See also diagrams.
Just imagine that we could have a language that combined high-performance numerical operations, interactive debugging, effortless integration of realtime and non-realtime interaction, easy parallelism, a large and active user community, ran on windows, linux, OS X and smartphones, had well-documented, performant graphics and smooth GUI integration.
Now, welcome to the real world, where every project is either
- a reasonably functional hodgepodge of the least-worst options that don’t suck in a particular domain, or
- grossly deficient in some way.
I’m going to try to get some data visualisation going in python via the hodgepodge method. I’d like it to be real-time and interactive or publication-quality, but I won’t be sad if I can’t achieve both simultaneously.
Visualisation is not especially a strong point of the python; the strong point is hodgepodge, decoupage, bricolage, and, uh, potpourri. I think we can cobble something together, or better, use someone else’s cobbling.
The classic option is matplotlib although it can’t yet do all your modern hipster graphs and is awful at animations and interactions, and it fugly per default. However, there are libraries which use matplotlib as a backend that are fare more beautiful:
Seaborn is another vaunted alternative option, which I would describe as an “Edward Tufte filter”
cute hack to justify matplotlib’s existence: xkcd graphs.
The mpld3 package is extremely easy to use: you can simply take any script generating a matplotlib plot, run it through one of mpld3’s convenience routines, and embed the result in a web page.
2d only, AFAICT.
GR is a universal framework for cross-platform visualization applications. It offers developers a compact, portable and consistent graphics library for their programs. Applications range from publication quality 2D graphs to the representation of complex 3D scenes.[…]
GR is essentially based on an implementation of a Graphical Kernel System (GKS) and OpenGL. […] GR is characterized by its high interoperability and can be used with modern web technologies and mobile devices. The GR framework is especially suitable for real-time environments.
It will also function as a matplotlib backend.
Holoviews has been crafted by some neurologists to serve science. Fresh, enthusiastic.
HoloViews focuses on bundling your data together with the appropriate metadata to support both analysis and visualization, making your raw data and its visualization equally accessible at all times. This process can be unfamiliar to those used to traditional data-processing and plotting tools, and this getting-started guide is meant to demonstrate how it all works at a high level. More detailed information about each topic is then provided in the User Guide .
With HoloViews, instead of building a plot using direct calls to a plotting library, you first describe your data with a small amount of crucial semantic information required to make it visualizable, then you specify additional metadata as needed to determine more detailed aspects of your visualization. This approach provides immediate, automatic visualization that can be effortlessly requested at any time as your data evolves, rendered automatically by one of the supported plotting libraries (such as Bokeh or Matplotlib).
VisPy is OpenGL-backed data visualisation, focussing on science (ooh!). It also offers a matplotlib compatibility layer. Here are some howtos:
Mayavi is an opinionated open-source commercially-backed interactive 3D visualiser. Its aesthetic I find grating. The source code repository is worryingly hard to find. For future reference, it’s here.
on a similar tip, although looking more basic and more bitrotten, is vtk - if I understand correctly, VTK is the engine used by Mayavi?
Disney (!) has a game library Panda3d, that seems to do all the fun things
even more bareback, more-or-less-directly calling into openGL, but seriously, I’m a statistician, not a coder. I could also hand-pulp hemp to make my own graph paper to draw my visualisations, drawn in home-made iron gall ink, but I would find it equally hard to argue that it was an efficient prioritisation.
I haven’t used PREdator (although I understand it’s been around longer than I. Heh.) Wiedemann, C., Bellstedt, P., & Görlach, M. (2014). PREdator: a python based GUI for data analysis, evaluation and fitting. Source Code for Biology and Medicine, 9(1), 21. DOI. Online.
jupyter notebooks have a rich enough API to integrate various more exotic graphics options; In fact, since you are now using the web browser, you can go straight into browser datavis, which is powerful and super hip.
Here are some promising hacks
- superset is Airbnb’s python+browser interactive data exploration tool.
- There is a full(ish) d3.js backend for matplotlib called mpld3
- same tool (web browser), different approach: bokeh does “big-data” and streaming-based browser graphing for python. And its website probably looks the nicest out of everything I’ve mentioned, which counts for a lot. However, its print-output is bad; this is a web-oriented tool
- INRIA’s Tulip has fans
- Visualizing a NetworkX graph in the IPython notebook with d3.js