gr.jl - plotting for julia based on gr

GR.jl – Plotting for Julia based on GR

June 24th – 28th, 2015

Massachusetts Institute of Technology, Cambridge, Massachusetts | JuliaCon 2015 | Josef Heinen | @josef_heinen

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June 24th – 28th, 2015 Josef Heinen, Forschungszentrum Jülich, Peter Grünberg Institute, Scientific IT Systems

The GR framework was originally developed for Python with the objective of providing easy-to-use methods for:

✓ displaying scattering spectra in real-time

✓ visualizing and analyzing two- and three-dimensional data sets

✓ creating publication-quality and web-ready graphics

✓ making glossy figures for scientific journals or press releases

2

Motivation


✓ speed up plotting times for real-time monitoring purposes

✓ visualize large data sets, probably with a dynamic component, preferably in real-time

✓ create animations or videos on the fly

3

… a bunch of (new) requirements


How can we fix them?

✓ Several Python modules can be compiled into native code, making them much faster (Cython)

✓ Compiling hotspots on the fly (Numba, PyPy) can significantly speed up numerical code segments

✓ Computations can be distributed, but …

… these approaches cannot easily be applied to visualization software!

➟ What about Julia?

4

… which lead to performance problems


Julia visualization solutions

Gadfly — most popular 2D plotting system for Julia

PyPlot — Python de-facto standard (“workhorse”)

Winston — MATLAB-like 2D plotting

Plotly — Julia wrapper for the plot.ly REST API

GLPlot — powerful 3D graphics(successor GLVisualize on the way)

OpenGL — fast, but low-level API

5

2D 3D

Gadfly

Bokeh

GLPlot

PyPlot

Plotly

Winston


separated 2D and (hardware accelerated) 3D world

some graphics backends "only" produce "figures"➟ no presentation of continuous data streams

poor performance on large data sets

Problems so far — Crux of the matter

6

Quality

Interop

Speed


Use GR to achieve more graphics performance

✓ procedural graphics backend (completely written in C) ➟ presentation of continuous data streams

✓ builtin support for 2D plotting and OpenGL (GR3) ➟ coexistent 2D and 3D world

✓ interoperability with GUI toolkits and Web frameworks➟ good user interaction

✓ … ready for Julia v0.4

7


Use GR to extend PyPlot’s capabilities

✓ combine the power of PyPlot and GR ➟ next Matplotlib release will allow selecting the backend by setting the environment variable MPLBACKEND

✓ produce video contents on the fly by adding a single line of code ➟ no need to import an animation module or write extra code

✓ create plots containing both 2D and 3D graphics elements

8


How it works: GR layer architecture

GKS logical device drivers

C / C++

GKS

GR

OpenGL (WGL / CGL / GLX)

POV-Ray generation

off-screen rendering

direct rendering

Browser

JavaScriptgeneration

WebGL

IPython

Win32X11

GKSTermgksqt LATEX

Qt Quartz PDF

C / ObjC

OpenGL ES

glgr / iGR Appsocket

communication

Qt / wx event loop

0MQ OpenGL

More logical device drivers / plugins:– CGM, GKSM, GIF, RF, UIL– WMF, Xfig– GS (BMP, JPEG, PNG, TIFF)

...

HTML5

wx

POV-Ray

GLUTGLFW

wxGLCanvasQGLWidget

...

SVGPSMOV

GR3

Highlights:– simultaneous output to multiple output devices– direct generation of MPEG4 image sequences– flicker-free display ("double buffering”)– IPython / Jupyter notebook integration

TikZ

SwiftPyPy

Generate ray-traced graphics scenes

Notebooks support for Python or Julia kernels

Use GR as a Matplotlib backend;

mix GR, MPL and GR3 code

Create HTML5 output

Qt console interaction

Create video animations on the fly


Matplotlib using the GR backend

10

Circle

Rectangle

Wedge

Polygon

Ellipse

Arrow

PathPatch

FancyBoxPatch

Line2D

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GR.jl in action …

11


GR + PyPlot interop

12

PyPlot

GR3

GR

Important: tells MPL backend not to update


… using GR in Juno

click images to view notebooks …

✓ simple IDE

✓ syntax highlighting

✓ command completion

✓ inline graphics


… or Jupyter

Performance (anim.jl)

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100

200

300

400

500

600

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MPL MPL+GR GR

fps

click images to view notebooks …

http://pgi-jcns.fz-juelich.de/pub/doc/JuliaCon_2015/700K_460.html














































http://pgi-jcns.fz-juelich.de/pub/doc/JuliaCon_2015/anim.html


















































What happens next?

✓ provide more convenience function

✓ migrate the GR3 library to modern OpenGL (using OpenGL shader language) ➟ visualize millions of vertices / faces

✓ simplify the installation

✓ migrate the molecule renderer (mogli) from Python to C

✓ create a JavaScript based GR backend using an LLVM-to-JS compiler infrastructure, e.g. emscripten(in a proof-of-concept we could show, that GR + GKS can be transpiled to JS without any modifications)

15


Resources

✓ Website: http://gr-framework.org

✓ GR package: https://github.com/jheinen/GR.jl

✓ Mogli package: https://pypi.python.org/pypi/mogli

✓ Talk material: GR.jl – Plotting for Julia based on GR

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http://gr-framework.org

https://github.com/jheinen/GR.jl

https://pypi.python.org/pypi/mogli

http://pgi-jcns.fz-juelich.de/pub/doc/JuliaCon_2015/


Thank you for your attention

Questions?

Contact: [email protected] @josef_heinen

Thanks to: Florian Rhiem, Ingo Heimbach, Christian Felder, David Knodt, Jörg Winkler, Fabian Beule, Philip Klinkhammer, Marvin Goblet, Daniel Kaiser, et al.

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mailto:[email protected]

gr.jl - plotting for julia based on gr

Software

josef heinen

peter grnberg institute

forschungszentrum jlich

d plotting system

d graphics elements

d world interoperability

scientic journals

graphics backends