Several years ago I wrote about adding analog-to-digital capabilities to the Raspberry Pi. At that time, I used an ATtinyx61 series MCU to provide ADC capabilities, communicating with the RPi via an I2C interface. In retrospect it was much more complicated than necessary. What follows is an attempt to re-do that project using an MCP3008, a 10 bit ADC that communicates on the SPI bus.

MCP3008 device

The MCP3008 is an 8-channel 10-bit ADC with an SPI interface^[Datasheet can be found here.]. It has a 4 channel cousin, the MCP3004 that has similar operating characteristics. The device is capable of performing single-ended or differential measurements. For the purposes of this write-up, we’ll only concern ourselves with single-ended measurement. A few pertinent details about the MCP3008:

New Year’s resolution time is at hand. But not for me; at least not in a traditional sense. I was inspired by David Cain’s experiments. In short, he conducts monthly experiments in self-improvement.

The idea of an experiment is appealing in ways that a resolution is not. A resolution presumes an outcome and relies only on the long application of will to see it through. An experiment on the other hand, makes only a conjecture about the outcome and can be conducted for a shorter period.

Yet another diversion to keep me from focusing on actually using Anki to learn Russian. I stumbled on the R programming language, a language that focuses on statistical analysis.

Here’s a couple snippets that begin to scratch the surface of what’s possible. Important caveat: I’m an R novice at best. There are probably much better ways of doing some of this…

Counting notes with a particular model type

Here we’ll use R to do what we did previously with Python.

Since one of the cornerstones of my approach to learning the Russian language has been to track how many words I’ve learned and their frequencies, I was intrigued by reading the following statistics today:

• The 15 most frequent words in the language account for 25% of all the words in typical texts.
• The first 100 words account for 60% of the words appearing in texts.
• 97% of the words one encounters in a ordinary text will be among the first 4000 most frequent words.

In other words, if you learn the first 4000 words of a language, you’ll be able to understand nearly everything.

Continuing my series on accessing the Anki database outside of the Anki application environment, here’s a piece on accessing the note type model. You may wish to start here with the first article on accessing the Anki database. This is geared toward mac OS. (If you’re not on mac OS, then start here instead.)

The note type model

Since notes contain flexible fields in Anki, the model for a note type is in JSON. The best guess definition of the JSON is:

I previously wrote about accessing the Anki database using Python on mac OS. Extending that post, I’ll show how to work with a specific deck in this short post.

To use a named deck you’ll need its deck ID. Fortunately there’s a built-in method for finding a deck ID by name:

col = Collection(COLLECTION_PATH)
dID = col.decks.id(DECK_NAME)

Now in queries against the cards and notes tables we can apply the deck ID to restrict them to a certain deck. For example, to find all of the cards currently in the learning stage:

Not long ago I ran across this post detailing a method for opening and inspecting the Anki database using Python outside the Anki application environment. However, the approach requires linking to the Anki code base which is inaccessible on mac OS since the Python code is packaged into a Mac app on this platform.

The solution I’ve found is inelegant; but just involves downloading the Anki code base to a location on your file system where you can link to it in your code. You can find the Anki code here on github.