Here's a (working) OpenAPI schema definition (openapi.json) that I put together as part of my PFun Digital Health AI product pipeline.
The linked openapi.json file can be used to easily generate a client or proxy server to the Dexcom Developer API.
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| AI-generated image (DALL-E) |
I've been messing around with the read/write methods in scipy.io.wavfile to make some "music" using the output of some of my biophysical models.
So for example, here's some simulation output with an audio track (caution, may be loud):
So another interesting point... these sounds can be parametrized...
Here's another example with different parameters (again, caution of loudness):
I'm thinking I'll make a public API at some point in the near future so you can make your own physiological music... 😉
Over summer 2016, I decided to make a Twitter bot just for fun. Here, I'll describe the bot's programming at a high level. But first, a little background...
I started undergraduate as an english major... then graduated with a degree in neuroscience. Although I have pretty firmly switched my career plans toward math and science, I still appreciate and respect art. As a reflection on my artistic pipedream, I designed my bot to write haikus. If you don't remember haikus from high school, hit that link in the previous sentence.
For connecting to Twitter, I used the tweepy library. I simply wrote a subclass of StreamListener to process incoming tweets and write them to a file called tweets.txt. You can read more about this process in the tweepy docs (linked above).
Each time I start my bot, most of the high-level functionality is located in one function, do_tweet().
For counting syllables and determining parts of speech, I used NLTK.
Counting syllables:
As you can see, the algorithm randomly chooses a word of syllable length sw, which is generated by the pick_syl() function. If this is hard to interpret, don't worry. It took me a while to come up with an algorithm that would randomly choose words but still adhere to the 5/7/5 haiku format. The conditions like if ix == 0 and line == 0 are there to determine which line of the haiku is being written. In this case, the first word of the first line is being chosen. Then the lines:
sdict1 = dict([(sk, sv) for sk, sv in sdict.items() if pos_tag(word_tokenize(sk),
tagset='universal')[0][1]=='NOUN']) # Chooses a noun
gsyls = syl_of_size(sdict1, sw) # Chooses a noun of syllable length sw
Then, setting up the haiku in order and writing to the file haiku.txt:
So currently this bot would write haikus with randomly chosen words, which is cool... but we can do better! ;^)
Eventually when I have a little more time, I'll give meta ai ai haiku more smarts, but for now the bot uses a simple algorithm for determining the "best" tweets, then recycles words from these tweets. Dumb, I know... it's a work in progress.
Here's a link to my Twitter bot meta ai ai haiku: robcapps.com/docs/haiku. Please feel free to ask questions in the comments section!
