Sliders have format property, see
https://docs.bokeh.org/en/latest/docs/reference/models/widgets.sliders.html
A) Where is the documentation for this property?
B) Is there an example of using the format attribute?
EDIT: is there a way to pass a function that takes the slider value and returns a string?
Formatting documentation can be found on this page with multiple examples. The sliders value can be used by calling slider.value.
I also edited an example where I added a formatter for the amplitude slider. The slider values in this example are used to change the sine wave.
You can run this example by using this command: bokeh serve script.py --show
import numpy as np
from bokeh.io import curdoc
from bokeh.layouts import row, column
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import Slider, TextInput
from bokeh.plotting import figure
# Set up data
N = 200
x = np.linspace(0, 4*np.pi, N)
y = np.sin(x)
source = ColumnDataSource(data=dict(x=x, y=y))
# Set up plot
plot = figure(plot_height=400, plot_width=400, title="my sine wave",
tools="crosshair,pan,reset,save,wheel_zoom",
x_range=[0, 4*np.pi], y_range=[-2.5, 2.5])
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
# Set up widgets
text = TextInput(title="title", value='my sine wave')
offset = Slider(title="offset", value=0.0, start=-5.0, end=5.0, step=0.1)
amplitude = Slider(title="amplitude", value=1.0, start=-5.0, end=5.0, step=0.0000001, format='0.000f') #Slider with different formatting
phase = Slider(title="phase", value=0.0, start=0.0, end=2*np.pi)
freq = Slider(title="frequency", value=1.0, start=0.1, end=5.1, step=0.1)
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
a = amplitude.value
b = offset.value
w = phase.value
k = freq.value
# Generate the new curve
x = np.linspace(0, 4*np.pi, N)
y = a*np.sin(k*x + w) + b
source.data = dict(x=x, y=y)
for w in [offset, amplitude, phase, freq]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = column(text, offset, amplitude, phase, freq)
curdoc().add_root(row(inputs, plot, width=800))
curdoc().title = "Sliders"
Related
I'm trying to link a grid of images to a scatterplot, so that the right scatterplot point is highlighted when hovering over the corresponding image.
Problem is that when registering DOM events iteratively, they seem to get overwritten, and as a result only the last point of the scatterplot gets highlighted. How can I register independent events for each image.
See reproducible examples below.
import io
import random
import ipywidgets as ipw
from ipyevents import Event
import numpy as np
import PIL
from PIL import Image as PILImage
from bqplot import LinearScale, Figure, Axis, Scatter
# Random ipywidget image generator
def random_image():
arr = np.random.randint(255, size=(200, 200, 3), dtype=np.uint8)
img = PILImage.fromarray(arr)
with io.BytesIO() as fileobj:
img.save(fileobj, 'PNG')
img_b = fileobj.getvalue()
img_w = ipw.Image(value=img_b)
return img_w
# Create data
data = [{'x': idx, 'y': random.randint(1,10), 'image': random_image()}
for idx in range(1,6)]
# Create scatter plot
xs = LinearScale()
ys = LinearScale()
xa = Axis(scale=xs)
ya = Axis(scale=ys, orientation='vertical')
points = Scatter(x=[d['x'] for d in data],
y=[d['y'] for d in data],
scales={'x': xs, 'y': ys})
highlighted_point = Scatter(x=[-1000], y=[-1000], # Dummy point out of view
scales={'x': xs, 'y': ys},
preserve_domain={'x': True, 'y': True},
colors=['red'])
fig = Figure(marks=[points, highlighted_point], axes=[xa,ya])
# Add DOM events to images
img_list = []
for d in data:
img = d['image']
event = Event(source=img, watched_events=['mouseenter'])
def handle_event(event):
highlighted_point.x = [d['x']]
highlighted_point.y = [d['y']]
event.on_dom_event(handle_event)
img_list.append(img)
images = ipw.HBox(img_list)
ipw.VBox([fig, images])
I found a way to do it using functools.partial
import functools
# Event handler
def handle_event(idx, event):
highlighted_point.x = [data[idx]['x']]
highlighted_point.y = [data[idx]['y']]
# Add DOM events to images
img_list = []
for idx, d in enumerate(data):
img = d['image']
event = Event(source=img, watched_events=['mouseenter'])
event.on_dom_event(functools.partial(handle_event, idx))
img_list.append(img)
images = ipw.HBox(img_list)
ipw.VBox([fig, images])
I'm trying to add interaction to heatmap(using rect) using CustomJS to show another bokeh plot based on the selected value.
