Skip to main content

Asynchronous client-server library for simple plotting.

Project description

Async Plotter

Simple two-process client/server plotting with the following features:

  • Plotting does not slow down calculations.
  • User maintains control of the calculation (i.e. KeyboardInterrupts work).
  • Auto-launch client from the server.
    • The user defines the plotting code in a (picklable) class, and provides the class to the Server() constructor. The Server passes the pickled Plotter to the client when the client process is launched in a new python interpreter. The python interpreter command line options for the client are passed to the Server constructor at the time of instantiation. Default values are: (ex. [“ipython”, “–pylab=osx”, “-c”])

To Do

Prototype for asynchronous plotting with separate processes using sockets

Still Needs:

  • Comprehensive testing
  • Logging (print debug messages in debug mode for checking network problems)
  • Profile performance.
  • Configuration (hostname, port specification etc.)

I think that these have been dealt with, but they need testing:

  • socket buffer overflow (recv)
  • cleanup thread and socket command line termination
  • error handling. ex. when a client disconnects then server listen continues, network errors
  • multiple clients (plotting)

Other Design Approaches

Threading Solution

The simplest approach is a multi-thread approach. In principle, one can run the computations in the main thread and plotting in a separate thread. This solution is sketched in but fails with most matplotlib backends due to their requirement of running in the main thread. A quick work-around is to run the computation in a secondary thread, but this precludes the user being able to interrupt the computation.

A nice feature of the python GIL is that one can be fairly confident about sharing data (a careful solution would require locks etc.)

Multiprocessing Solution

This same solution should work with multiprocessing, but this fails on my development platform (Mac OS X 10.5) with the following error:

The process has forked and you cannot use this CoreFoundation
functionality safely. You MUST exec().
Break on

Separate Processes

It seems that the most robust solution is to have the calculation and plotters run in completely separate processes. This has an added benefit:

  • User can plot remotely.

One issue that needs to be addressed here (and in the multiprocessing solution) is the copying of data. One common use-case is that the plotter may be slower than the computation. Thus, intermediate data may be discarded and should not be sent across the network.

Additional Approaches

It seems that one should be able to use IPython to do this, but I have not found a simple way to do this yet.

Project details

Release history Release notifications | RSS feed

This version


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for asyncplot, version 0.1
Filename, size File type Python version Upload date Hashes
Filename, size asyncplot-0.1.tar.gz (8.7 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page