I joined github because although I’ve been writing code since high school, there’s precious little of it to be found. Joining github has given me a place to archive, motivation to create, and an opportunity to participate in a culture of sharing.

In high school I took two years of computer science. My first year we started with punch cards that had to be processed at the school board’s headquarters, but thankfully we were able to move on to BASIC on Commodore 64s halfway through the year. The second year, we had Unisys ICONs. I had no idea that I could have kept on with Computer Science in university! I don’t know where the guidance counsellors were or how I could have been so clueless at the time — although since no one in my family had ever been to university it’s not such a surprise looking back.

When I did make it to post-secondary, I was still interested in tech so I took all the CS courses for non-majors. There were some gems in the IT Minor (Programming in C, CyberEthics, and some html, css, and javascript), but I wish I’d taken a double major in CS. And now, since I never use C, I really wish I had easy access to the programs I wrote. Enter github: now I have a reason to dig them out of the digital graveyard that is my box of old hard drives and give them new life. And now I have a place to record my adventures in Python — like Robo-Sam!

Couldn't find my SUP pics so snorkelling will have to do.

I got to try Stand Up Paddling (SUP) for the first time last fall while in Hawaii. We were told that stand up paddling was a traditional means of transportation between the islands – and from where I was on the coast of Maui, in the Makena area, it didn’t seem inconceivable that I might paddle a few miles to Lanai with a basket on the front of the board…

As it turns out, I’ll be attending the Museums and the Web conference in San Diego next month and just saw on Twitter (hashtag #mw2012) that there’s a contingent possibly planning some SUP while we’re in town. That sounded good enough to me (there’s something pretty incredible about standing on top of water and paddling around) – but then I checked. out. the. website.

Someone has found a company that doesn’t stop at renting boards. They also offer classes, including SUP Yoga. People actually do yoga on the boards. Early in the morning. They also offer core workouts and a unique cross-training experience:

This is an intense and athletic class which consists of a strong distance paddle, soft sand run and sprint intervals, cadenced and interval paddle strokes. Prepare for a SUP race or just add this to your weekly fitness regimen.

Pretty incredible what people do, eh? I don’t think I’ll be doing yoga or a fitness workout standing on a board. However, I really am hoping to spend some time in (or on top of) the ocean while I’m there.

Here’s what we did to Chapter 4: Guess the Number. To run this code you’ll have to copy and paste it in a text editor, save it (numberguessing.py) and run it from the terminal (type: python numberguessing.py). You can really see my kid’s…uh…sense of humour… in these programs. Keep in mind he’s a beginner. I realize some line breaks, etc would add to the aesthetics, but I’m trying to reduce cognitive overload. One thing at a time!

<code># March 11, 2011. Mama &amp; me
# based on _Invent with Python_ by Albert Sweigart
# Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
# This is a guess the number game.

You might argue that this makes my kid look pretty rotten, but I promise you, he really is a sweetheart.

Here’s the map he made:

Blue is for pools
Red is for libraries
Purple is for schools
Orange is for stores
Green is for restaurants

I’m inspired by my daughter, who’s taking her last year of high school computer science. The school she’s at offers three years of courses. In year one they do Turing (and Flash), the next is Python, and in the last they do Java. She’s planning on going on to study either Computer Science or Engineering at university in another year. She also loves the humanities so I’m excited to see what combination she puts together with double majors or minors.

I’m eager for my kids to grow up as creators, not just consumers of technology. The two younger kids both have old computers that run Ubuntu (one has no internet, one has restricted internet) so their use of the machines might be different because of that. They first started out making their own levels in kGoldrunner, but they’ve moved on to spreadsheets of stats for the characters in the role-playing games they create as well as using the word processor in Open Office to write stories for their games. Learning to program seemed the next step, but one they weren’t making on their own. Friday night at the coffee shop seemed like a good way to clear away distractions, learn something new, spend time with mom, and wait for big brother to finish practice. It’s also great for the youngest to learn something before his older brother. Youngest kids often get stuck tagging along and doing everything last. An added bonus here is altering that pattern.

