The Living Thing / Notebooks :

Plotting in python

Colourizing data for science

Usefulness: 🔧
Novelty: 💡
Uncertainty: 🤪
Incompleteness: 🚧 🚧 🚧

See also diagrams.

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 inconsolable if I cannot achieve both simultaneously.

Visualisation is not an especially strong suit of python; the strong suit is hodgepodge, decoupage, bricolage, and, uh, potpourri. Therefore our solution will be to cobble something together, or better, to use someone else’s cobbling.

Matplotlib

.

The classic option is matplotlib. It can’t do all those modern hipster graphs and is awful at animations and interactions, and it fugly per default, it works OK out of the box. There are libraries which use matplotlib as a backend and aim for something smoother. Note some confusing terminology; An Axes object, which is constructed by an add_subplot command, contains two Axis objects, but is much more than a list of such objects, being the basic“graph” object. Read Jakevdp’s manual

Matplotlib styling

The default matplotlib stylesheet aspires to look like 80s spreadsheet defaults, but if you are not a retrofuturist, you want to change the stylesheet. Some of the built-in stylesheets are OK.

Here is an ugly gallery of sometimes-beautiful graph styles. Here is an ugly gallery of sometimes-beautiful colour maps.

Seaborn is another vaunted extension, which I would describe as an “Edward Tufterizer”. Extends matplotlib with modern apperance and some missing plot types.

Fonts

Someone made the idiosyncratic choice that default font is sans serif, even for mathematical text. You can change this by setting serif fonts also for mathtext.

from matplotlib import rc
rc(
  'font',
  family='serif',
  serif=['Palatino']
)
rc(
  'mathtext',
  fontset='cm'
)

Supported math fonts are reputedly

Alternatively you can render your graph labels with TeX which leads to some weird spacing but allows you to match fonts better.

Yellowbrick

Yellowbrick is a matplotlib specialisation for hyperparameter optimisation.

Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. Under the hood, it’s using Matplotlib.

GR

GR.py wraps GR, a cross-platform visualisation framework:

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.

Visdom

Visdom pumps graphs to a visualisation server.

Holoviews

Holoviews has been crafted by some neurologists to serve science. Fresh, enthusiastic. Is it good?

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).

Part of a suite of visualisations tools and guides called Pyviz.

Python+browser

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 hip.

Here are some promising hacks

Related: create your geographic visualisations using python + leaflet.js javascript maps using folium.

network-specific stuff

In browser datavis I found Sigma.js; there are surely more JS graph visualisations.

VisPy

VisPy is OpenGL-backed data visualisation, focussing on science (ooh!). It also offers a matplotlib compatibility layer. Here are some howtos:

There seems to be a lot more writing of OpenGL shaders than one would like to draw a line graph.

Mayavi

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? Better maintained and possibly still vtk-based is Paraview, which supports pluggable backends.

Not exactly graphing libraries