ANN: Bokeh 1.2 Released
On behalf of the Bokeh team I am pleased to announce the release of version 1.2 of Bokeh! Please read all about it in the announcement post at: https://blog.bokeh.org/posts/release-1-2-0 If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/latest/docs/installation.html Full documentation is available at: https://bokeh.pydata.org Community support is available at: https://discourse.bokeh.org/ Contributions can be made on Github: https://github.com/bokeh/bokeh There are now 360 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. If you are interested in contributing, please come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 1.0 Released
On behalf of the Bokeh team I am pleased to announce the release of version 1.0 of Bokeh! Please read all about it in the announcement post at: https://bokeh.github.io/blog/2018/10/24/release-1-0-0/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/latest/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org There are over 321 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. If you are interested in contributing, please come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.13.0 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.13.0 of Bokeh! For more information and details, please see the announcement post at: https://bokeh.github.io/blog/2018/6/13/release-0-13-0/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/0.13.0/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org/en/0.13.0 There are over 301 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. Or if you are interested in contributing, come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.11 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.15 of Bokeh! For more information and details, please see the announcement post at: https://bokeh.github.io/blog/2018/3/29/release-0-12-15/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/0.12.15/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org/en/0.12.15 There are over 287 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. Or if you are interested in contributing, come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.11 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.11 of Bokeh! For more information and details, please see the announcement post at: https://bokeh.github.io/blog/2017/11/28/release-0-12-11/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/0.12.11/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org/en/0.12.11 There are over 268 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. Or if you are interested in contributing, come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.10 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.10 of Bokeh! For more information and details, please see the announcement post at: https://bokeh.github.io/blog/2017/10/17/release-0-12-10/ If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: https://bokeh.pydata.org/en/0.12.10/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at: https://bokeh.pydata.org/en/0.12.10 There are over 260 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally, for questions or technical assistance we recommend starting with detailed posts on Stack Overflow. Or if you are interested in contributing, come by the Bokeh dev chat room: https://gitter.im/bokeh/bokeh-dev Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.9 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.9 of Bokeh! OF SPECIAL NOTE: *** JupyterLab and Fast Array Transport now supported *** Please see the announcement post at: https://bokeh.github.io/blog/2017/9/12/release-0-12-9/ which has more information and demonstrations. If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: http://bokeh.pydata.org/en/0.12.9/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at http://bokeh.pydata.org/en/0.12.9 There are over 250 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally (as always), for questions, technical assistance or if you're interested in contributing, questions can be directed to the Bokeh mailing list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.6 Released
On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.6 of Bokeh! *** OF SPECIAL NOTE *** PNG and SVG export directly from python is now supported! Please see the announcement post at: https://bokeh.github.io/blog/2017/6/13/release-0-12-6/ which has more information as well as live demonstrations. If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: http://bokeh.pydata.org/en/0.12.6/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at http://bokeh.pydata.org/en/0.12.6 There are over 231 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally (as always), for questions, technical assistance or if you're interested in contributing, questions can be directed to the Bokeh mailing list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.4 Released
Hi all, On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.4 of Bokeh! Please see the announcement post at: https://bokeh.github.io/blog/2017/1/6/release-0-12-4/ which has more information as well as live demonstrations. If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: http://bokeh.pydata.org/en/0.12.4/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at http://bokeh.pydata.org/en/0.12.4 There are over 200 total contributors to Bokeh and their time and effort help make Bokeh such an amazing project and community. Thank you again for your contributions. Finally (as always), for questions, technical assistance or if you're interested in contributing, questions can be directed to the Bokeh mailing list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh Thanks, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12.1 released