We made the above map in about an hour. We started in Google Spreadsheets, which he’d never used before (limited Internet), though he’d used OpenOffice Calc so understood how a spreadsheet works. He made the list of fifteen places he’d like to map. Then we used map search plus everything search to track down the street addresses and postal codes of all the places on the list. We got to talk about good web practices because the first few addresses we found were images of text instead of text — frustrating! He quickly learned we wanted addresses as selectable text so that there’d be no need to retype long strings of text.

Once all the addresses were found (we took turns after awhile because this was tedious) we added a data type for each: pool, library, school, restaurant, or coffeeshop and had a look at our choices for map icons. He decided which marker he wanted for each data type and we added that to the spreadsheet.

Next we imported the spreadsheet into Google Fusion Tables, set the map to grab location using the street address in the Address column and visualized as a map. Then we modified the map style to use the marker specified in the Icon column and cool! a map was born! He knows that he can go back to his map and add more data, re-purpose it, or use it as an example for a new one.

Once it was done, we shifted to a conversation about places he’d like to go. He navigated Google Street Maps to travel to Spain, Venice (to see canals), Paris (to see the Eiffel Tower), Iqaluit, Japan, Korea, and Rome. We walked down the streets in every place that had street view (we’re both bad at controlling the little person) and looked at the relationships between cities and large bodies of water.

I really do love these Friday night nerd sessions and I know that I’m making it normal to use computers for more than surfing. He’s learning that he can visualize his data, turn his stories into games, and make whatever he can imagine.

Kids are all back at school for the first time in a week. The oldest and the youngest have been sick. I don’t recall the last time anyone missed this much school and it certainly has never happened where two were this sick at the same time. Because it’s been awhile, our morning routine was a bit off. The kids who got used to sleeping in (albeit with fevers) were up early today for a change.

And then the pant wars began. It isn’t even about the pants I’m sure, but rather one of those chances to practice being stubborn/defiant/independent. I don’t know where they find the worn out pants with holes in the knees, but one kid manages to find them on school days when I’m extra tired. Once they’re on, he’s committed to them, and it’s a battle to get him to change into something else. Today the first change was into dirty pants from the hamper, covered in materials from a 2-days-ago project. Eventually everyone made it out the door dressed for the winter weather, with plans for pants shopping after school. /sigh/

Key to icons:

	Large red = Community Centres
	Large purple = Libraries
	Large blue = Arenas
	Small green = Heritage Sites (listed & designated)

Process

This is how I made this map, but not exactly what I’d do next time. See note below.

1. download csv files from the City of Windsor Site
2. refine data using Excel* (see note below): columns were in different order on different spreadsheets, not all included all columns, added data type and icon type.
3. import all spreadsheets into Google Fusion Tables
4. modify data to recognize lat & long coordinates as Location
5. Visualize map to view the data points on GoogleMaps.
6. Embed map in blog.
7. Share!

*Next time I plan to use Google Refine to clean the data. Working across multiple spreadsheets was a pain.

Total time to make the map: 30 mins.

Because I’ve been with Teksavvy so long I’ll qualify to keep my current package (200 GB for under $40) under the CRTC’s grandfather clause if some kind of UBB is approved. I sure hope it isn’t and I’ve written my representatives and donated to OpenMedia.ca. However, I’m well aware of wishes and horses, etc.

What I’d really like to do is completely drop the account I currently have with Bell – the one that brings the internet across the phone lines to the house. I want to switch over to dry DSL. Unfortunately, if I switch to dry DSL, Teksavvy’s phone staff believes they’ll consider me a new customer on a new account and I’ll lose my grandfather-eligible status.

photo © 2010 Sander Veenhof | more info (via: Wylio)
Rather than go through all the steps Gene showed us I’ll point you over to the tutorial he wrote up from the session. I’m anxious to fill my neighbourhood with giant pigs and old architecture. I’m interested in the applications of AR for history & place projects, particularly oral history audio files. My real fear of AR is people crashing into things while they walk around holding their phones out in front of them, but it’s a risk I’m willing to take.

