Category Archives: Electronics

What I am up to?

This is how my 2014 passed, or what I am up to:

  • riding my motorcycle a lot
  • hacking electronics and IoT using OSHW
  • dockerising things (apps, services)
  • playing with Apache Mesos, and ecosystem around it
  • hacking projects using Javascript (framework, toolchain, node.js, ES6 way of doing things) and web-standards (HTML5, CSS3, components, etc)
  • got Google’s Widevine certification (as required in one of projects). Personally, I like non DRM’d content :-)
  • built a Chromecast app using dash.js, modular Widevine DRM with node.js based proxy service for licensing, and Java for content encryption and packaging
  • been reading a lot of stuff through hackernews
  • helped and built stuff: QRizq, Diziana, 99doodles, IndieReign and other projects
  • not having meetings (waste of time)
  • not interviewing candidates who are not worth anyone’s time. [should do another post: sad and bad state of technical education in India]
  • bunch of other things I can’t recall

Did you notice that I have been mostly playing (or having fun)? On other side of it, I have also been thinking what’s nex; I think, I have figured that out.

At present, I am still involved with couple of projects mentioned above. I have also taken up a couple of interesting consulting assignments around technologies mentioned above along-with my old love (flash/actionscript).

Installing NumPy and SciPy on Mac OSX 10.8 (Mountain Lion)

I am playing with ExpEyes, which is awesome tool for anyone who wants to learn electronics (and physics).

ExpEyes comes with software (written in python) for GNU/Linux and Windows. I intend to run it on Mac OSX, because it can (provided all dependencies are met).

It is generally easy to install python packages on OSX using easy_install or pip, however, installing NumPy and SciPy turned out to be pain on OSX (10.8.2, latest version the time of posting).

In my case, I am using python binary installed via homebrew, so I can’t take advantage of numpy (which comes installed with mountain-lion).

I have XCode 6 installed, which comes with gcc-4.2.1 (llvm build). I figured out, I can build numpy and scipy from source.

Following is what I did to build numpy and scipy. SciPy requires gfortran, so that has to be installed (I used homebrew to do that):

$ git clone
$ git clone
$ cd numpy
$ python build && python install
$ brew install gfortran
$ cd ../scipy
$ python build && python install

BTW! In case, you are wondering, why didn’t I use Scipy Superpack script, which indeed makes job easier?

I didn’t, because I want to install gnu/unix packages using homebrew, and I already have git and other things. Super Spicypack downloads (gfortran, etc.), builds and installs packages directly to system, making it harder to remove those later? Homebrew makes job easier.