Hi all, On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.1 of Bokeh! This is a minor, incremental update that adds a few new small features and fixes several bugs. Please see the announcement post at: https://bokeh.github.io/blog/2016/6/28/release-0-12-1/ which has much more information as well as live demonstrations. And as always, see the CHANGELOG and Release Notes for full details. If you are using Anaconda/miniconda, you can install it with conda: conda install -c bokeh bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: http://bokeh.pydata.org/en/0.12.1/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at http://bokeh.pydata.org/en/0.12.1 Questions can be directed to the Bokeh mailing list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh Thanks, Bryan Van de Ven Continuum Analytics - Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
Re: ANN: Bokeh 0.12 Released
Apologies, I neglected to include a project description: """ Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. """ > On Jun 28, 2016, at 1:32 PM, Bryan Van de Ven <bry...@continuum.io> wrote: > > > Hi all, > > On behalf of the Bokeh team, I am pleased to announce the release of version > 0.12.0 of Bokeh! > > This release was a major update, and was focused on areas of layout and > styling, new JavaScript APIs for BokehJS, and improvements to the Bokeh > Server. But there were many additional improvements in other areas as well. > Rather than try to describe all the changes here, I encourage every one to > check out the new project blog: > > https://bokeh.github.io/blog/2016/6/28/release-0-12/ > > which has details as well as live demonstrations. And as always, see the > CHANGELOG and Release Notes for full details. > > If you are using Anaconda/miniconda, you can install it with conda: > > conda install bokeh > > Alternatively, you can also install it with pip: > > pip install bokeh > > Full information including details about how to use and obtain BokehJS are at: > > http://bokeh.pydata.org/en/0.12.0/docs/installation.html > > Issues, enhancement requests, and pull requests can be made on the Bokeh > Github page: https://github.com/bokeh/bokeh > > Documentation is available at http://bokeh.pydata.org/en/0.12.0 > > Questions can be directed to the Bokeh mailing list: bo...@continuum.io or > the Gitter Chat room: https://gitter.im/bokeh/bokeh > > Thanks, > > Bryan Van de Ven > Continuum Analytics -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.12 Released
Hi all, On behalf of the Bokeh team, I am pleased to announce the release of version 0.12.0 of Bokeh! This release was a major update, and was focused on areas of layout and styling, new JavaScript APIs for BokehJS, and improvements to the Bokeh Server. But there were many additional improvements in other areas as well. Rather than try to describe all the changes here, I encourage every one to check out the new project blog: https://bokeh.github.io/blog/2016/6/28/release-0-12/ which has details as well as live demonstrations. And as always, see the CHANGELOG and Release Notes for full details. If you are using Anaconda/miniconda, you can install it with conda: conda install bokeh Alternatively, you can also install it with pip: pip install bokeh Full information including details about how to use and obtain BokehJS are at: http://bokeh.pydata.org/en/0.12.0/docs/installation.html Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Documentation is available at http://bokeh.pydata.org/en/0.12.0 Questions can be directed to the Bokeh mailing list: bo...@continuum.io or the Gitter Chat room: https://gitter.im/bokeh/bokeh Thanks, Bryan Van de Ven Continuum Analytics -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.11.1 released
Hi all, I am please to announce a new point release of Bokeh, version 0.11.1, is now available. Installation instructions can be found in the usual location: http://bokeh.pydata.org/en/latest/docs/installation.html This release focused on providing bug fixes, small features, and documentation improvements. Highlights include: * documentation: - instructions for running Bokeh server behind an SSL terminated proxy - Quickstart update and cleanup * bugfixes: - notebook comms handles work properly - MultiSelect works - Oval legend renders correctly - Plot title orientation setting works - Annulus glyph works on IE/Edge * features: - preview of new streaming API in OHLC demo - undo/redo tool add, reset tool now resets plot size - "bokeh static" and "bokeh sampledata" commands - can now create Bokeh apps directly from Jupyter Notebooks - headers and content type now configurable on AjaxDataSource - new network config options for "bokeh serve" For full details, refer to the CHANGELOG in the GitHub repository, and the full release notes (http://bokeh.pydata.org/en/latest/docs/releases/0.11.1.html) Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Full documentation is available at http://bokeh.pydata.org/en/0.11.1 Questions can be directed to the Bokeh mailing list: bo...@continuum.io Thanks, Bryan -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.11 released
