Develop Reference

Hide annotation alongside legend in Bokeh

I have an interactive Bokeh plot that's able to hide certain circle plots when clicking on the Legend. Now when I disable a plot by clicking on it, the plotted circles disappear but the annotations remain. Could someone explain to me how I can toggle those on/off all together or does anyone have a quick fix?
Here's a picture when it's toggled off:
I plot the circles + legend with the following code:
q.circle('lng', 'lat', source = source2, name='vri', color='red', size=5, hover_line_color="black", legend_label = 'VRI')
vri_labels = LabelSet(x='lng', y='lat', text='kruispuntn', x_offset=5, y_offset=5, source=source2, text_font_size = '10pt')
q.legend.location = "bottom_left"
q.legend.click_policy="hide"
q.add_layout(vri_labels)
show(q)

You can link the visible property via a CustomJS callback:
from bokeh.io import show
from bokeh.models import ColumnDataSource, LabelSet, CustomJS
from bokeh.plotting import figure
p = figure()
cds = ColumnDataSource(data=dict(x=[0, 1], y=[0, 1], z=[1, 0]))
for var, params in [('y', {}),
('z', {'color': 'green'})]:
renderer = p.circle('x', var, source=cds, legend_label=var, size=20, **params)
label_set = LabelSet(x='x', y=var, text=var, source=cds, x_offset=5, y_offset=5)
p.add_layout(label_set)
renderer.js_on_change('visible', CustomJS(args=dict(ls=label_set),
code="ls.visible = cb_obj.visible;"))
p.legend.click_policy = 'hide'
show(p)

Multiple HoverTools for different lines (bokeh)

I have more than one line on a bokeh plot, and I want the HoverTool to show the value for each line, but using the method from a previous stackoverflow answer isn't working:
https://stackoverflow.com/a/27549243/3087409
Here's the relevant code snippet from that answer:
fig = bp.figure(tools="reset,hover")
s1 = fig.scatter(x=x,y=y1,color='#0000ff',size=10,legend='sine')
s1.select(dict(type=HoverTool)).tooltips = {"x":"$x", "y":"$y"}
s2 = fig.scatter(x=x,y=y2,color='#ff0000',size=10,legend='cosine')
fig.select(dict(type=HoverTool)).tooltips = {"x":"$x", "y":"$y"}
And here's my code:
from bokeh.models import HoverTool
from bokeh.plotting import figure
source = ColumnDataSource(data=dict(
x = [list of datetimes]
wind = [some list]
coal = [some other list]
)
)
hover = HoverTool(mode = "vline")
plot = figure(tools=[hover], toolbar_location=None,
x_axis_type='datetime')
plot.line('x', 'wind')
plot.select(dict(type=HoverTool)).tooltips = {"y":"#wind"}
plot.line('x', 'coal')
plot.select(dict(type=HoverTool)).tooltips = {"y":"#coal"}
As far as I can tell, it's equivalent to the code in the answer I linked to, but when I hover over the figure, both hover tools boxes show the same value, that of the wind.

You need to add renderers for each plot. Check this. Also do not use samey label for both values change the names.
from bokeh.models import HoverTool
from bokeh.plotting import figure
source = ColumnDataSource(data=df)
plot = figure(x_axis_type='datetime',plot_width=800, plot_height=300)
plot1 =plot.line(x='x',y= 'wind',source=source,color='blue')
plot.add_tools(HoverTool(renderers=[plot1], tooltips=[('wind',"#wind")],mode='vline'))
plot2 = plot.line(x='x',y= 'coal',source=source,color='red')
plot.add_tools(HoverTool(renderers=[plot2], tooltips=[("coal","#coal")],mode='vline'))
show(plot)
The output look like this.

Manually change x range for Bokeh plot

So I'm trying to create a Bokeh plot for which I would like to be able to manually adapt the range of the X-Axis with a slider.
Being a newbie I've only managed to get this far and despite researching this subject I haven't been able to solve this problem.
Here my code:
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
from bokeh.plotting import Figure, output_file, show
output_file("test.html")
x = [x*0.5 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = Figure(plot_width=600, plot_height=400, x_range=(0, 100))
plot.line('x', 'y', source=source, line_width=2, line_alpha=0.75)
callback = CustomJS(args=dict(x_range=plot.x_range), code="""
var start = cb_obj.value
x_range.set({"start": start, "end": start+10})
""")
slider = Slider (start=0, end=90, value=20, step=10)
slider.js_on_change('value', callback)
layout = column(slider, plot)
show(layout)
My biggest problem is to understand how I connect the CustomJS to my plot.
I would gladly appreciate your help.

