James Goldie jimjam-slam

In Svelte:

export each Illustrator layer as a separate SVG (using the same artboard)
modify the SVG files:
- remove the xml parent node;
- rename the extension to .svelte
- if using several layers, rename the classes to avoid collisions between them when inlining
import the components in svelte: `import FileName from "./FileName.svelte"
use the layer components sequentially in a parent component with <FileName>. wrap them in a parent div.
stack the layers visually by giving the parent div position: relative and the map layers position: absolute; top: 0; left: 0;. use :global([selector]) to inject the styles into the svgs
insert other svelte components, like buttons, in between or on top of the layers

flutter_pytorch_mobile

lib/model.dart:

Defines:
- getPrediction: calls the predict method
- getImagePrediction: calls the predictImage method and then compares the maximum score agains tthe label list
- getImagePredictionList: calls the predictImage method, returns all scores

android/src/main/java/io/funn/pytroch_mobile/PyTorchMobilePlugin.java:

Shapefile(s) to vector tile set

graph LR
    A[Vector source - eg. shapefile] --> |ogr2ogr| B[geoJSON]
    B --> |tippecanoe| C[Set of .pbf tiles]

	isLandscape = Generators.observe(next => {
	// yield initial value
	next(screen.orientation?.angle == 90 \|\| screen.orientation?.angle == 180);

	// define event listener
	const listener = () => next(screen.orientation?.angle == 90 \|\| screen.orientation?.angle == 180);

	// attach event listener
	screen.orientation.addEventListener("change", listener);

	gdp <- data.frame(
	name = c("us", "cn", "jp", "de", "in", "gb", "fr", "it", "ca"),
	gdp = c(26854, 19374, 4410, 4309, 3740, 3160, 2924, 2170, 2090),
	x = c(1,2,3,1,2,3,1,2,3),
	y = c(3,3,3,2,2,2,1,1,1))


	p1 <- ggplot(gdp) +
	aes(x, y) +
	geom_flag(aes(country = name, size= gdp)) +

	my_func <- function(alpha, beta, gamma) alpha + beta + gamma

	# no
	tibble(a = 1:5, b = 6:10, c = 11:15) %>%
	mutate(z = pmap_int(., my_func))

	# yes
	tibble(a = 1:5, b = 6:10, c = 11:15) %>%
	mutate(z = pmap_int(
	dplyr::select(., alpha = a, beta = b, gamma = c),

	library(sf)
	library(httr2)
	library(ggplot2)

	# based on https://community.esri.com/t5/gis-blog/
	# accessing-arcgis-rest-services-using-r/ba-p/898451

	api_query <- url_parse("https://geo.abs.gov.au")

	api_root <- "/arcgis/rest/services"

	# in hindsight i probably should've used a network analysis package to do this

	library(tidyverse)
	set.seed(1)

	# create a sample data frame of origins and destinations
	testdf <-
	tibble(
	origin = sample(letters, 10),
	destination = sample(letters, 10),

	project:
	type: website