Hi all, On behalf of the Bokeh team, I am excited to announce the release of version 0.11 of Bokeh. Bokeh Version 0.11 is a large release with *many* new improvements. The major focus of this release was to introduce a new Bokeh server, based on Tornado and websockets that is more stable, has higher performance, and simpler to use and deploy. You can already see a small sample of hosted Bokeh application examples at a new site located here: http://demo.bokehplots.com We will be adding many new examples here over the coming weeks. There is so much exciting in this release, that we will need a few blog posts to talk about everything. Keep an eye on our Twitter @BokehPlots or the mailing list for upcoming blog announcements. Some of the highlights from this release are: * New Bokeh Server based on Tornado and websockets - highly expanded documentation, with examples and guidance for usage * User-Defined Models allowing anyone to extend Bokeh * Significant GIS features and improvements - Support for Stamen, OpenStreetMap, and other tile sources - GeoJSON data source - Patches with holes * WebGL support for rendering lines * Python -> JS compilation for CustomJS callbacks (Py3 only for now) * New general push_notebook() based on Jupyter comms * Updates to charts, charts examples, and charts docs * UX improvements - configurable and "auto" range bounds - wheel zoom scroll capture turned off by default - easily set visual styling for highlighting hovered points - responsive improvements, maintain plot aspect and auto-resize Nearly 400 issues and PRs were closed for the release! See the CHANGELOG for full details, and the release notes for known issues and any migration notes. The next big feature we plan is to dramatically improve our layout options using PhosporJS, which is also the foundation of the new Jupyter Workbench project. We also plan to have several, more frequent point releases with smaller incremental improvements and fixes. I'd also like to take the opportunity to thank all the Bokeh contributors, and especially new people who have helped greatly with this release: Havoc Pennington, Greg Nordin, and Christian Tremblay. If you are using Anaconda/miniconda, you can install it with conda: conda install bokeh Alternatively, you can also install it with pip: pip install bokeh Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh Full documentation is available at http://bokeh.pydata.org/en/0.11.0 Questions can be directed to the Bokeh mailing list: bo...@continuum.io Thanks, The Bokeh Team -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.8 released
Hi all, We are excited to announce the release of version 0.8 of Bokeh, an interactive web plotting library for Python... and other languages! This release includes many major new features: * New and updated language bindings: R, JavaScript, Julia, Scala, and Lua now available * More bokeh-server features geared towards production deployments * Live gallery of server examples and apps! * Simpler, more easily extensible design for charts API, plus new Horizon chart * New build automation and substantial documentation improvements * Shaded grid bands, configurable hover tool, and pan/zoom for categorical plots * Improved and more robust crossfilter application * AjaxDataSource for clients to stream data without a Bokeh server In addition, many smaller bugfixes and features, both old and new---over 100 issues---were closed for this release! See the CHANGELOG for full details. Perhaps the biggest news of this release is the long awaited arrival of rbokeh (https://github.com/bokeh/rbokeh), which brings native bindings for Bokeh to the R language. You see more details and examples about the release at: http://continuum.io/blog/bokeh-0.8 If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh Developer builds are also now made available to get features in the hands of interested users more quickly. See the Developer Builds section in the documentation for more details. BokehJS is also available by CDN for use in standalone Javascript applications: * http://cdn.pydata.org/bokeh/release/bokeh-0.8.0.min.js * http://cdn.pydata.org/bokeh/release/bokeh-0.8.0.min.css Please note that the file layout on CDN has changed slightly (however all older releases will always be available at their original locations). 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: bo...@continuum.io Thank you for your attention! Bryan Van de Ven Continuum Analytics bry...@continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.6 release
