def create_multi_line_plot_renderer(x_index, y_index, data, amplitude=0.5):
# Create the data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=data)
xs = ArrayDataSource(x_index, sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(y_index, sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
mlp = MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=xrange),
value_mapper=LinearMapper(range=yrange),
value=ds,
global_max=np.nanmax(data),
global_min=np.nanmin(data),
#use_global_bounds=True,
#default_origin='top left', origin='top left',
#**kw
)
mlp.normalized_amplitude = amplitude
return mlp
def get_quiver_plot():
NPOINTS = 250
# points are distributed uniformly between -1 and 1
xs = np.random.uniform(low=-1.0, high=1.0, size=(NPOINTS, ))
ys = np.random.uniform(low=-1.0, high=1.0, size=(NPOINTS, ))
x, y = get_data_sources(x=xs, y=ys)
x_mapper, y_mapper = get_mappers(x, y)
# vectors are tangent to a circle centered in (0, 0) and the size depends
# on the radius
r = np.sqrt(xs * xs + ys * ys) * 20.0
v = r * np.array([-np.sin(np.arctan2(ys, xs)), np.cos(np.arctan2(ys, xs))])
v_source = MultiArrayDataSource(v.T)
quiver_plot = QuiverPlot(index=x,
value=y,
vectors=v_source,
index_mapper=x_mapper,
value_mapper=y_mapper,
aspect_ratio=1.0)
add_axes(quiver_plot, x_label='x', y_label='y')
return quiver_plot
def __init__(self, x_index, y_index, data, **kw):
super(MyPlot, self).__init__(**kw)
# Create the data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=data)
xs = ArrayDataSource(x_index, sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(y_index, sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
mlp = MultiLinePlot(index=xs,
yindex=ys,
index_mapper=LinearMapper(range=xrange),
value_mapper=LinearMapper(range=yrange),
value=ds,
global_max=np.nanmax(data),
global_min=np.nanmin(data),
**kw)
self.plot = Plot()
self.plot.add(mlp)
def _multi_line_plot_renderer_default(self):
"""Create the default MultiLinePlot instance."""
xs = ArrayDataSource(self.model.x_index, sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(self.model.y_index, sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
# The data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=self.model.data)
multi_line_plot_renderer = \
MultiLinePlot(
index = xs,
yindex = ys,
index_mapper = LinearMapper(range=xrange),
value_mapper = LinearMapper(range=yrange),
value=ds,
global_max = self.model.data.max(),
global_min = self.model.data.min())
return multi_line_plot_renderer
def _signals_data_model_changed(self, old, new):
print('model_changed')
xs = ArrayDataSource(self.signals_data_model.x_index,
sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(self.signals_data_model.y_index,
sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
# The data source for the MultiLinePlot.
print('ds')
ds = MultiArrayDataSource(data=self.signals_data_model.data)
self.signals_renderer.set(
index=xs,
yindex=ys,
#index_mapper = LinearMapper(range=xrange), value_mapper = LinearMapper(range=yrange),
value=ds,
global_max=self.signals_data_model.data.max(),
global_min=self.signals_data_model.data.min())
self.signals_renderer.index_mapper.range = xrange
self.signals_renderer.value_mapper.range = yrange
self.signals_renderer.request_redraw()
def _signals_renderer_default(self):
print('_signals_renderer_default')
"""Create the default MultiLinePlot instance."""
