hnbdr

• Introduction
• Attention
• Resources
• QOL
  • Gimp
  • Debugger
    • Debugger configuration for vscode
    • Installing debugpy
    • Connecting debugpy

• How it works

h-a-graham

# Description: This script is used to download HLS data from NASA's Earthdata
# STAC API and build a cloud-free composite image.

# ----- Functions -----

# band mapping for HLS SL data
#' @return a named character vector of band mappings for HLS SL data
hlssl_band_mapping <- function() {
  c(
    B01 = "A", B02 = "B", B03 = "G", B04 = "R",

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

charliejhadley

library("shiny")
library("tidyverse")
library("rnaturalearthdata")
library("sf")

# Using Africa instead of whole world as countries110 contains
# 3 invalid geometries. Also, too many examples use the USA everytime
africa_sf <- countries110 %>% 
  st_as_sf() %>% 
  st_transform(4326) %>% 

JosiahParry

library(googledrive)

# authenticate yourself
drive_auth()

gdoc_to_rmd <- function(drive_id, output, verbose = TRUE, overwrite = FALSE) {
  
  # get the doc
  doc <- drive_get(as_id(drive_id))
  

eliocamp

---
title: "Tweet thread"
author: "Elio Campitelli"
output: github_document
---


```{r}
knitr::opts_chunk$set(dev = "png", 
                      tweet_this = TRUE)

matteodefelice

import cdstoolbox as ct

@ct.application(title='PV Power Potential', 
               description = 'Computation of PV Power potential for a specific day using the dimensionless described in Mavromatakis et al. https://doi.org/10.1016/j.renene.2009.11.010',
               abstract = 'Lorem Ipsum')
@ct.input.dropdown('show_year', values=range(2010, 2017))
@ct.input.dropdown('region', values=['Europe', 'China'])
@ct.input.text('month', type = int, label='Month', default=7)
@ct.input.text('day', type = int, label='Day', default=1)

DavisVaughan

# This example demonstrates running furrr code distributed on 2 AWS instances ("nodes").

# The instances have already been created.

library(future)
library(furrr)

# Two t2.micro AWS instances
# Created from http://www.louisaslett.com/RStudio_AMI/
public_ip <- c("34.205.155.182", "34.201.26.217")

fbreitwieser

a = read.delim("~/Accuracy_as_a_function_of_sketch_size_and_set_size.txt")
head(a)
a$rel_error = sqrt(a$Mean.squared.error) / a$Exact.size

a$p = factor(a$Sketch.size..log2.)
library(cowplot)
library(ggplot2)

plot1 = ggplot(a, aes(x=Exact.size, sqrt(Mean.squared.error) / Exact.size, 

J-Cleeland

---
title: "albatross_breeding_cycle"
author: "Jaimie Cleeland jaimie.cleeland@utas.edu.au"
date: "12/12/2016"
output: html_document
---

Load libraries
```{r}
library(dplyr)