plot1 = self.line_plot(ticker1)
self.line_plot2 = self.line_plot(ticker2, self.line_plot1.x_range)
self.hist_plots()
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 make_plots(self):
ticker1 = self.ticker1
ticker2 = self.ticker2
self.plot = circle(
ticker1 + "_returns", ticker2 + "_returns",
size=2,
title="%s vs %s" %(ticker1, ticker2),
source=self.source,
plot_width=400, plot_height=400,
tools="pan,wheel_zoom,select",
title_text_font_size="10pt",
nonselection_alpha=0.02,
)
self.line_plot1 = self.line_plot(ticker1)
self.line_plot2 = self.line_plot(ticker2, self.line_plot1.x_range)
self.hist_plots()
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 test_xaxis(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.xaxis()), 1)
expected = set(plt.xaxis())
ax = LinearAxis()
expected.add(ax)
p.above.append(ax)
self.assertEqual(set(plt.xaxis()), expected)
ax2 = LinearAxis()
expected.add(ax2)
p.above.append(ax2)
self.assertEqual(set(plt.xaxis()), expected)
p.left.append(LinearAxis())
self.assertEqual(set(plt.xaxis()), expected)
p.right.append(LinearAxis())
self.assertEqual(set(plt.xaxis()), expected)
def test_xaxis(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.xaxis()), 1)
expected = set(plt.xaxis())
ax = LinearAxis()
expected.add(ax)
p.above.append(ax)
self.assertEqual(set(plt.xaxis()), expected)
ax2 = LinearAxis()
expected.add(ax2)
p.above.append(ax2)
self.assertEqual(set(plt.xaxis()), expected)
p.left.append(LinearAxis())
self.assertEqual(set(plt.xaxis()), expected)
p.right.append(LinearAxis())
self.assertEqual(set(plt.xaxis()), expected)
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)
if yvkey:
all_yv.append( sdata["stats"][yvkey] )
### check for data
if not len(all_y):
continue
fig = bplt.figure()
bplt.hold()
### build data source for hover tool
source = bplt.ColumnDataSource(data=dict(x=all_x, y=all_y, stefilename=all_stefilename, Nmodes=all_Nmodes, Ngens=all_Ngens, Ntriples=all_Ntriples))
### plot circle glyphs
bplt.circle(all_x, all_y, source=source, tools=TOOLS, fill_color=None, fill_alpha=0.6, line_color=all_color, Title="%s vs %s"%(xlabel, ylabel), plot_width=plot_width, plot_height=plot_height)
# bplt.circle(all_x, all_y, radius=radii, source=source, tools=TOOLS, fill_color=None, fill_alpha=0.6, line_color=all_color, Title="%s vs %s"%(xlabel, ylabel))
### annotate circle glyphs
# text(x, y, text=inds, alpha=0.5, text_font_size="5pt", text_baseline="middle", text_align="center", angle=0)
### find hover tool, and tell it what to look for
hover = [t for t in bplt.curplot().tools if isinstance(t, HoverTool)][0]
hover.tooltips = OrderedDict([
#("index", "$index"),
("(x,y)", "($x, $y)"),
("stefilename", "@stefilename"),
("Nmodes","@Nmodes"),
("Ntriples","@Ntriples"),
("Ngens","@Ngens"),
])
def test_grid(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.grid()), 2)
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 setUpModule():
global _embed_test_plot
_embed_test_plot = circle([1,2], [2,3])
def setUpModule():
global _embed_test_plot
_embed_test_plot = circle([1,2], [2,3])
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_grid(self):
plt.figure()
p = plt.circle([1,2,3], [1,2,3])
self.assertEqual(len(plt.grid()), 2)
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg)
# running the exploration
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N)
# making graphs
radius, alpha = 1.0, 0.35
if ex_name == 'distrib_corner':
radius, alpha = 0.75, 0.15
plotting.circle([0.0], [0.0],
radius=1.0,
x_range=(-xy_range, xy_range),
y_range=(-xy_range, xy_range),
fill_color='#000000',
fill_alpha=0.075,
line_color=None,
title='goal distribution for {}'.format(ex_name))
graphs.hold(True)
graphs.spread(ex.s_channels,
s_vectors=(),
s_goals=s_goals,
g_radius=radius,
g_alpha=alpha,
grid=None,
title='{} goals'.format(ex_name))
graphs.spread(ex.s_channels,
s_vectors=s_vectors,
s_goals=(),
