Programming

I’ve written previously about extracting and processing mp3 files from web pages. The use case that I described, obtaining Russian word pronunciations for Anki cards is basically the same although I’m now obtaining many of my words from Forvo. However, Forvo doesn’t seem to apply any audio dynamic range processing or normalization to the audio files. While many of the pronunciation mp3’s are excellent as-is, some need post-processing chiefly because the amplitude is too low. However, being lazy by nature, I set out to find a way of improving the audio quality automatically before I insert the mp3 file into my new vocabulary cards.

Indigo currently shipping version 7 is a leading Mac home automation software package. One of it’s mostly widely-used features is its ability to execute user-provided Python scripts of AppleScripts. In my previous introduction to scripting Indigo with Python I showed how to use the Indigo plugin host to execute Python scripts. In this post, I’ll describe how I use a third-party charting package rrdtool to graph data from Indigo by taking advantage of Indigo’s ability to execute arbitrary Python scripts. This tutorial is focused on using Python as a bridge between Indigo 7 and rrdtool. If you are interested in a solution that takes advantage of AppleScript and bash scripts to do the samee thing, see this thread on the Indigo forums.

Among the many reasons I use iTerm2 in lieu of the macOS Terminal is its AppleScript support.

I recently had the need to automate some tasks on my Amazon Web Services EC2 server in a way that takes advantage of iTerm2 AppleScript functionality.

Use case

I’ve found recently, that my screen sessions were disappearing. Although I haven’t completely excluded other causes, some have suggested that infrequently-reconnected sessions can be cleaned up. Since I’m not a Unix sysadmin, I’m not sure about this. However, I decided to test the hypothesis by writing an AppleScript that logs into my EC2 server, attaches to each screen session, detaches and closes the connection.

I’ve used Indigo home automation software for a few years. It’s a integrated home automation software environment for the Mac and its a solid stable and well-supported platform.

Within Indigo, it’s possible to script triggers and actions either AppleScript or Python. It’s funny - AppleScript often looks like the easier route to take. It looks more like plain English than Python. But as they say, looks are deceiving. Two bits of bad news put the nail in AppleScript’s coffin for me - as least with Indigo.

As I’ve mentioned before I use Anki extensively to memorize and practice Russian vocabulary. With language learning in particular, adding spoken pronunciations to the cards makes an enormous difference. Since I use Open Russian extensively to provide information to built my Anki cards, it’s a natural source of audio data, too. To optimize my learning time, I built two small scripts to grab and rename the audio files from the Open Russian site. First, I’ll describe my workflow.

Indigo is a well-known home automation controller software package for Mac OS X. I’ve written a plugin for Indigo 6 that allows you to create a virtual weather station from Environment Canada data. If you live in Canada, this will be a useful way of using weather data in your Indigo rules. For example, you could use wind and temperature data to adjust your irrigation schedule.

You can download the plugin from its git repo. After downloading the files, you’ll just need to configure them as a plugin. To do this, create a new folder and rename it EC.indigoPlugin. Copy the Contents folder that you just downloaded. Right-click on the EC.indigoPlugin bundle and Show Package Contents. Paste the Contents folder here. To install in Indigo, double-click the bundle file.

The spaced repetition software system Anki is the de facto standard for foreign language vocabulary learning. Its algorithm requires lots of performance data to schedule flashcards in the most efficient way. Anki displays these statistics in a group of thorough and informative statistical graphs and descriptive text.

However, they aren’t easily available for the end-user to export. Thus, the reason behind the companion projects AnkiStats and AnkiStatsServer.

The premise is that you can run your own more extensive experiments and statistical tests on the data once you have it in hand. A bit of technical expertise is needed to get it operational but if you are up to it, clone the github repos above and go for it.

I have a number of AppleScript applications that need to run at odd times. These maintenance tasks often attempt to run while the computer is sleeping. Particularly those that rely on UI scripting do not function during this period.

This most flexible way of dealing with this is to manipulate the power management settings directly via the pmset(1) command.

The variety of options available using pmset is staggering and beyond the scope of this post. Here’s what I do to wake the computer up at specific times so that scheduled AppleScripts can run:

After waking up this morning with my mouse locked onto the Anki icon in the dock and trying to figure out how to get Activity Monitor up and running so I could force quite my Automator application that I described yesterday I figured it was back-to-the-drawing board.

I’d like to have used the Accessibility Inspector to manipulate the PyQt objects in Anki’s windows, they aren’t exposed in a may that you can script them. But System Events rules all.

While working on a project to automatically collect statistics on my Anki databases (stay tuned…) I worked out a system for scheduling synchronization from my desktop OS X machine.

Prerequisites

• LaunchControl is a GUI application that lets you create and manage user services on OS X
• Anki is a spaced repetition memorization software system

The solution relies on Automator. Normally, I don’t care much for Automator. It has too many limits on what tasks I can accomplish and workflows created with it are often fragile. However, in this case, we take advantage of its workflow recording feature. We’re going to record the process of opening Anki, selecting the profile to sync, then quitting Anki. This sequence of events ensures that the database on the local system is synchronized with the remote version.