This is what I've tried
heat_map_df_stack = pd.DataFrame(heat_map_df.stack(), columns=['rate']).reset_index()
....
issue_heat_map = figure(title="",
x_range=issues, y_range=list(reversed(products)),
x_axis_location="above", plot_width=400, plot_height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('Product & Issue Id', '#Product #Issue'), ('Issue Count', '#rate')],
name='issue_heat_map')
....
issue_heat_map.rect(x="Issue", y="Product", width=1, height=1,
source=heat_map_df_stack,
fill_color={'field': 'rate', 'transform': mapper},
line_color=None)
....
taptool = issue_heat_map.select(type=TapTool)
taptool.callback = CustomJS(args = dict(source = ""), code =
"""
console.log('test')
console.log(cb_obj)
var inds = cb_obj.selected;
window.alert(inds);
""")
On click of the rect or selection, nothing is happening now.
[Edit] : I updated the above code. Now I'm able to see console log and alert, but have no clue on how to get selected value from the heat map.
Here is a version using the bokeh server. The code is a adaption of the heatmap example from the bokeh gallery.
from math import pi
import pandas as pd
import numpy as np
from bokeh.io import curdoc
from bokeh.models import LinearColorMapper, BasicTicker, PrintfTickFormatter, ColorBar
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.layouts import gridplot
from bokeh.sampledata.unemployment1948 import data
data['Year'] = data['Year'].astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'
years = list(data.index)
months = list(data.columns)
# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()
source = ColumnDataSource(df)
# this is the colormap from the original NYTimes plot
colors = ["#75968f", "#a5bab7", "#c9d9d3", "#e2e2e2", "#dfccce", "#ddb7b1", "#cc7878", "#933b41", "#550b1d"]
mapper = LinearColorMapper(palette=colors, low=df.rate.min(), high=df.rate.max())
TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom, tap"
p = figure(title="US Unemployment ({0} - {1})".format(years[0], years[-1]),
x_range=years, y_range=list(reversed(months)),
x_axis_location="above", plot_width=900, plot_height=400,
tools=TOOLS, toolbar_location='below',
tooltips=[('date', '#Month #Year'), ('rate', '#rate%')])
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "5pt"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
heatmap = p.rect(x="Year", y="Month", width=1, height=1,
source=source,
fill_color={'field': 'rate', 'transform': mapper},
line_color=None)
color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size="5pt",
ticker=BasicTicker(desired_num_ticks=len(colors)),
formatter=PrintfTickFormatter(format="%d%%"),
label_standoff=6, border_line_color=None, location=(0, 0))
p.add_layout(color_bar, 'right')
# Adding the tap interaction + plot
other_source = ColumnDataSource({'x': range(10), 'y': range(10)})
other_plot = figure(title="Other Plot")
other_line = other_plot.line(x='x', y='y', source=other_source)
def update(attr, old, new):
if not old:
old = [1]
if new:
other_source.data.update(y=np.array(other_source.data['y'])/old[0]*new[0])
source.selected.on_change('indices', update)
curdoc().add_root(gridplot([[p, other_plot]]))
The important part are the last few lines, where I set up the second plot and add the update function to change the slope of the line in the second plot according to the selected rect from the heatmap.
It is kind of a complex example, but I desperately hope to get help...
I'm using jupyter-notebook 5.2.0, bokeh version is 0.12.9 and ipywidgets is 7.0.1.