It has been answered at the Bokeh Google group
To reiterate, the only change needed is to replace x_range.set with x_range.setv.

Adding node labels to bokeh network plots

I am using the following code to produce an interactive bokeh network graph. How do I add the name of the nodes to the nodes in the bokeh plot?
from bokeh.io import show, output_notebook
from bokeh.models import Plot, Range1d, MultiLine, Circle, HoverTool, TapTool, BoxSelectTool
from bokeh.models.graphs import from_networkx, NodesAndLinkedEdges, EdgesAndLinkedNodes
from bokeh.palettes import Spectral4
from bokeh.models import LabelSet
plot = Plot(plot_width=900, plot_height=500,
x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
plot.title.text = "Graph Interaction Demonstration"
plot.add_tools(HoverTool(tooltips=None), TapTool(), BoxSelectTool())
graph_renderer = from_networkx(G, nx.circular_layout, scale=1, center=(0,0))
graph_renderer.node_renderer.glyph = Circle(size=15, fill_color=Spectral4[0])
graph_renderer.node_renderer.selection_glyph = Circle(size=15, fill_color=Spectral4[2])
graph_renderer.node_renderer.hover_glyph = Circle(size=15, fill_color=Spectral4[1])
graph_renderer.node_renderer.glyph.properties_with_values()
graph_renderer.edge_renderer.glyph = MultiLine(line_color="#CCCCCC", line_alpha=0.8, line_width=5)
graph_renderer.edge_renderer.selection_glyph = MultiLine(line_color=Spectral4[2], line_width=5)
graph_renderer.edge_renderer.hover_glyph = MultiLine(line_color=Spectral4[1], line_width=5)
graph_renderer.selection_policy = NodesAndLinkedEdges()
graph_renderer.inspection_policy = EdgesAndLinkedNodes()
plot.renderers.append(graph_renderer)
show(plot)
resulting bokeh networkx graph:

I guess you mean interactive labels for the nodes, something I also wanted to do. To achieve this, you need to modify a couple of lines:
hover = HoverTool(tooltips=[("Name:", "#name")])
plot.add_tools(hover, TapTool(), BoxSelectTool(), WheelZoomTool())
...
graph_renderer.inspection_policy = NodesAndLinkedEdges()
Then modify the data source for the nodes:
graph_renderer.node_renderer.data_source.data['name'] = [name1, ... ,nameN]
(the data source already exists; it's a dictionary for which the only key is 'index', a numbered list of nodes. Thus you can add more keys that reference lists of the same length - such as a list of names - and these lists can be accessed via '#key')

I found #SergioLucero's answer too incomplete to answer the question, the code sample is not working.
However, with code and answer created by #ifearthenight (from this question: Lining up labels with the nodes on a Bokeh figure generated from a NetworkX graph) I was able to produce a working example.
from bokeh.io import show, output_notebook
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, LabelSet
from bokeh.models.graphs import from_networkx
output_notebook()
G = nx.karate_club_graph()
plot = figure(title="Karate Club Graph", tools="", x_range=(-1.5, 1.5),
y_range=(-1.5, 1.5), toolbar_location=None)
graph = from_networkx(G, nx.spring_layout)
plot.renderers.append(graph)
x, y = zip(*graph.layout_provider.graph_layout.values())
node_labels = nx.get_node_attributes(G, 'club')
source = ColumnDataSource({'x': x, 'y': y,
'club': [node_labels[i] for i in range(len(x))]})
labels = LabelSet(x='x', y='y', text='club', source=source,
background_fill_color='white')
plot.renderers.append(labels)
show(plot)

Hot topic!! I think I just got it. They may be more pythonic ways, but what I did was
Create a DataSource with the relevant labels
Retrieve positions from the graph using pos = nx.circular_layout(G)
add these positions to my Datasource
Create a LabelSet from the positions and the source
Leonardo, you are missing the line where G gets created. I took the karate_club_graph and this works for me:
from bokeh.models import ColumnDataSource
pos = nx.circular_layout(G)
x,y=zip(*pos.values())
source = ColumnDataSource({'x':x,'y':y,'kid':['Joe %d' %ix for ix in range(len(x))]})
labels = LabelSet(x='x', y='y', text='kid', source=source)
plot.renderers.append(labels)

