Hi Dustin,
You can definitely do this. The JS potting interface is not quite as developed and documented as the python side yet, but it is possible to pass more than one glyph and source to make plot. You scan do:
plot = Bokeh.Plotting.make_plot([image, marker], [window.img, window.marker], options)
Where “marker” is a glyph spec for whatever glyph you want to show at the maximum value. Then to update it you simply update the data in the window.marker, something like:
window.marker.set('data', {x: [newx], y: [newy]})
@source.trigger('change', window.marker, {})
whenever the (x,y) for the max value changes.
Let me know if this is enough to get you going, if not I can try to work up a complete example.
BTW we are definitely interested in expanding and improving the BokehJS api, so if yo have any thoughts or ideas (or contributions!) please don’t hesitate to share them here on on GitHub.
Thanks,
Bryan
On May 1, 2014, at 2:01 PM, Dustin Maas dustin.314@gmail.com wrote:
Hello,
I’ve built a simple web application that uses BokehJS to display and update an image glyph every time new data is received via a websocket. I would like to add a marker glyph on top of the image glyph to show the location of the maximum image value, but I can’t find any information in the BokehJS Interface Reference on how to do this. Is it possible? If so, how can I accomplish this?
Thank you for your help,
Dustin
Here is the CoffeeScript for my real-time image plot:
$ →
window.first_message = 1
window.ws = new ReconnectingWebSocket ‘ws://’ + document.domain + ‘:8080/ws’
ws.onopen = () → console.log ‘open’
ws.onmessage = (e) →
console.log e
msg = JSON.parse e.data
console.log msg
if window.first_message then init_plot(msg) else update(msg)
ws.onclose = (e) →
console.log 'websocket closed!!'
ws.onerror = (e) →
console.log 'error'
init_plot = (msg) →
window.img = Bokeh.Collections('ColumnDataSource').create(
data:
image: [msg.img_vector]
)
image = {
type: 'image',
x: msg.x_min
y: msg.y_min
dw: msg.x_max
dh: msg.y_max
image: 'image'
rows: msg.y_pixels
cols: msg.x_pixels
palette: 'Spectral-10'
}
options = {
dims: [20 * msg.x_pixels, 20 * msg.y_pixels]
xrange: [0, msg.x_max]
yrange: [0, msg.y_max]
xaxes: 'min'
yaxes: 'min'
tools: true
}
window.first_message = 0
plot = Bokeh.Plotting.make_plot(image, window.img, options)
Bokeh.Plotting.show(plot)
update = (msg) →
data = window.img.get('data')
data['image'] = [msg.img_vector]
window.img.set('data', data)
window.img.trigger('change', img, {})
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