Here is my DataFrame df:
import numpy as np
import pandas as pd
import datetime
import string
start = int(datetime.datetime(2017,1,1).strftime("%s"))
end = int(datetime.datetime(2017,12,31).strftime("%s"))
# set parameters of DataFrame df for simualtion
size, numcats = 100,10
rints = np.random.randint(start, end + 1, size = size)
df = pd.DataFrame(rints, columns = ['zeit'])
df["bytes"] = np.random.randint(5,20,size=size)
df["attr1"] = np.random.randint(5,100,size=size)
df["ind"] = ["{}{}".format(i,j) for i in string.ascii_uppercase for j in string.ascii_uppercase][:len(df)]
choices = list(string.ascii_uppercase)[:numcats]
df['who']= np.random.choice(choices, len(df))
df["zeit"] = pd.to_datetime(df["zeit"], unit='s')
df.zeit = df.zeit.dt.date
df.sort_values('zeit', inplace = True)
df = df.reset_index(drop=True)
df.head(3)
Now, let's create a bar plot, also using hover tool:
from bokeh.io import show, output_notebook, push_notebook
from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
import ipywidgets as widgets
output_notebook()
# setup figure
hover = HoverTool(tooltips=[
("index", "$index"),
("ind", "#ind"),
("who", "#who"),
("bytes", "#bytes"),
("attr1", "#attr1"),
])
fig = figure(x_range=list(df.ind), plot_height=250, title="Test Bars",
toolbar_location=None, tools=[hover])
x = fig.vbar(x="ind", top="bytes", width=0.9, source=ColumnDataSource(df))
h=show(fig, notebook_handle=True)
I'm using a ipywidgets.widgets.SelectionRangeSlider to select a range of dates:
import ipywidgets as widgets
# create slider
dates = list(pd.date_range(df.zeit.min(), df.zeit.max(), freq='D'))
options = [(i.strftime('%d.%m.%Y'), i) for i in dates]
index = (0, len(dates)-1)
myslider = widgets.SelectionRangeSlider(
options = options,
index = index,
description = 'Test',
orientation = 'horizontal',
layout={'width': '500px'}
)
def update_source(df, start, end):
x = df[(df.zeit >= start) & (df.zeit < end)]
#data = pd.DataFrame(x.groupby('who')['bytes'].sum())
#data.sort_values(by="bytes", inplace=True)
#data.reset_index(inplace=True)
#return data
return x
def gui(model, bars):
def myupdate(control1):
start = control1[0].date()
end = control1[1].date()
#display(update_source(model, start, end).head(4))
data = update_source(model, start, end)
return myupdate
widgets.interactive(gui(df, x), control1 = myslider)
The problem is, I can't get an update to the graph from the widget:
x.data_source = ColumnDataSource(update_source(df, myslider.value[0].date(), myslider.value[1].date()))
push_notebook(handle=h)
At least, it does something with the plot, as hover is not working anymore...
What am I missing? Or is this a bug?
Thanks for any help
Markus
Figured out how to do it using bokeh: https://github.com/bokeh/bokeh/issues/7082, but unfortunately it only works sometimes...
Best to use CDSViewer.
I'm trying to do something that I'd normally consider trivial but seems to be very difficult in bokeh: Adding a vertical colorbar to a plot and then having the title of the colorbar (a.k.a. the variable behind the colormapping) appear to one side of the colorbar but rotated 90 degrees clockwise from horizontal.
From what I can tell of the bokeh ColorBar() interface (looking at both documentation and using the python interpreter's help() function for this element), this is not possible. In desperation I have added my own Label()-based annotation. This works but is klunky and displays odd behavior when deployed in a bokeh serve situation--that the width of the data window on the plot varies inversely with the length of the title colorbar's title string.
Below I've included a modified version of the bokeh server mpg example. Apologies for its complexity, but I felt this was the best way to illustrate the problem using infrastructure/data that ships with bokeh. For those unfamiliar with bokeh serve, this code snippet needs to saved to a file named main.py that resides in a directory--for the sake of argument let's say CrossFilter2--and in the parent directory of CrossFilter2 one needs to invoke the command
bokeh serve --show CrossFilter2
this will then display in a browser window (localhost:5006/CrossFilter2) and if you play with the color selection widget you will see what I mean, namely that short variable names such as 'hp' or 'mpg' result in a wider data display windows than longer variable names such as 'accel' or 'weight'. I suspect that there may be a bug in how label elements are sized--that their x and y dimensions are swapped--and that bokeh has not understood that the label element has been rotated.
My questions are:
Must I really have to go to this kind of trouble to get a simple colorbar label feature that I can get with little-to-no trouble in matplotlib/plotly?
If I must go through the hassle you can see in my sample code, is there some other way I can do this that avoids the data window width problem?
