I've been posting somewhat haphazardly with regards to my amplifier goings on, and am trying to wrap my head around the concept of audio transformers. I promise this will settle down once I stop barfing my random technical thoughts online.
Anyway, I think I made a breakthrough today by running some white noise through the 1:1 transformer I pried out of an old digital TV box.
I know this is not the way to do stuff, but I don't have an oscilloscope and Waveosaur is free.
What you see there is the frequency response for white noise, and a telltale peak at 1242 Hz. The transformer was being used in an RLC high-pass configuration, with 240Ohms and a 100nF cap, which would indicate my transformer is roughly 0.16 Henries. Different cap and resistor values seemed to be consistent with this pattern, so at least I have that one part of the amp somewhat figured out.
Now to drive it from the 386 without blowing another power cap...
Just a little something I'm working on. I got a transformer hooked up to a small LM386 guitar amp I built, and so far the sounds its producing aren't unpleasant. More to follow (including some Python code, because I got tired of manually calculating the 386's gain and bass boost frequencies).
Did you remember to take the second left at RefSeq Genomic Accession ID?
I complain ad nauseaum about the various competing formats used to identify various biological molecules. Here's a bug#&*% insane helpful map from the people at bioDBnet.
I've been on a little bit of a communication kick lately, having against all reason actually enjoyed presenting at GLBIO. In truth, representing data visually is just a subset of the task of communication. Through visualization, we transfer data from a medium to our brains via the sense of sight. The peculiar custom of transferring data through standing in front of a crowd of strangers and talking for an hour or two is curious, but similar in many ways.
A few months ago of my co-workers was kind enough to direct me to the work of Edward R. Tufte, a critic and theorist who specializes in visual data representation. I am ashamed to say that until she literally put a book of his in front of my nose, I hadn't seen any of his work. I have since remedied this.
Among his writings, there exists a well-distributed, crabby critique called 'The Cognitive Style of PowerPoint' that I've recently taken to heart. By taking it to heart, I mean that I've used its principles to utterly dismantle the PowerPoint presentation I put on at GLBIO 2012. I redid that presentation in-lab today with the following set of basic rules:
Only use slides for things that slides are good at.
If you ask Tufte what slides are good at, he would tell you 'very little'. He spends a great deal of time discussing the many ways in which PowerPoint slides, and particularly their templates, just plain suck at communication. Data is parceled out in 10 to 20 line chunks, forcing the presenter to unnaturally partition their narrative. PP graphs are low resolution, illustrating only the most blunt of points, and used in places where a simple 'and then X happened' would have sufficed from the presenter. For the most part, a standard template for a PP presentation serves less as a means of communication than as an assistance device to organize the presenter, albeit within the bizarre constraints of 'one slide per topic' regardless of the scope or complexity of that topic.
With all of this in mind, I looked at the slides I had, and I started to pick them apart. Immediately gone were organizational slides. Unless a slide could communicate a concept more effectively than speaking alone, it was scrapped too. Out of 27 original slides, I kept 8, and those were severely cut down and held almost nothing but graphics.
8 slides to represent an hour long portion of my talk.
The results of this cull?
Twofold.
The ugly side of this was that I had underestimated the usefulness of PowerPoint as an organizational device. Stripped of a hierarchy of bullet points, I realized at midnight before I was to present, I had lost my narrative. The mess of bullet points that I transferred to five pages of printout were a poor substitute for a rehearsed talk. I stumbled over what should have been a flowing exploration of graph visualization practices.
Though this resulted in a few uncomfortable moments, it drove home a point Tufte made often in his critique: giving presentations is hard. Having PowerPoint, at best, turns a poor presentation into a boring one. Instead of spending hours of my time culling my slides, I should have invested that time into practicing my narrative and ensuring that the more efficient form of data transfer, the 150 word-per-minute speech, was as well-oiled as it could get.
The second result of my re-formatting was that I saw how effective a good graphic could be. If anything saved the presentation, it was this slide:
This horrible, horrible slide.
This slide was an illustration of bad visualization practices. It comes from this article in Nature, which is, ironically, about improving graph visualization. I have seen many different versions of the same concept repeated over and over again in graph visualizations: people misunderstanding the purpose of graph visualization entirely. Graphs excel at communicating to the viewer information about relationships between objects. In this example, none of the graph edges are remotely traceable, the relationships between the implied complexes are indistinct and there is no evidence to suggest WHY any of the complexes should exist. There is NOTHING achieved by this graph that could not have been done more elegantly with tables of protein names. It is the Michael Bay of graphs.
"...and then the protein complex transforms into a DEATH JET
that shoots FIRE while Megan Fox SWEATS PROVOCATIVELY!"
The effect of showing this graph was immediate: the entire room groaned. They understood, very quickly, the concepts I had been stumbling across in my unpracticed narrative. Including the slide was to my advantage. It saved my ass.
So, two lessons learned.
In closing, I'd like to leave you with something that kept me sane while I experimented with dangerous presentation techniques. The following is the first part of a lecture given by Louie Simmons, a trainer and competitive power-lifter. I listened to this lecture in between editing sessions to remind me that a good presentation doesn't need ANY slides. It needs content and a presenter capable of communicating it.
GLBIO 2012 has come and gone. I came, I ate the food my registration fees paid for, I presented. In case you're curious, the slides and relevant materials I presented are here.
And here is what I learned:
Tutorials need to go easy on figures and formulas. Audience retention was terrible in the tutorials I witnessed, and I suspect part of it was due to presenters translating a paper to Powerpoint too literally. Bioinformatics is a very broad discipline; it's likely that the only person in the audience that understands enough about your paper to follow a presentation on it is you.
Your presentation needs a punchline. Mine, sadly, didn't have one. I spent an hour discussing the ins and outs of a visualization workflow, only to have my example of that workflow... not be spectacular. Not that I think I needed a pyrotechnics display, but having a definite conclusion instead of just stopping dead and thanking your sponsors would have been the icing on the cake.
Pictured: Not the end of my tutorial.
Be open to criticism. I swear some people go to presentations just to be assholes. There's nothing worse than listening to two eggheads prattle on about the third variable someone chose in part 4 of their 18 part analysis for ten minutes. However, sometimes criticism comes from a place of shared interest, which means you meet the coolest people by listening to what they have to say.
In less than a week, I'll be journeying to Ann Arbor for GLBIO to run a tutorial on graph visualization for biologists.
Right now, this is Slide 1:
I may have to dial down the haranguing I had originally intended to occupy 119 of my presentation's 120 minutes. It's not like biologists are dangerous, even in large numbers, but exposing them to my rants about data representation and standardization might drive them back into the wet-lab and away from the computer desk.
