It's been a busy and fun week again!

Most of my time was spenting finalizing what the elevation profile Datasette enrichment would look like. I finally found the last piece of the puzzle laying around in a pile of Jinja template use practice. One of the goals this week will be to release the elevation package.

I made my first pull request for an open source project, datasette, in this case. (I make them at workr, but this is my first foray into the wider world.) 

ChatGPT provided the final kick to get the elevation maps plotted really eaisly.

Subsequently, I learned about the Pyplot package and how to add labels to charts.

I got a start back towards writing about dadding. I spruced up some writing I had lying around about cleaning the kitchen with the gang. I found another older piece that I love while checking out old blog entries here: Meerktas and Ravens. Oh, and then this one about what unschooling with toddlers lools like.

Goals

 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 :) 

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 and then the API. Finally did a Google search to find the API.

Install the datasette-extract plugin

I've run into an issue here. I think I have too old of a version of Datasette and Windows can't figure out how to uninstall it

Using cached datasette_extract-0.1a6-py3-none-any.whl (815 kB)

Using cached datasette-1.0a13-py3-none-any.whl (302 kB)

Using cached datasette_secrets-0.1a4-py3-none-any.whl (12 kB)

Installing collected packages: datasette, datasette-secrets, datasette-extract

  Attempting uninstall: datasette

    Found existing installation: datasette 1.0a3

    Uninstalling datasette-1.0a3:

ERROR: Could not install packages due to an OSError: [WinError 32] The process cannot access the file because it is being used by another process: 'c:\\users\\m3n7es\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\scripts\\datasette.exe'

Check the permissions.

I'll clone a dev environment for the plugin and then run in venv. Time now 21:00.

Still Installing

Adding Table Column Names

21:21 The column names have been defined with hints. 

id primary key

question follows a line starting with G ends with '?'

class Defaults to G for every question

subelement A number following G before a second letter

group_index The letter following subelement's number (G)(\d)(A-Z)(\d\d) Use \$3

group_number two digit number following group_index (G)(\d)(A-Z)(\d\d) use \$4

answer A single letter between parentheses that indicates the correct answer choice

answer_a next line starting with 'A.'

answer_b next line starting with 'B.'

answer_c next line starting with 'C.'

answer_d next line starting with 'D.'

I added the additional instructions

The questions and answers are in line sorted by headings that contain class (always G), then subelement (a single digit following G), then group_index (a single letter following the subelement), then group_number (a question number within the group_index), then the single letter correct answer enclosed in parentheses. The next line contains the entire question text for the question field. The next four lines in each question contain the four possible answers. The end of each question is denoted by '~~'.

I've copied the entire question pool starting at 

Time is 21:26 UTC

Extracting to Table

Error: Error code: 404 - {'error': {'message': 'The model `gpt-4-turbo` does not exist or you do not have access to it.', 'type': 'invalid_request_error', 'param': None, 'code': 'model_not_found'}}

OK. Looking at My OpenAI account I see:

OK! The API is like using a Clipper Card on BART. You have to pay up front

I'll try to extract again. It's 21:43.

It's Working!!!

[
  {
    "id": 1,
    "question": "On which HF and/or MF amateur bands are there portions where General class licensees cannot transmit?",
    "class": "G",
    "subelement": "G1",
    "group_index": "A",
    "group_number": "01",
    "answer": "C",
    "answer_a": "60 meters, 30 meters, 17 meters, and 12 meters",
    "answer_b": "160 meters, 60 meters, 15 meters, and 12 meters",
    "answer_c": "80 meters, 40 meters, 20 meters, and 15 meters",
    "answer_d": "80 meters, 20 meters, 15 meters, and 10 meters"
  },
  {
    "id": 2,
    "question": "On which of the following bands is phone operation prohibited?",

The engine is still cranking along at 21:47.

And Then </exceeds>

  {
    "id": 19,
    "question": "When is it permissible to communicate with amateur stations in countries outside the areas administered by the Federal Communications Commission?",
    "class": "G",
    "subelement": "G1",
    "group_index": "B",
    "group_number": "08",
    "answer": "B",
    "answer_a": "Only when the foreign country has a formal third-party agreement filed with the FCC",
    "answer_b": "When the contact is with amateurs in any country except those whose administrations have notified the ITU that they object to such communications",
    "answer_c": "Only when the contact is with amateurs licensed by a country whic...  Click to expand ... <exceeds maximum number of characters> ,,groupId,,quizzes,,element,,data,,result,,direct,,[]}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]}</exceeds>}]}]},"
  }
]

Did I hit the end of my billing envelope?

21:51 No, billing seems fine. I wonder is I need to add the file in as a pdf because of this message:

exceeds maximum number of characters

Trying again with a pdf file

Full Subelement at a time

I'll try a db that doesn't revolve around a memory table next.

No Memory Table DBs

Adding Remaining SubElement Group by Hand 

Can it do two subelement groups?

The rest of the groups in the subelement?

Descriptions Removed

 "id": "G1E12",

More Instructions

Don't give away the ending

After changing 'Additional instructions' to 

Let's flush the state and start 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 issues:

How would penelope make a line chart of two dimensional data (distance on the x axis , elevation on the y axis) in python, then create a png file of the chart, then dump out the PNG encoding of the chart so that it could be included in a data:image/png;base64 html tag

ChatGPT came back with an answer a few seconds later that was unlike anything I'd tried so far.

import matplotlib.pyplot as plt # Sample data (distance and elevation) distance = [0, 1, 2, 3, 4, 5] elevation = [100, 120, 110, 130, 125, 140] # Create line chart plt.plot(distance, elevation) plt.xlabel('Distance') plt.ylabel('Elevation') plt.title('Elevation Profile') plt.grid(True) # Save the chart as a PNG file plt.savefig('elevation_chart.png') # Show the chart (optional) # plt.show()

That would get me a chart. But how to get the chart back as png encoded text? ChatGPT wasn't done yet...

import base64 # Read the PNG file as binary with open('elevation_chart.png', 'rb') as file: png_data = file.read() # Encode the PNG image as base64 string encoded_png = base64.b64encode(png_data).decode('utf-8')

and that was that!

It took some tinkering, but I wound up with an enrichment that returned text based png images per row in our Datasette QSO log like the following:

In this MiscDotGeek article, we’re trying something… different. This is an experiment. You know my writing, you know my style. Read the article. Poke holes in it. Why? See at the end (don’t cheat!) High Altitude Balloons: Unlocking the Potential of Near Space Exploration Have you ever wondered what the view from the edge of …

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