One of the big challenges that matplotlib faces as it enters its second decade is moving from a desktop app to the web browser client/server paradigm. This need has been known for a few years at least: SAGE and the IPython notebook are rich web clients and powerful ways to interact with Python, however, their plotting is still necessarily limited by matplotlib’s design to rendering a static image. John Hunter concluded his keynote at SciPy 2012 arguing that this was the single biggest challenge to matplotlib’s relevance today.
When trying to determine how to pull matplotlib kicking and screaming into this Brave New World, let’s assume that the network bottleneck between the server (e.g. an IPython kernel) and the client (i.e. the web browser) is too high to simply send images over repeatedly. It would be awfully nice, if we’re going to do all this work anyway, to allow for interacting with a server that may be over a slow and high-latency internet connection on the other side of the globe. The only way to make interactivity bearable in that scenario is to put some actual plotting smarts into the client.
For the purposes of this discussion, we should define what interactivity means. I think it basically amounts to:
- data cursor (i.e. getting the current position of the mouse in data coordinates)
- panning and zooming
- adjusting the edges of the axes within the figure
Other interactive features, such as a "back" button or "apply tight layout" button have an "activate and return" interaction, rather than real time interaction, so can probably be handled with a round-trip to the server and thus aren’t really considered here.
It’s well known that matplotlib has a number of backends that handle drawing to specific GUI frameworks or file formats. The matplotlib "core" understands how to build and generate plots, and then sends low-level drawing commands to the currently selected backend. In order to reduce code duplication, there is a solid wall between the core and the backends, and we’re constantly trying to minimize the amount of code required to write a backend. The advantage of this is not just to reduce the number of lines of code, but to ensure consistency between the backends, so that when you render a streamplot with hatching and custom markers to a PDF file, it looks the same as when you render it to an SVG.
So why can’t we just add a new "webbrowser" backend? The problem is that the backends are too low-level. They know where the shapes and the text are, but they know nothing how they relate to one another, how the data scales from its native data coordinates to the coordinates of the screen, and how to best add ticks and other annotations to the graph. All of that information would be required for any sort of interactivity.
To even begin to tackle this, we need to move from the current two-way split of the plotting core and rendering backends to a three-way split into the phases of outputting a plot:
- Build: This phase is where the various Artist objects that make up the plot are created and related to one another. This is where most of the domain-specific code about particular types of plots lives.
- Drawing: Given those Artists and view limits for the axes, figures out how to scale them, and where to place the ticks, labels and other pieces of text. This phase also includes decimating or downscaling the data for display, since how to do so is dependent on the limits. Newer features such as "tight limits" also need to happen during this phase.
- Rendering: Converts a series of simple commands from the drawing phase into the native commands understood by a particular GUI framework or file format.
In normal interactive use, the Build phase happens once, but the Drawing and Rendering phases happen in a continuous loop as the figure is panned, zoomed and resized.
The Drawing phase comprises a great deal of Python and C++ code , much of it at the heart of what matplotlib is. The big pieces are:
- Ticking (i.e. deciding where the numeric values and gridlines should go) is a surprisingly involved task, and matplotlib’s ticking is very flexible, supporting many different scales (such as log scale) and formats (controlling the precision of the values, for example). Because of this, the Drawing phase is dependent on matplotlib’s transformation infrastructure.
- Simplification and downsampling is performed on-the-fly as the data is zoomed to reduce unnecessary drawing and make the interactivity much snappier. Of course, when it comes to large data there are other issues about the network bandwidth and the memory efficiency of the data representation within the browser that may be limiting relative to what matplotlib can do now.
- Text layout, including math text layout, is done at this stage, because the size of the text relative to other items can not be known until draw time.
I hope to follow this blog post up with some experiments into various possible solutions over the upcoming days and weeks. In the meantime, I encourage all the comments and help on this I can get.
|||It’s easy enough to see what code is required for the drawing phase by using coverage.py and turning it on at the start of Figure.draw and turning it off again at the end.|