ANN: Bokeh 0.7 released

Community Support
1

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.

Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

  • IPython widgets and animations without a Bokeh server

  • Touch UI working for tools on mobile devices

  • Vastly improved linked data table

  • More new (and improving) bokeh.charts (high level charting interface)

  • Color mappers on the python side

  • Improved toolbar

  • Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

  • Simplifying production and multi-user Bokeh server deployments

  • Colorbar axis and axis location inspectors

  • Better support for maps and projections

As usual, don’t forget to check out the full documentation, interactive gallery, and tutorial at

http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install it with conda:

conda install bokeh

Alternatively, you can install it with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

http://cdn.pydata.org/bokeh-0.7.0.min.js

http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

2

This project seems to be progressing nicely but there is one fundamental thing which stops me from actually using it: how do you save images programatically (from python) for publications? It seems great for interactive exploration and fine-tuning your chart, but when you are happy with it and only want to update it from within the simulation script, what are you supposed to do? A quick search on the net seems to say “there is no solution yet”. Is there a roadmap to add such a feature? Some kind of “server backend” maybe? On a related note, “Preview/Save->right-click-Save as” is really awkard (even for one-time charts) but I guess this is not fixable.

I also found two minor glitches in this release:

  • the help text of the help button in the toolbar is outdated (not need for shift for wheel zoom)

  • I cannot reach the “learn more” link in the help text (the tooltip disappears) (using FF/Windows)

  • the last few charts in the gallery (at http://bokeh.pydata.org/docs/gallery.html) are wrong/out of sync: the details when you click on them do not correspond to the thumbnail.

Thanks for any advice,

Gaëtan

···

On Mon, Dec 8, 2014 at 4:27 PM, Damian Avila [email protected] wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.

Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

  • IPython widgets and animations without a Bokeh server
  • Touch UI working for tools on mobile devices
  • Vastly improved linked data table
  • More new (and improving) bokeh.charts (high level charting interface)
  • Color mappers on the python side
  • Improved toolbar
  • Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

  • Simplifying production and multi-user Bokeh server deployments
  • Colorbar axis and axis location inspectors
  • Better support for maps and projections

As usual, don’t forget to check out the full documentation, interactive gallery, and tutorial at

http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install it with conda:

conda install bokeh

Alternatively, you can install it with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

http://cdn.pydata.org/bokeh-0.7.0.min.js

http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

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3

Hi Gaëtan,

I agree this is a very desirable and prominent missing feature. Unfortunately there are some very substantial technical hurdles to getting the data saved programmatically if rendering happens on a browser (either a real browser, or PhantomJS), and right now there are no other backends to render to. Long term, there is hope that MEP 25 (Create new page · matplotlib/matplotlib Wiki · GitHub) will provide a mechanism to render Bokeh statically using Matplotlib as a backend, but I do not have any insight or estimate for when that work might be completed (or started) by the MPL project. It's possible that in the shorter term we will have to consider adding our own static rendering backend (most likely an ad-hoc plumbing to MPL or Chaco) but I don't savor the idea and it would probably not be 100% complete.

That said there is lots of interest in this, so we will definitely makes it a topic of discussion amongst the team to see what might be doable in the near future.

Bryan

···

On Dec 8, 2014, at 1:04 PM, Gaëtan de Menten <[email protected]> wrote:

This project seems to be progressing nicely but there is one fundamental thing which stops me from actually using it: how do you save images programatically (from python) for publications? It seems great for interactive exploration and fine-tuning your chart, but when you are happy with it and only want to update it from within the simulation script, what are you supposed to do? A quick search on the net seems to say "there is no solution yet". Is there a roadmap to add such a feature? Some kind of "server backend" maybe? On a related note, "Preview/Save->right-click-Save as" is really awkard (even for one-time charts) but I guess this is not fixable.

I also found two minor glitches in this release:
* the help text of the help button in the toolbar is outdated (not need for shift for wheel zoom)
* I cannot reach the "learn more" link in the help text (the tooltip disappears) (using FF/Windows)
* the last few charts in the gallery (at http://bokeh.pydata.org/docs/gallery.html\) are wrong/out of sync: the details when you click on them do not correspond to the thumbnail.

Thanks for any advice,
Gaëtan

On Mon, Dec 8, 2014 at 4:27 PM, Damian Avila <[email protected]> wrote:
On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.
Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

* IPython widgets and animations without a Bokeh server
* Touch UI working for tools on mobile devices
* Vastly improved linked data table
* More new (and improving) bokeh.charts (high level charting interface)
* Color mappers on the python side
* Improved toolbar
* Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh\. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

* Simplifying production and multi-user Bokeh server deployments
* Colorbar axis and axis location inspectors
* Better support for maps and projections

As usual, don't forget to check out the full documentation, interactive gallery, and tutorial at

    http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

    Jupyter Notebook Viewer

To install the latest release, if you are using Anaconda, you can install it with conda:

    conda install bokeh

Alternatively, you can install it with pip:

    pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

    http://cdn.pydata.org/bokeh-0.7.0.min.js
    http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

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4

Also thank you for the bug reports I have made issues here:

Thanks,

Bryan

···

On Dec 8, 2014, at 2:44 PM, Bryan Van de Ven <[email protected]> wrote:

Hi Gaëtan,

I agree this is a very desirable and prominent missing feature. Unfortunately there are some very substantial technical hurdles to getting the data saved programmatically if rendering happens on a browser (either a real browser, or PhantomJS), and right now there are no other backends to render to. Long term, there is hope that MEP 25 (Create new page · matplotlib/matplotlib Wiki · GitHub) will provide a mechanism to render Bokeh statically using Matplotlib as a backend, but I do not have any insight or estimate for when that work might be completed (or started) by the MPL project. It's possible that in the shorter term we will have to consider adding our own static rendering backend (most likely an ad-hoc plumbing to MPL or Chaco) but I don't savor the idea and it would probably not be 100% complete.

