Stuart Axon stuaxo

Packages needed

$ sudo apt install v4l2loopback-dkms v4l2loopback-utils

Create virtual device

$ sudo modprobe -v v4l2loopback exclusive_caps=1 card_label="Virtual Webcam"

Ubuntu in QEmu via UTM on a Mac

UTM is available from the app store for £9, or here: mac.getutm.app

Look up the instructions for installing Ubuntu, install it using Qemu. I used this 22.04 image (As of 3-Aug-2023 I was NOT successful in installing, or upgrading to 23.04) https://cdimage.ubuntu.com/jammy/daily-live/current/jammy-desktop-arm64.iso

	[build-system]
	requires = ["hatchling"]
	build-backend = "hatchling.build"

	[project]
	name = "sqlcmdurl"
	version = "0.1.0"
	description = "Connect to SQL Server using dj-database-url format"
	dependencies = [
	"dj-database-url",

	#!/bin/bash
	# Install the Microsoft Aptos Fonts
	# Requires: lynx, unzip, unrtf
	set -e

	URL='https://download.microsoft.com/download/8/6/0/860a94fa-7feb-44ef-ac79-c072d9113d69/Microsoft%20Aptos%20Fonts.zip'

	TEMP_DIR=$(mktemp -d)
	cd "$TEMP_DIR"
	wget -O aptos.zip "$URL"

	#!/bin/bash
	#
	# script that paraphtases info from the debian wiki at: https://wiki.debian.org/ppviewerFonts
	#
	#
	set -e

	DOWNLOAD_URL="https://archive.org/download/PowerPointViewer_201801/PowerPointViewer.exe"
	EXPECTED_CHECKSUM="249473568eba7a1e4f95498acba594e0f42e6581add4dead70c1dfb908a09423"
	FONT_DIR="$HOME/.local/share/fonts/ppviewer"

	def is_container_type(type_hint: Any) -> bool:
	"""\
	:return: for containers you might add to a dataclass, like List[...] but types like str.
	"""
	origin = get_origin(type_hint) or type_hint

	if origin is str:
	return False

	if isinstance(origin, type):

	# Extend https://yeonwoosung.github.io/posts/pydantic-vs-dataclass/
	# to convert nested dataclasses.

	from functools import lru_cache

	from dataclasses import fields, _MISSING_TYPE, is_dataclass
	from typing import Any, Optional

	import pydantic

	aws-env() {
	export AWS_REGION=$(aws configure get region)
	export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
	export AWS_ACCESS_KEY_ID=$(aws configure get aws_access_key_id)
	export AWS_SECRET_ACCESS_KEY=$(aws configure get aws_secret_access_key)
	echo "AWS environment variables set:"
	echo "AWS_REGION=$AWS_REGION"
	echo "AWS_ACCOUNT_ID=$AWS_ACCOUNT_ID"
	echo "AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID"
	echo "AWS_SECRET_ACCESS_KEY=********"

	DATA_FILE = "your-file.parquet"

	import pandas as pd

	# Read the Parquet file
	df = pd.read_parquet(DATA_FILE)

	def df_schema(df):
	# Print the column names and their types
	for column in df.columns:

	#!/bin/bash
	#
	# Creates a Jupyter kernel for a virtual environment.
	#
	# Usage:
	# ./create_kernel.sh [venv_name]
	#
	# Arguments:
	# venv_name Optional. Name of the virtual environment to create kernel for.
	# If not provided, uses currently activated environment.