On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6! 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 bug fixes and improvements over our most recent 0.5.2 release: * Abstract Rendering recipes for large data sets: isocontour, heatmap * New charts in bokeh.charts: Time Series and Categorical Heatmap * Full Python 3 support for bokeh-server * Much expanded User and Dev Guides * Multiple axes and ranges capability * Plot object graph query interface * Hit-testing (hover tool support) for patch glyphs See the CHANGELOG for full details. I'd also like to announce a new Github Organization for Bokeh: https://github.com/bokeh. Currently it is home to Scala and and Julia language bindings for Bokeh, but the Bokeh project itself will be moved there before the next 0.7 release. Any implementors of new language bindings who are interested in hosting your project under this organization are encouraged to contact us. In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, R language bindings, Blaze integration and cloud hosting for Bokeh apps. 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/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.6.min.js http://cdn.pydata.org/bokeh-0.6.min.css Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io If you have interest in helping to develop Bokeh, please get involved! Thanks, Bryan Van de Ven Continuum Analytics http://continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.5 released
I am very happy to announce the release of Bokeh version 0.5! (http://continuum.io/blog/bokeh-0.5) Bokeh is a Python library for visualizing large and realtime datasets on the web. This release includes many new features: weekly dev releases, a new plot frame, a click tool, always on hover tool, multiple axes, log axes, minor ticks, gears and gauges glyphs, and an NPM BokehJS package. Several usability enhancements have been made to the plotting.py interface to make it even easier to use. The Bokeh tutorial also now includes exercises in IPython notebook form. Of course, we've made many little bug fixes - see the CHANGELOG for full details. The biggest news is all the long-term and architectural goals landing in Bokeh 0.5: * Widgets! Build apps and dashboards with Bokeh * Very high level bokeh.charts interface * Initial Abstract Rendering support for big data visualizations * Tighter Pandas integration * Simpler, easier plot embedding options Expect dynamic, data-driven layouts, including ggplot style auto-faceting in upcoming releases, as well as R language bindings, more statistical plot types in bokeh.charts, and cloud hosting for Bokeh apps. Check out the full documentation, interactive gallery, and tutorial at http://bokeh.pydata.org as well as the new Bokeh IPython notebook nbviewer index (including all the tutorials) at: http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/index.ipynb If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.5.min.js http://cdn.pydata.org/bokeh-0.5.min.css Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io If you have interest in helping to develop Bokeh, please get involved! Special thanks to recent contributors: Tabish Chasmawala, Samuel Colvin, Christina Doig, Tarun Gaba, Maggie Mari, Amy Troschinetz, Ben Zaitlen. Bryan Van de Ven Continuum Analytics http://continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.4.4 released
I am happy to announce the release of Bokeh version 0.4.4! Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients. Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. If you are a Matplotlib user, you can just use %bokeh magic to start interacting with your plots in the notebook immediately! Check out the full documentation, interactive gallery, and tutorial at http://bokeh.pydata.org If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh We are still working on some bigger features but want to get new fixes and functionality out to users as soon as we can. Some notable features of this release are: * Additional Matplotlib, ggplot, and Seaborn compatibility (styling, more examples) * TravisCI testing integration at https://travis-ci.org/ContinuumIO/bokeh * Tool enhancements, constrained pan/zoom, more hover glyphs * Server remote data and downsampling examples * Initial work for Bokeh app concept Also, we've also made lots of little bug fixes and enhancements - see the CHANGELOG for full details. BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.4.4.js http://cdn.pydata.org/bokeh-0.4.4.css http://cdn.pydata.org/bokeh-0.4.4.min.js http://cdn.pydata.org/bokeh-0.4.4.min.css Some examples of BokehJS use can be found on the Bokeh JSFiddle page: http://jsfiddle.net/user/bokeh/fiddles/ The release of Bokeh 0.5 is planned for early May. Some notable features we plan to include are: * Abstract Rendering for semantically meaningful downsampling of large datasets * Better grid-based layout system, using Cassowary.js * More MPL/Seaborn/ggplot.py compatibility and examples, using MPLExporter * Additional tools, improved interactions, and better plot frame * Touch support Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io If you have interest in helping to develop Bokeh, please get involved! Special thanks to recent contributors: Amy Troschinetz and Gerald Dalley Bryan Van de Ven Continuum Analytics http://continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.4.2
