def jitter(field_name, width, mean=0, distribution="uniform", range=None):
''' Create a ``DataSpec`` dict to apply a client-side ``Jitter``
transformation to a ``ColumnDataSource`` column.
Args:
field_name (str) : a field name to configure ``DataSpec`` with
width (float) : the width of the random distribition to apply
mean (float, optional) : an offset to apply (default: 0)
distribution (str, optional) : ``"uniform"`` or ``"normal"``
(default: ``"uniform"``)
range (Range, optional) : a range to use for computing synthetic
coordinates when necessary, e.g. a ``FactorRange`` when the
column data is categorical (default: None)
Returns:
dict
'''
return field(
field_name,
Jitter(mean=mean, width=width, distribution=distribution, range=range))
import numpy as np
from bokeh.io import vplot
from bokeh.plotting import figure, show, output_file
from bokeh.models.sources import ColumnDataSource
from bokeh.models import CustomJS, Button, Label
from bokeh.models.transforms import Jitter
N = 1000
source = ColumnDataSource(data=dict(x=np.ones(N),
xn=2 * np.ones(N),
xu=3 * np.ones(N),
y=np.random.random(N) * 10))
normal = Jitter(width=0.2, distribution="normal")
uniform = Jitter(width=0.2, distribution="uniform")
p = figure(x_range=(0, 4), y_range=(0, 10))
p.circle(x='x', y='y', color='firebrick', source=source, size=5, alpha=0.5)
p.circle(x='xn', y='y', color='olive', source=source, size=5, alpha=0.5)
p.circle(x='xu', y='y', color='navy', source=source, size=5, alpha=0.5)
label_data = ColumnDataSource(data=dict(
x=[1, 2, 3], y=[10, 10, 10], t=['Original', 'Normal', 'Uniform']))
labels = Label(x='x',
y='y',
text='t',
y_offset=2,
source=label_data,
render_mode='css',