Copasetic Flow

 I started a project, ahem, yesterday to 'quickly' see if ChatGPT could read the entire United States general class amateur radio exam question pool into a Datasette instance using the datasette-extract plugin. As of this morning, I haven't been able to coax ChatGPT using the gpt-4-turbo model. I capture my rather raw notes below. The short version of this is that I was never able to get the AI to capture more than 19 questions at a time. I'm hopeful that the pool could be moved into a database table using iterative processes, but for now, I've run out of time for this quick project :)  Occasionally ChatGPT seemed to hallucinate out part of its process into the table Notes Follow I'm going to track how easy it is to get the general exam question pool into a database using the Datasette Plugin. I started this endeavor at 20:37 UTC. Get my already existent OpenAI API key ready to go 20:43: Done. As usual with OpenAI, the hardest part was finding login screens

 This project has been going on for a while, but remember, the point of the elevation profile project was to add the elevation profile between our ham radio—Project TouCans—and the receiving station at the other end of a QSO to the map of the QSO itself. This week, I finally got to start work on the maps themselves, and I've found a few things. First, an image added to a Placemark description without a  width="100%" tag will overflow the pane that Google Earth Web creates for the Placemark A user can scroll the chart horizontally, but theOh  information isn't all visible at once. Adding the width tag back in fixes the issue on Google Earth Web, but leaves Google Earth Pro with a very small chart I thought a good compromise between the two might be to house the image in its own div. For Google Earth Web we get but on Google Earth Pro we still get the tiny chart: It seems the only way to get this done will be to learn the new Google Earth kml 'Carousel' extensio

 I've outlined the work on the ham radio QSO Google Maps Elevation API Datasette Enrichment  (try saying that 3 times fast) over the last several days.  What I haven't talked about much, but thought about pretty often is what the released elevation enrichment should do. Here goes. The Datasette enrichment will store the JSON data returned from the Elevation format in a database column named 'elevation'. Remember, the enrichment is taking two coordinate pairs that form a QSO like the brown line shown between Villa Nueva, NM and Idaho (KJ7DT's QTH). It then determines the endpoint of a line that only covers the first 200 meters of that path, shown as the blue line below: (Yes, 8 year-old Tawnse and I were sitting on the side of the 'cliff' on the other side of the Pecos River from the campground during that QSO. Yes, Tawnse does have a lower center of gravity than I do and led me up that path. Yes, I did have a heck of a time getting back down once I turned

 The project to add elevation profiles to each ham radio QSO on a QSO map is moving along. I've started to prototype inserting elevation charts into the map—via kml—of elevation profiles per QSO today. I had a slight bit of consternation that the charts might not be visible at all after coming across a post on stackoverflow that indicated text encoded images cannot be used as icons in kml files for Google Earth. I am happy to report however, that text encoded images in description fields for kml Placemark tabs work great! A tag like: <Placemark> <name>kj7dt </name> .............. <description> <![CDATA[<div width="400px"<h1>kj7dt </h1>Date/Time GMT: <div><br></div>"2023-02-12T21:58:00"<div><br></div><div><img width="100%" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAH.............. .......FTkSuQmCC"></div></div>]]></descr

 I learned how to arrange labels on PyPlot charts yesterday.  I've mentioned that the scales on the ham radio QSO elevation profiles weren't entirely comparable to one another. Due to the variety off hills, mountain ridges, and coasts the kids and I make POTA activations on, the vertical scales on the elevation chart y axes range from three meters in total to dozens of meters. What's important (and interesting) about the various sites though isn't the sheer number of meters of descent or ascent, but rather, the angle of the terrain with respect to level. That angle gets subtracted from the natural radiation angle of the dipole that houses Project TouCans and, theoretically, extends the distance that TouCans can transmit before bouncing off the F2 layer of the ionosphere. Consequently, rather than determining a scaling algorithm to make all the elevation profile graphs returned for the various QSOs comparable, I elected to simply calculate the slope of the terrain over

 The initial release of the ham radio QSO elevation profiles Datasette enrichment is up and running!  (In the next few days, I expect there to be several posts on this.) First, let's talk about the beauty of ChatGPT for personal coding challenges, my personal coding challenges. Over the course of the last week, I've tugged back and forth at the the problem of moving data from the Google Maps Elevation API into a png chart that I thought was going to be provided by the Google Chart API. All of this work taught me a lot, but did not produce useful results. Yesterday though, I had ChatGPT open seeing what sort of job it could do creating electronic circuit schematics (spoiler: the results aren't pragmatically useful yet, although, they are pretty.) Since I had the app open anyway, I popped over to my ongoing conversation with ChatGPT about my fictional friend Penelope. (You can see her prompt below.) Here's what I jotted down to see if Penelope could solve my charting issu