xs = ArrayDataSource(self.signals_data_model.x_index,
sort_order='ascending')
xrange = DataRange1D()
xrange.add(xs)
ys = ArrayDataSource(self.signals_data_model.y_index,
sort_order='ascending')
yrange = DataRange1D()
yrange.add(ys)
# The data source for the MultiLinePlot.
ds = MultiArrayDataSource(data=self.signals_data_model.data)
multi_line_plot_renderer = \
MultiLinePlot(
index = xs,
yindex = ys,
index_mapper = LinearMapper(range=xrange),
value_mapper = LinearMapper(range=yrange),
value=ds,
global_max = self.signals_data_model.data.max(),
global_min = self.signals_data_model.data.min(),
fast_clip = False)
# Add pan tool
multi_line_plot_renderer.tools.append(
PanTool(multi_line_plot_renderer, restrict_to_data=True))
# Add zoom tool
multi_line_plot_renderer.overlays.append(
ZoomTool(multi_line_plot_renderer,
tool_mode="range",
always_on=False,
x_max_zoom_factor=20.0,
y_max_zoom_factor=20.0,
x_min_zoom_factor=1.0,
y_min_zoom_factor=1.0,
zoom_to_mouse=True))
#multi_line_plot_renderer.overlays.append(LineInspector(multi_line_plot_renderer, axis="index",write_metadata=True,is_listener=True))
# multi_line_plot_renderer.overlays.append(LineInspector(multi_line_plot_renderer, axis='value',
# write_metadata=True,
# is_listener=True))
multi_line_plot_renderer.overlays.append(
LineInspector(multi_line_plot_renderer,
axis="index",
write_metadata=True,
is_listener=True))
return multi_line_plot_renderer
def get_multiline_plot():
prng = np.random.RandomState(seed=1234)
x = np.linspace(0, 10, 50)
y_data = np.column_stack([
x**2,
50 * np.sin(x),
50 * np.cos(x),
0.5 * x**2 + 2 * prng.randn(50),
0.7 * x**2 + prng.randn(50),
])
# data sources for the two axes
xs = ArrayDataSource(np.arange(50))
ys = ArrayDataSource(np.arange(y_data.shape[1]))
y_range = DataRange1D(low=-0.5, high=y_data.shape[1] - 0.5)
y_mapper = LinearMapper(range=y_range)
# data source for the multiple lines
lines_source = MultiArrayDataSource(data=y_data.T)
colors = ['blue', 'green', 'yellow', 'orange', 'red']
def color_generator(color_idx):
return color_table[colors[color_idx]]
multiline_plot = MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=DataRange1D(xs)),
value_mapper=y_mapper,
value=lines_source,
normalized_amplitude=1.0,
use_global_bounds=False,
color_func=color_generator,
**PLOT_DEFAULTS)
add_axes(multiline_plot, x_label='Days', y_label='Stock price changes')
y_grid = PlotGrid(mapper=y_mapper,
orientation="horizontal",
line_style="dot",
component=multiline_plot)
multiline_plot.overlays.append(y_grid)
return multiline_plot
def get_multiline_plot():
prices = datasets.fetch_mldata('regression-datasets stock')
T, N_LINES = 70, 5
prices_data = prices['data'][:T, :N_LINES]
prices_data -= prices_data[0, :]
# data sources for the two axes
xs = ArrayDataSource(np.arange(T))
ys = ArrayDataSource(np.arange(N_LINES))
y_range = DataRange1D(low=-0.5, high=N_LINES - 0.5)
y_mapper = LinearMapper(range=y_range)
# data source for the multiple lines
lines_source = MultiArrayDataSource(data=prices_data.T)
colors = ['blue', 'green', 'yellow', 'orange', 'red']
def color_generator(color_idx):
return color_table[colors[color_idx]]
multiline_plot = MultiLinePlot(
index=xs,
yindex=ys,
index_mapper=LinearMapper(range=DataRange1D(xs)),
value_mapper=y_mapper,
value=lines_source,
normalized_amplitude=1.0,
use_global_bounds=False,
color_func=color_generator,
**PLOT_DEFAULTS)
add_axes(multiline_plot, x_label='Days', y_label='Stock price changes')
y_grid = PlotGrid(mapper=y_mapper,
orientation="horizontal",
line_style="dot",
component=multiline_plot)
multiline_plot.overlays.append(y_grid)
return multiline_plot