Bokeh hovertool in multiple_line plot

I'm new to bokeh and I just jumped right into using hovertool as that's why I wanted to use bokeh in the first place.
Now I'm plotting genes and what I want to achieve is multiple lines with the same y-coordinate and when you hover over a line you get the name and position of this gene.
I have tried to mimic this example, but for some reason the I can't even get it to show coordinates.
I'm sure that if someone who actually knows their way around bokeh looks at this code, the mistake will be apparent and I'd be very thankful if they showed it to me.
from bokeh.plotting import figure, HBox, output_file, show, VBox, ColumnDataSource
from bokeh.models import Range1d, HoverTool
from collections import OrderedDict
import random
ys = [10 for x in range(len(levelsdf2[(name, 'Start')]))]
xscale = zip(levelsdf2[('Log', 'Start')], levelsdf2[('Log', 'Stop')])
yscale = zip(ys,ys)
TOOLS="pan,wheel_zoom,box_zoom,reset,hover"
output_file("scatter.html")
hover_tips = levelsdf2.index.values
colors = ["#%06x" % random.randint(0,0xFFFFFF) for c in range(len(xscale))]
source = ColumnDataSource(
data=dict(
x=xscale,
y=yscale,
gene=hover_tips,
colors=colors,
)
)
p1 = figure(plot_width=1750, plot_height=950,y_range=[0, 15],tools=TOOLS)
p1.multi_line(xscale[1:10],yscale[1:10], alpha=1, source=source,line_width=10, line_color=colors[1:10])
hover = p1.select(dict(type=HoverTool))
hover.tooltips = [
("index", "$index"),
("(x,y)", "($x, $y)"),
]
show(p1)
the levelsdf2 is a pandas.DataFrame, if it matters.

I figured it out on my own. It turns out that version 0.8.2 of Bokeh doesn't allow hovertool for lines so I did the same thing using quads.
from bokeh.plotting import figure, HBox, output_file, show, VBox, ColumnDataSource
from bokeh.models import Range1d, HoverTool
from collections import OrderedDict
import random
xscale = zip(levelsdf2[('series1', 'Start')], levelsdf2[('series1', 'Stop')])
xscale2 = zip(levelsdf2[('series2', 'Start')], levelsdf2[('series2', 'Stop')])
yscale2 = zip([9.2 for x in range(len(levelsdf2[(name, 'Start')]))],[9.2 for x in range(len(levelsdf2[(name, 'Start')]))])
TOOLS="pan,wheel_zoom,box_zoom,reset,hover"
output_file("linesandquads.html")
hover_tips = levelsdf2.index.values
colors = ["#%06x" % random.randint(0,0xFFFFFF) for c in range(len(xscale))]
proc1 = 'Log'
proc2 = 'MazF2h'
expression1 = levelsdf2[(proc1, 'Level')]
expression2 = levelsdf2[(proc2, 'Level')]
source = ColumnDataSource(
data=dict(
start=[min(xscale[x]) for x in range(len(xscale))],
stop=[max(xscale[x]) for x in range(len(xscale))],
start2=[min(xscale2[x]) for x in range(len(xscale2))],
stop2=[max(xscale2[x]) for x in range(len(xscale2))],
gene=hover_tips,
colors=colors,
expression1=expression1,
expression2=expression2,
)
)
p1 = figure(plot_width=900, plot_height=500,y_range=[8,10.5],tools=TOOLS)
p1.quad(left="start", right="stop", top=[9.211 for x in range(len(xscale))],
bottom = [9.209 for x in range(len(xscale))], source=source, color="colors")
p1.multi_line(xscale2,yscale2, source=source, color="colors", line_width=20)
hover = p1.select(dict(type=HoverTool))
hover.tooltips = OrderedDict([
(proc1+" (start,stop, expression)", "(#start| #stop| #expression1)"),
("Gene","#gene"),
])
show(p1)
Works like a charm.
EDIT: Added a picture of the result, as requested and edited code to match the screenshot posted.
It's not the best solution as it turns out it's not all that easy to plot several series of quads on one plot. It's probably possible but as it didn't matter much in my use case I didn't investigate too vigorously.
As all genes are represented on all series at the same place I just added tooltips for all series to the quads and plotted the other series as multi_line plots on the same figure.
This means that if you hovered on the top line at 9.21 you'd get tooltips for the line at 9.2 as well, but If you hovered on the 9.2 line you wouldn't get a tooltip at all.