While there seems to be plenty of interest in AR in the San Franciso Bay Area, I haven’t found any locals working with it yet. If you’re in Windsor/Detroit and want to get involved in some local AR please let me know!

I’m not sure what type of research I’d use Fusion tables to model, but as I work through the examples I might figure that out.

I spent a lot of my time trying to copy Mano’s urls into my own browser so I could follow along with what he was doing. It got easier once I found a data set to manipulate, but I would have gotten more out of the session if I’d been able to work through some meaningful visualizations instead of watching a demo. There were some great examples shown and I tried to gather the urls for those for working through later.

I thought we were going to get an intro to working with the api, but I think we attendees were more beginner than Mano had expected. I’m not a developer yet, but I’m working on it. He did mention that as a DA it’s his job to answer questions people have as they work with the api and if a group gathers 25-50 people (local to SF) or 50-100 (outside the area) Google can send an advocate to run a bootcamp session. Something to keep in mind as I get further along here….

I’ve never done anything remotely close to text mining, which is why I attended this session. Here’s what I learned:

• In its most basic definition, text is words.
• N-grams: are sequences of 1 or 2 or 3, etc words. In the sentence “I had a great time and learned so much at THATcamp” the 1-grams are I, had, a, great, time, and, learned, so, much, at, and THATcamp. The 2-grams are “I had”, “had a”, “a great”, “great time”, “time and”, “and learned”, “learned so”, “so much”, “much at”, “at THATcamp.” The 3-grams are “I had a”, “had a great”, “a great time”, etc. Text mining can examine how many times an n-gram appears. Sentences can be built by stringing together n-grams. Sometimes they sound a bit off.
• Words have roles as parts of speech, e.g. nouns, verbs, adjectives, etc. (I think I spent half my elementary school years diagramming sentences in English and then in French.) Each part-of-speech (POS) has a behaviour in relation to other words in the sentence. In the process of “parts-of-speech-tagging,” every word in a corpus is tagged (i.e. POS tagging). The corpus used to train a parser matters: some have been trained using the Wall Street Journal; others use much older texts. Once a parser has been trained it can be used to analyze a specific text or set of texts – for example, to return all the adjectives in sentences that contain the words “woman” + “should.”
• Stanford Tregex was given as an example for visualizing results from parser as trees. This makes results much easier to analyze.
• Metaphors, irony, sarcasm, emoticons, etc. are hard for parsers to spot. Basically this is all of literature. Computational linguists haven’t dealt with this yet, but perhaps this is where digital humanists come into the conversation.
• Computational musicologists have been analyzing sound bites since the late 1960s but there doesn’t seem to be much cross-pollinating there yet.
• Topic modeling is a way to group words that are frequently used together; these are often sematically coherent. “Dynamic topic models” shows topics plus how they have changed over time. A popular toolkit for topic modeling is Mallet.

A variety of tools were suggested:

• Command line: http://nlp.stanford.edu
• Java: OpenNLP & Mallet
• Python: NLTK. This was the recommended tool for a beginner, but will need to create a POS tagger so go look at Stanford’s
• Ling pipe

An example of text-mining an historical diary done by Cameron Blevins @historying at StanfordU:

• http://historying.org/2010/04/01/topic-modeling-martha-ballards-diary

Some limitations of text mining:

• So far there has been an emphasis on English language tools so other language tools are not very good yet. Arabic and French language tools are getting there, but I didn’t catch which ones were worth checking out.
• The first step of text-mining is to digitize records. OCR, mechanical turk, and grad students were suggested as possibilities for getting through this stage.

I’ll be working through this list (just as soon as I get my text in a digital format that can be processed).