May 102015

For a recent project, I had to create a game-style controller Android app that senses the phone’s tilt and uses it as an input. (image via SO)

To simplify the explanation, imagine a jet thruster game where the farther you tilt the phone back, the stronger the jet thrusters fire:

  • When the phone sits upright, the thrusters don’t fire at all
  • When the phone is tilted all the way back, the thrusters fire at full force

Going into it, I knew I would need to use Sensors, specifically something about the Accelerometer. Digging in a bit more, no amount of Googling/Stack searching provided me with exactly what I was looking for, so I figured I’d write it up here…

Initial search results:


My fully open-sourced solution lives here:

Specifically, check out

A few notes:

Jan 132015

So my wife and I were riding the train this morning, and having finished the crossword puzzle, we gave the KenKen a shot. It definitely provided some fun, but more so it got me thinking about combinations and permutations…

Eventually I started wondering how many total permutations there are of the 6 by 6 grid of numbers, and how many of those constitute valid KenKen boards (each row and each column must have all of the numbers between 1-6).

I ended up writing this script to brute force it:

But it seems like there are more combinations than I’d guessed! …

$ ./ 1
Number of unique combinations for cardinality 1: 1
Good: 1
Bad: 0

$ ./ 2
Number of unique combinations for cardinality 2: 6
Good: 2
Bad: 4
$ ./ 3
Number of unique combinations for cardinality 3: 1680
Good: 12
Bad: 1668

I’m running it with cardinality of 4 now (4×4 grid of numbers between 1-4) and it’s checking board 3 billion something…

Next steps would be trying to think of a clever way to find out the number of valid combinations for a 6×6 grid. Either we can continue with a programatic approach, either by using multiprocessing or just throwing random combinations at it, or both…

OR we could probably divert to math. A quick Google search gave me this link that seems to explain it pretty well:

I guess the answer is 6! * 5! * 4! * 3! * 2! * 1!, or 24883200 (right? someone wanna double check me here?).

Long story short, it looks like there’s no shortage of viable KenKen boards!

Thanks Cornell Department of Mathematics…

Jan 132015

Big fan of Retrofit, but have a staging server that has an invalid SSL cert?

Fear not! Just use the AllCertsValidClient!

Example usage:

Gson gson = new GsonBuilder()
AllCertsValidClient client = new AllCertsValidClient();
RestAdapter restAdapter = new RestAdapter.Builder()
    .setConverter(new GsonConverter(gson))
service = restAdapter.create(MyApiService.class);

Have fun, and be safe! (don’t use this thing in production environments!)

Dec 022014

This post serves as an update to my previous post Streaming Audio Wirelessly.

As suggested by my buddy Razr, I was indeed able to connect my Airport Express to my new apartment’s wireless network and enable AirPlay!


Thanks Razr!

Now playing (over the speakers via Airport Express + AirFoil, yay!), Sharon Van Etten – Spotify Sessions – perfect rainy Tuesday morning music

Nov 202014

So you use Spotify and want a list of all of your playlists so you can come up with your #AOTY (album of the year). I’m sure there are other ways of doing this, but my rudimentary google searches produced no results, so here goes…


  • Spotify account
  • python
  • internets

First, sign up as a Spotify developer and create an application, here:

Once you’ve created an application, make note of the Client ID and Client Secret, and add a new Redirect URIs to some working website.

Make sure to hit SAVE! If you don’t, this won’t work!!!

Now onto the code part…

First we need to put our Client ID Client Secret and Redirect URI into environment variables to be read later:

$ export SPOTIPY_CLIENT_ID='your-client-id-here'
$ export SPOTIPY_CLIENT_SECRET='your-client-secret-here'

Now we need an access token – here’s Spotify’s full formal auth guide:

but for this exercise we’ll be using Spotipy (docs)

Install spotipy:

$ pip install spotipy

Next we need to determine our scope. Here’s a list of the available scopes:

For this exercise, we’ll be using playlist-read-private.

