[Ieee_vis] TTK 0.9.9 is out!

[Julien Tierny]

Dear colleagues,

(sorry for cross-posting) 

after a year of intense development, we're excited to announce the release of the version 0.9.9 of the Topology ToolKit (TTK, https://topology-tool-kit.github.io/).

Although the version number says "0.9.9", this looks pretty much like a 2.0 release, with a major revamp of several aspects of the library, for improved performance, usability and extensibility.

Among the main new features, this new release includes:
- Major performance updates, with an observed speedup of up to an order of magnitude (on certain examples from the TTK website), wrt to the original 2017 release. These performance gains result from:
  * a revamped triangulation traversal API
  * a revamped (order-based) internal representation of scalar data
  * a new pre-conditioning feature for regular grids
  * various low-level updates in the support of Discrete Morse Theory (e.g. improved worst case run time with homotopy expansion, IEEE PAMI 2011)
  * new, parallel backend for topological simplification of scalar data (IEEE VIS 2020)
- New convenience features
  * Binary packages for Ubuntu, Windows and MacOS (experimental)
  * Migration to VTK9/ParaView5.8
  * New internal module API, which makes creating a new TTK module simpler, clearer and more convenient
  * New, absolutely necessary, awesome ASCII-art based logging backend
- Various new features
  * Fuzzy Contour Trees (EuroVis 2020)
  * Cinema Darkroom (IEEE LDAV 2020)
  
### About

TTK can handle scalar data defined either on regular grids or triangulations, in 2D, 3D, or more. It provides a substantial collection of generic, efficient and robust implementations of key algorithms in topological data analysis. It includes:
  - For scalar data: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Reeb graphs, Morse-Smale complexes, topological simplification, topology-aware compression, harmonic design;
  - For bivariate scalar data: fibers, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces;
  - For uncertain scalar data: mandatory critical points;
  - For ensemble scalar data: Bottleneck and Wasserstein distances between persistence diagrams (exact Munkres-based computation or fast Auction-based approximation), Wasserstein barycenters and clusters of persistence diagrams (fast progressive algorithms), distance matrices (Lp norm, Wasserstein distances), contour tree alignment;
  - For time-varying scalar data: critical point tracking, nested tracking graphs;
  - For high-dimensional / point cloud data: dimension reduction, persistence-based clustering;
  - and more!
If you need to robustly analyze your data, you may want to use TTK.
* Check out our gallery page to see visualizations we obtained with TTK:
https://topology-tool-kit.github.io/gallery.html

TTK makes topological data analysis accessible to end users thanks to easy-to-use plugins for the visualization front end ParaView. Thanks to ParaView, TTK supports a variety of input data formats.
* Check out our video tutorials to see TTK in action:
https://topology-tool-kit.github.io/tutorials.html

TTK is written in C++ but comes with a variety of bindings (VTK/C++, Python) and standalone command-line programs. It is modular and easy to extend. 
We have specifically developed it such that you can easily write your own data analysis tools as TTK modules.
* Check out our developer documentation:
https://topology-tool-kit.github.io/documentation.html

TTK is open-source (BSD license). You can use it at your convenience, for open-source or proprietary projects. You are also welcome to contribute.
* Check out our contribution page:
https://topology-tool-kit.github.io/contribute.html

* To try out TTK, checkout our installation instructions:
https://topology-tool-kit.github.io/installation.html

If you have questions, need support regarding the usage of TTK, or just want to provide feedback, thanks for sending us an email at topology.tool.kit at gmail.com

We hope you'll enjoy TTK!
-- 
Dr Julien Tierny
CNRS Researcher
Sorbonne Universite
http://lip6.fr/Julien.Tierny