import numpy as np
import pandas as pd
from bokeh.layouts import row, widgetbox
from bokeh.models import Select
from bokeh.models import HoverTool, ColorBar, LinearColorMapper, Label
from bokeh.palettes import Spectral5
from bokeh.plotting import curdoc, figure, ColumnDataSource
from bokeh.sampledata.autompg import autompg_clean as df
df = df.copy()
SIZES = list(range(6, 22, 3))
COLORS = Spectral5
# data cleanup
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
columns = sorted(df.columns)
discrete = [x for x in columns if df[x].dtype == object]
continuous = [x for x in columns if x not in discrete]
quantileable = [x for x in continuous if len(df[x].unique()) > 20]
def create_figure():
xs = df[x.value].tolist()
ys = df[y.value].tolist()
x_title = x.value.title()
y_title = y.value.title()
name = df['name'].tolist()
kw = dict()
if x.value in discrete:
kw['x_range'] = sorted(set(xs))
if y.value in discrete:
kw['y_range'] = sorted(set(ys))
kw['title'] = "%s vs %s" % (y_title, x_title)
p = figure(plot_height=600, plot_width=800,
tools='pan,box_zoom,wheel_zoom,lasso_select,reset,save',
toolbar_location='above', **kw)
p.xaxis.axis_label = x_title
p.yaxis.axis_label = y_title
if x.value in discrete:
p.xaxis.major_label_orientation = pd.np.pi / 4
if size.value != 'None':
groups = pd.qcut(df[size.value].values, len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
else:
sz = [9] * len(xs)
if color.value != 'None':
coloring = df[color.value].tolist()
cv_95 = np.percentile(np.asarray(coloring), 95)
mapper = LinearColorMapper(palette=Spectral5,
low=cv_min, high=cv_95)
mapper.low_color = 'blue'
mapper.high_color = 'red'
add_color_bar = True
ninety_degrees = pd.np.pi / 2.
color_bar = ColorBar(color_mapper=mapper, title='',
#title=color.value.title(),
title_text_font_style='bold',
title_text_font_size='20px',
title_text_align='center',
orientation='vertical',
major_label_text_font_size='16px',
major_label_text_font_style='bold',
label_standoff=8,
major_tick_line_color='black',
major_tick_line_width=3,
major_tick_in=12,
location=(0,0))
else:
c = ['#31AADE'] * len(xs)
add_color_bar = False
if add_color_bar:
source = ColumnDataSource(data=dict(x=xs, y=ys,
c=coloring, size=sz, name=name))
else:
source = ColumnDataSource(data=dict(x=xs, y=ys, color=c,
size=sz, name=name))
if add_color_bar:
p.circle('x', 'y', fill_color={'field': 'c',
'transform': mapper},
line_color=None, size='size', source=source)
else:
p.circle('x', 'y', color='color', size='size', source=source)
p.add_tools(HoverTool(tooltips=[('x', '#x'), ('y', '#y'),
('desc', '#name')]))
if add_color_bar:
color_bar_label = Label(text=color.value.title(),
angle=ninety_degrees,
text_color='black',
text_font_style='bold',
text_font_size='20px',
x=25, y=300,
x_units='screen', y_units='screen')
p.add_layout(color_bar, 'right')
p.add_layout(color_bar_label, 'right')
return p
def update(attr, old, new):
layout.children[1] = create_figure()
x = Select(title='X-Axis', value='mpg', options=columns)
x.on_change('value', update)
y = Select(title='Y-Axis', value='hp', options=columns)
y.on_change('value', update)
size = Select(title='Size', value='None',
options=['None'] + quantileable)
size.on_change('value', update)
color = Select(title='Color', value='None',
options=['None'] + quantileable)
color.on_change('value', update)
controls = widgetbox([x, y, color, size], width=200)
layout = row(controls, create_figure())
curdoc().add_root(layout)
curdoc().title = "Crossfilter"
You can add a vertical label to the Colorbar by plotting it on a separate axis and adding a title to this axis. To illustrate this, here's a modified version of Bokeh's standard Colorbar example (found here):
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.models import LogColorMapper, LogTicker, ColorBar
from bokeh.layouts import row
plot_height = 500
plot_width = 500
color_bar_height = plot_height + 11
color_bar_width = 180
output_file('color_bar.html')
def normal2d(X, Y, sigx=1.0, sigy=1.0, mux=0.0, muy=0.0):
z = (X-mux)**2 / sigx**2 + (Y-muy)**2 / sigy**2
return np.exp(-z/2) / (2 * np.pi * sigx * sigy)
X, Y = np.mgrid[-3:3:100j, -2:2:100j]
Z = normal2d(X, Y, 0.1, 0.2, 1.0, 1.0) + 0.1*normal2d(X, Y, 1.0, 1.0)
image = Z * 1e6
color_mapper = LogColorMapper(palette="Viridis256", low=1, high=1e7)
plot = figure(x_range=(0,1), y_range=(0,1), toolbar_location=None,
width=plot_width, height=plot_height)
plot.image(image=[image], color_mapper=color_mapper,
dh=[1.0], dw=[1.0], x=[0], y=[0])
Now, to make the Colorbar, create a separate dummy plot, add the Colorbar to the dummy plot and place it next to the main plot. Add the Colorbar label as the title of the dummy plot and center it appropriately.