Now let’s use this to get our scope. My username is loisaidasam, so that’s the username you’ll see me use throughout this post:

>>> import spotipy.util as util
>>> util.prompt_for_user_token('loisaidasam', scope='user-library-read')

This should open a browser window and trigger you to authenticate against your newly made application. When you say yes, it’ll redirect you to your redirect uri, which you copy and paste back here:

Enter the URL you were redirected to: 

The next string it gives you is your token. Copy and paste that into a variable called token

Now, for getting your playlists…

Here’s the Spotify documentation describing how to read a user’s playlists:

but for this exercise we’ll be using Spotipy’s user_playlists() method:

We have to use limit/offset to get all of the results, so try this code:

>>> import spotipy
>>> s = spotipy.Spotify(token)
>>> offset = 0
>>> while True:
>>>     playlists = s.user_playlists('loisaidasam', offset=offset, limit=50)
>>>     if not playlists['items']:
>>>         break
>>>     for item in playlists['items']:
>>>         print item['name']
>>>     offset += len(playlists['items'])

Running this should print out all of your playlist names one by one.


Nov 032014

Recently I bought a three-pack of these cool Nike Dri-FIT Crew Socks and I guess they mold them specifically for your left and right foot?

In getting ready for work this morning, I grabbed a pair of these bad boys, and I noticed that more often than not, the laundry place where I get my laundry done seems to pair them correctly (meaning that each “L” sock is paired with a corresponding “R” sock). I was wondering if they’re just hyper-considerate laundry folders over there, or if maybe it was just a coincidence.

Not recalling my statistics, I wrote a brute-force script to figure out what percentage of the time they would be bundled “correctly” when bundled at random:

And I found the answer to be an astonishing 40%!

$ python 3 -n 100000
3 pairs of socks
100000 iterations
{False: 60011, True: 39989}
Good 39.99% of the time 

I don’t know about you, but I find that percentage to be super high! We’re saying that when choosing socks in random order, that almost half of the time they’ll end up being bundled “correctly” with three bundles of properly matched “R” and “L” socks!

Update: I finished getting ready and hopped on my bike, and as I was commuting into work I started thinking about my results, specifically wondering if I could come up with a statistical explanation, and I think I figured it out.

Steps and corresponding probabilities:

  1. Choose one sock at random (cool 100% of the time, hard to mess this one up)
  2. Choose a sock that matches (cool 60% of the time – of the 5 remaining socks, 3 should be the correct match, and 2 the wrong one)
  3. Choose another remaining sock at random (cool 100% of the time)
  4. Choose a sock that matches this sock (cool 66.666…% of the time – of the remaining 3 socks, 2 are the correct match and 1 is wrong)
  5. The last two socks will always match each other (100%)

Now using statistics, you multiply the probabilities of these events happening (right?):

1.00 * 0.60 * 1.00 * 66.666 * 1.00

or in fractions

3/5 * 2/3





Yay, math!

Oct 292014

This morning I set out on a quest to grab ESPN’s latest headlines using pup.

Long story short: I couldn’t figure out how to do it using pup alone.

Luckily, Eric Chiang (pup’s creator) came to my rescue with a solution using pup and jq in combination, and I’ve since written a command-line script for grabbing ESPN’s latest headlines, as well as modified my motd to include them!

Happy ESPN’ing! :)

Oct 282014

So first off, let me start by saying that I noticed that my last post is entitled NYC Subway Data, Part 4 – moar data!. I later noticed that there is no “Part 3″ in this series. There never was. Too bad, it will remain a mystery.

Going forward, I’m happy to introduce MTA Fare Buster!!!

Based on Ben Wellington (iQuantNY)‘s post on the leftover money that you commonly end up with when buying a metrocard, I did some research and despite there being a few similar apps out there on the Android market, one was good but not updated with the latest bonus policy, one doesn’t give you options, and one wasn’t free, so I decided to write my own.

Full source code here:

Happy commuting!

Oct 202014

After spending some fun time with the subway schedule data the other night, I’m a bit more curious now about usage.

On the data list I found the following resources:

(could be helpful for finding fare type popularity? dunno how interesting that is…)

Location data, cool! Through the NYC open data site, I even found this cool little map showing the NYC subway entrances (I’m assuming based on this data):

THIS is interesting because hourly turnstile data COULD be helpful for trying to estimate station popularity at a given time of day/day of week/etc. The only problem is that the Remote Unit/Control Area/Station Name Key data doesn’t really matchup with the GTFS dataset Stops identifiers:

Remote Booth Station Data


GTFS Stops Data

Hopefully more soon…

Oct 142014

A follow up to my last post about trying to make something useful of the NYC subway data provided by the MTA…

I was googling around to try and see if I could find a list of stations per subway line, and didn’t really find any good answers.

I did find this post, essentially asking for the same thing I was looking for:

And decided that I’d lend a hand and help out, so I came up with this:

(feel free to upvote my answer)

Happy commuting y’all!