CurveEdit#
Download this notebook from GitHub (right-click to download).
Title: CurveEdit Stream#
Description: A linked streams example demonstrating how to use the CurveEdit stream.
Dependencies: Bokeh
Backends: Bokeh
import numpy as np
import holoviews as hv
from holoviews import opts, streams
from holoviews.plotting.links import DataLink
hv.extension('bokeh')
The CurveEdit stream adds a bokeh tool to the source plot, which allows drawing, dragging and deleting points and making the drawn data available to Python. The tool supports the following actions:
Move vertex
Tap and drag an existing vertex, the vertex will be dropped once you let go of the mouse button.
Delete vertex
Tap a vertex to select it then press BACKSPACE or DELETE key while the mouse is within the plot area.
As a simple example we will create a CurveEdit stream and attach it to a Curve with a simple timeseries. By using a DataLink we then link the tool to a Table.
If we select the PointDraw tool (
) the vertices will appear and allow us to drag and delete vertex. We can also see the x/y position change in the table and edit it. To change the appearance of the vertices we can supply a style to the CurveEdit stream:
curve = hv.Curve(np.random.randn(10).cumsum())
curve_stream = streams.CurveEdit(data=curve.columns(), source=curve, style={'color': 'black', 'size': 10})
table = hv.Table(curve).opts(editable=True)
DataLink(curve, table)
(curve + table).opts(
opts.Table(editable=True))
Whenever the data source is edited the data is synced with Python, both in the notebook and when deployed on the bokeh server. The data is made available as a dictionary of columns:
curve_stream.data
{'x': [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
'y': [1.534555575352807,
1.6594132457455872,
0.3570368116133824,
0.9742042174161603,
1.888964649368814,
3.2537392621677204,
4.626673141832312,
4.1225966350391685,
5.143641253768031,
5.392198904779841]}
Alternatively we can use the element property to get an Element containing the returned data:
curve_stream.element
Download this notebook from GitHub (right-click to download).