I am happy to announce the release of Bokeh version 0.4.2! Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients. Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. Check out the full documentation, interactive gallery, and tutorial at http://bokeh.pydata.org If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh Some of the new features in this release include: * Additional Matplotlib and Seaborn compatibility (PolyCollection) * Extensive tutorial with exercises and solutions added to docs * new %bokeh magic for improved IPython notebook integration * Windows support for bokeh-server with two new storage backends (in-memory and shelve) Also, we've fixed lots of little bugs - see the CHANGELOG for full details. BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.4.2.js http://cdn.pydata.org/bokeh-0.4.2.css http://cdn.pydata.org/bokeh-0.4.2.min.js http://cdn.pydata.org/bokeh-0.4.2.min.css Some examples of BokehJS use can be found on the Bokeh JSFiddle page: http://jsfiddle.net/user/bokeh/fiddles/ The release of Bokeh 0.5 is planned for late March. Some notable features we plan to include are: * Abstract Rendering for semantically meaningful downsampling of large datasets * Better grid-based layout system, using Cassowary.js * More MPL/Seaborn/ggplot.py compatibility and examples * Additional tools, improved interactions, and better plot frame * Touch support Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io Special thanks to recent contributors: Melissa Gymrek, Amy Troschinetz, Ben Zaitlen, Damian Avila, and Terry Jones Regards, Bryan Van de Ven Continuum Analytics http://continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.4 Release
I am pleased to announce the release of Bokeh version 0.4! Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients. Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. Check out the full documentation and interactive gallery at http://bokeh.pydata.org If you are using Anaconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh Some of the new features in this release include: * Preliminary work on Matplotlib support: convert MPL figures to Bokeh plots * Free public beta of Bokeh plot hosting at http://bokehplots.com * Tool improvements: - always on pan tool and wheel zoom tool (with shift key) - box zoom tool - viewport reset tool * Enhanced datetime axis, with better performance and nicer ticking * Expanded testing, including TravisCI integrations and static image output using PhantomJS * RGBA and color mapped image plots now available from Python * Python 3 supported * Vastly improved documentation for glyphs, with inline examples and JSFiddle integration Also, we've fixed lots of little bugs - see the CHANGELOG for full details. Bokeh will be having a free Office Hours later this week! Join us this Thursday at 2pm CST on EngineHere athttps://www.enginehere.com/stream/437/bokeh-04-release/ for a live informational session about the latest release. We'll be covering all the newest features and updates through a combination of live lecture, QA, and pair programming. It's all free, just sign up to the EngineHere learning platform. BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.4.js http://cdn.pydata.org/bokeh-0.4.css http://cdn.pydata.org/bokeh-0.4.min.js http://cdn.pydata.org/bokeh-0.4.min.css Some examples of BokehJS use can be found on the Bokeh JSFiddle page: http://jsfiddle.net/user/bokeh/fiddles/ The release of Bokeh 0.5 is planned for late March. Some notable features we plan to include are: * Abstract Rendering for semantically meaningful downsampling of large datasets * Better grid-based layout system, using Cassowary.js * Selection tools, tooltips, etc. Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io Special thanks to recent contributors: Janek Klawe, Samantha Hughes, Rebecca Paz, and Benedikt Sauer. Regards, Bryan Van de Ven Continuum Analytics http://continuum.io -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
ANN: Bokeh 0.3 released
All, I am pleased to announce the release of Bokeh 0.3! Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. If you are using Anaconda, you can install through conda: conda install bokeh Alternatively you can install from PyPI using pip: pip install bokeh This release was largely an internal refactor to merge the BokehJS and Bokeh projects into one repository, and to greatly improve and simplify the BokehJS coffee script build process. Additionally, this release also includes a number of bug and stability fixes, and some enhancements, including: * bugfixes: - #155 ColumnDataSource does not update column_names - #150 If you plot circles that all have a position (0,0), there is a crash - #117 axis_line_color=None does not work * enhancements: - #157 xaxis, etc should return collection object - #129 The sampledata download is confusing - #82 Improve hold() functionality in notebook See the CHANGELOG for full details. Several new examples were added including a reproduction of Burtin's Antibiotics, and examples of animation using the Bokeh plot server. ColorBrewer palettes were also added on the python side. Finally, the user guide has been flushed out and will continually be updated as features and API changes are made. Check out the full documentation and interactive gallery at http://bokeh.pydata.org The release of Bokeh 0.4 is planned for early January. Some notable features to be included are: * Integrate Abstract Rendering into bokeh server * Better grid-based layout system; use Cassowary.js for layout solver * Tool Improvements (pan always on, box zoom always on, passive resize with hot corners) * Basic MPL compatibility interface (enough to make ggplot.py work) * Expose image plot in Python interface: Add BSON for sending large data Issues or enhancement requests can be logged on the Bokeh Github page: https://github.com/continuumio/bokeh Questions can be directed to the Bokeh mailing list: bo...@continuum.io Regards, Bryan Van de Ven -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