That said there is lots of interest in this, so we will definitely makes it a topic of discussion amongst the team to see what might be doable in the near future.

Bryan

On Dec 8, 2014, at 1:04 PM, Gaëtan de Menten <[email protected]> wrote:

This project seems to be progressing nicely but there is one fundamental thing which stops me from actually using it: how do you save images programatically (from python) for publications? It seems great for interactive exploration and fine-tuning your chart, but when you are happy with it and only want to update it from within the simulation script, what are you supposed to do? A quick search on the net seems to say "there is no solution yet". Is there a roadmap to add such a feature? Some kind of "server backend" maybe? On a related note, "Preview/Save->right-click-Save as" is really awkard (even for one-time charts) but I guess this is not fixable.

I also found two minor glitches in this release:
* the help text of the help button in the toolbar is outdated (not need for shift for wheel zoom)
* I cannot reach the "learn more" link in the help text (the tooltip disappears) (using FF/Windows)
* the last few charts in the gallery (at http://bokeh.pydata.org/docs/gallery.html\) are wrong/out of sync: the details when you click on them do not correspond to the thumbnail.

Thanks for any advice,
Gaëtan

On Mon, Dec 8, 2014 at 4:27 PM, Damian Avila <[email protected]> wrote:
On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.
Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

* IPython widgets and animations without a Bokeh server
* Touch UI working for tools on mobile devices
* Vastly improved linked data table
* More new (and improving) bokeh.charts (high level charting interface)
* Color mappers on the python side
* Improved toolbar
* Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh\. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

* Simplifying production and multi-user Bokeh server deployments
* Colorbar axis and axis location inspectors
* Better support for maps and projections

As usual, don't forget to check out the full documentation, interactive gallery, and tutorial at

   http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

   Jupyter Notebook Viewer

To install the latest release, if you are using Anaconda, you can install it with conda:

   conda install bokeh

Alternatively, you can install it with pip:

   pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

   http://cdn.pydata.org/bokeh-0.7.0.min.js
   http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

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For more options, visit https://groups.google.com/a/continuum.io/d/optout\.

5

Excellent work, Bokeh is looking better and better! I have one question: is there an example and/or documentation somewhere on how the linked data table works? Even just the one shown in the Vine screenshot in the 0.7 blog post. I’ve searched the docs but can’t find any mention of it. I’m a complete Bokeh newcomer though so maybe I just didn’t look in the right place!

Best,
Stefan

···

On Monday, December 8, 2014 3:27:36 PM UTC, Damian Avila wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.

Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

  • IPython widgets and animations without a Bokeh server
  • Touch UI working for tools on mobile devices
  • Vastly improved linked data table
  • More new (and improving) bokeh.charts (high level charting interface)
  • Color mappers on the python side
  • Improved toolbar
  • Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

  • Simplifying production and multi-user Bokeh server deployments
  • Colorbar axis and axis location inspectors
  • Better support for maps and projections

As usual, don’t forget to check out the full documentation, interactive gallery, and tutorial at

http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install it with conda:

conda install bokeh

Alternatively, you can install it with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

http://cdn.pydata.org/bokeh-0.7.0.min.js

http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

6

Hi Stefan, some examples living here:

https://github.com/bokeh/bokeh/blob/0.7.0/examples/plotting/file/data_tables.py

https://github.com/bokeh/bokeh/blob/0.7.0/examples/glyphs/data_tables.py

and the vine one (server-based):

https://github.com/bokeh/bokeh/blob/0.7.0/examples/glyphs/data_tables_server.py

Documentation coming soon :wink:

Damian

···

On Tue, Dec 9, 2014 at 11:20 AM, Stefan Pfenninger [email protected] wrote:

Excellent work, Bokeh is looking better and better! I have one question: is there an example and/or documentation somewhere on how the linked data table works? Even just the one shown in the Vine screenshot in the 0.7 blog post. I’ve searched the docs but can’t find any mention of it. I’m a complete Bokeh newcomer though so maybe I just didn’t look in the right place!

Best,
Stefan

On Monday, December 8, 2014 3:27:36 PM UTC, Damian Avila wrote:

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7!

Bokeh is a Python library for visualizing large and realtime datasets on the web.

Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.

This release includes many major new features:

  • IPython widgets and animations without a Bokeh server
  • Touch UI working for tools on mobile devices
  • Vastly improved linked data table
  • More new (and improving) bokeh.charts (high level charting interface)
  • Color mappers on the python side
  • Improved toolbar
  • Many new tools: lasso, poly, and point selection, crosshair inspector

Check our blog post: http://continuum.io/blog/bokeh-0.7, to watch some of these tools in action! And you can also see the CHANGELOG for full details.

We would like to mention that the Github Organization for Bokeh is growing! This organization was already home to bokeh-scala and bokeh.jl, and now the Bokeh project itself has a new home there as well, located at https://github.com/bokeh/bokeh. Anyone interested in developing new language bindings for Bokeh is encouraged to contact us about hosting your project under this organization.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

  • Simplifying production and multi-user Bokeh server deployments
  • Colorbar axis and axis location inspectors
  • Better support for maps and projections

As usual, don’t forget to check out the full documentation, interactive gallery, and tutorial at

http://bokeh.pydata.org

as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at:

http://nbviewer.ipython.org/github/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install it with conda:

conda install bokeh

Alternatively, you can install it with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

http://cdn.pydata.org/bokeh-0.7.0.min.js

http://cdn.pydata.org/bokeh-0.7.0.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián

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