color_bar = ColorBar(color_mapper=color_mapper, ticker=LogTicker(),
label_standoff=12, border_line_color=None, location=(0,0))
color_bar_plot = figure(title="My color bar title", title_location="right",
height=color_bar_height, width=color_bar_width,
toolbar_location=None, min_border=0,
outline_line_color=None)
color_bar_plot.add_layout(color_bar, 'right')
color_bar_plot.title.align="center"
color_bar_plot.title.text_font_size = '12pt'
layout = row(plot, color_bar_plot)
show(layout)
This gives the following output image:
One thing to look out for is that color_bar_width is set wide enough to incorporate both the Colorbar, its axes labels and the Colorbar label. If the width is set too small, you will get an error and the plot won't render.
As of Bokeh 0.12.10 there is no built in label available for colorbars. In addition to your approach or something like it, another possibility would be a custom extension, though that is similarly not trivial.
Offhand, a colobar label certainly seems like a reasonable thing to consider. Regarding the notion that it ought to be trivially available, if you polled all users about what they consider should be trivially available, there will be thousands of different suggestions for what to prioritize. As is very often the case in the OSS world, there are far more possible things to do, than there are people to do them (less than 3 in this case). So, would first suggest a GitHub Issue to request the feature, and second, if you have the ability, volunteering to help implement it. Your contribution would be valuable and appreciated by many.
my short script looks like the following:
output_server('ts_sample.html')
count = 0
def update_title(attrname, old, new):
global count
count = count + 1
textInput = TextInput(title="query_parameters", name='fcp_chp_id', value='fcp_chp_id')
textInput.on_change('value', update_title)
curdoc().add_root(textInput)
p = figure( width=800, height=650,title="ts_sample",x_axis_label='datetime' )
p.line(np.array(data['date_trunc'].values, dtype=np.datetime64), data['latitude'], legend="test")
p.xaxis[0].formatter=bkmodels.formatters.DatetimeTickFormatter(formats=dict(hours=["%F %T"]))
show(curdoc())
It works, when bokeh server(bokeh serve) is running and I got the plotting, but on_change callback doesn't work as expected.
Assumed the value of textInput should be the content/string in the input box, but I changed it multiple times but the callback function update_title is never called (the count global variable is always 0). So apparently the underlying textInput.value is not changed, how can I change value attr and trigger the on_change function ?
Here's a simple TextInput example using a callback rather than .on_change(). This might be more helpful for beginners like me than the OP. I very slightly modified the slider example from
http://docs.bokeh.org/en/latest/docs/user_guide/interaction/callbacks.html#customjs-for-model-property-events
from bokeh.layouts import column
from bokeh.models import CustomJS, ColumnDataSource, Slider
from bokeh.models import TextInput
from bokeh.plotting import figure, show
x = [x*0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source), code="""
var data = source.get('data');
var f = cb_obj.get('value')
x = data['x']
y = data['y']
for (i = 0; i < x.length; i++) {
y[i] = Math.pow(x[i], f)
}
source.trigger('change');
""")
#slider = Slider(start=0.1, end=4, value=1, step=.1, title="power", callback=callback)
#layout = vform(slider, plot)
text_input = TextInput(value="1", title="power", callback=callback)
layout = column(text_input, plot)
show(layout)
I have the same problem as you .
After search, the on_change function not working with bokeh 0.10 realease but with the upcoming version 0.11 .
From : https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/MyztWSef4tI
If you are using the (new) Bokeh server in the latest dev builds, you can follow this example, for instance:
https://github.com/bokeh/bokeh/blob/master/examples/app/sliders.py
From : https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/PryxrZPX2QQ
The server has recently been completely re-written from the ground up
and is faster, smaller/simpler, and much easier to use and deploy and
explain. The major PR was just merged into master, and will show up in
the upcoming 0.11 release in December
For download the dev version : https://anaconda.org/bokeh/bokeh/files
I adapt the example, it may be helpful:
from bokeh.layouts import column
from bokeh.models import TextInput
from bokeh.models import CustomJS, ColumnDataSource, Slider
from bokeh.plotting import figure, show
x = [x*0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
plot = figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var f = cb_obj.value;
x = data['x']
y = data['y']
for (i = 0; i < x.length; i++) {
y[i] = Math.pow(x[i], f)
}
source.change.emit();
""")
#slider = Slider(start=0.1, end=4, value=1, step=.1, title="power", callback=callback)
#layout = vform(slider, plot)
text_input = TextInput(value="1", title="power", callback=callback)
layout = column(text_input, plot)
show(layout)