## Decoding a Midea Air Conditioner Remote

Last month, I purchased a 6000 BTU Midea window air conditioner (branded Arctic King WWK+06CR5) and thought it would be convenient if I could control it remotely. Doing so would involve decoding the remote’s IR signals; for this, I used a USB Infrared Toy and the PyIrToy Python library. Control signals for other Midea air conditioners have previously been decoded, providing a starting point. Although the signals transmitted by my air conditioner’s R09B/BGCE remote are similar to these previous remotes, they are also sufficiently different such that the actual data transmitted shares little in common. The signal is transmitted on a 38 kHz carrier, with a time base, T, of 21 carrier cycles, approximately 1.1 ms. Each bit consists of the IR transmitter off for 1T followed by it turned on for either 1T for `0` or 3T for `1`. Each frame consists of a start pulse, six bytes of data, a middle pulse, and then the inverse of the six data bytes. The start pulse consists of the transmitter off for 8T and then on for 8T; the middle pulse consists of the transmitter off for 1T, on for 9.5T, off for 4T, and then on for 4T.

## Automated Document Creation and Typesetting with LaTeX

Creating a new document class file and then using this class is usually considered the “correct” way to typeset a form or other document generated with data in $\LaTeX$. However, there’s also the quick-and-dirty method of creating a regular $\LaTeX$ document every time in a script using some sort of string concatenation and then typesetting this, which also has its merits. When a class file is used, the class describes the document look and structure; a new $\LaTeX$ document still needs to be created each time to define the data. Not writing a class file and placing the document look and structure typesetting code directly in the generation script isn’t as clean as the class method as it mixes styling with data, but it does make some things easier. The quick-and-dirty approach doesn’t require knowing the additional $\LaTeX$ language features needed for creating a class, using only what would use in a normal document. In particular, it is useful for automatically generating documents that change in structure based on the input data or other more complicated logic. This can obviously all be implemented as a $\LaTeX$ class since $\TeX$ is a Turing-complete language, but general purpose scripting languages such as Python are easier to use for this, particularly since most programmers use them much more often than they create complicated $\LaTeX$ classes. The quick-and-dirty approach trades the class method’s cleaner design for ease of script creation. However, if the form will ever be created by hand, the class method is definitely superior.

## OpenStreetMap Baltimore City Buildings and Addresses Import

Two years ago, I started trying to import building footprints and addresses provided by the City of Baltimore into OpenStreetMap but was held up by red tape and eventually gave up.1 The City provides building footprints and parcel data on their open data portal; the download pages for these two datasets list the data as public domain, but the site’s terms of service is the same as the rest of City’s websites, saying the data is copyrighted. I had worked through the technical aspects of preparing and simplifying the building footprints for import and had started working on how to associate addresses from the parcel data but eventually gave up after I was unable to secure the needed licensing clarifications from the Mayor’s Office of Information Technology (MOIT).

This past fall, Elliot Plack, who then worked for Baltimore County GIS and was appointed to the Maryland Open Data Council by Governor O’Malley, got in touch with me about restarting the import, after finishing an import of similar data for the County. After meeting with Jim Garcia from MOIT, he was able to secure the permissions that we needed to proceed with the import. Additionally, he was able to get address point data, which is much superior to and easier to use than the parcel data I was originally going to extract addresses from.