jduckles

# 1 - A Mermaid Diagram

This is an example of a prompt to the LLM which resulted in a code block which represents code for a simple diagram. Without knowing the mermaid language, you can ask the LLM to build a diagram for inclusion in your publication. We've shared the result of a prompt in this document for your reference in the "Example" section. Try your own prompt in the "#Try it yourself" section. 

With this file `1-mermaid-diagrams.smd` open in our VSCode plugin, you can click the "Run" button and it will re-run the prompt agains an LLM. This will result in a new version of the diadram being drawn. It may look a little bit different than the one you saw when you first opened the document. 

## Example 

::: create a mermaid horizontal diagram with rectangular boxes of four major steps in the process of photosynthesis >>>

jduckles

# Define the source directory and get all .md files
SRC_DIR := .
MD_FILES := $(wildcard $(SRC_DIR)/*.md)

# Define the output files for option1 and option2
PANDOC_FILES := $(patsubst %.md,%-pandoc.docx,$(MD_FILES))
STENCILA_FILES := $(patsubst %.md,%-stencila.docx,$(MD_FILES))

all: pandoc stencila 

jduckles

blueprint:
  name: Frigate Notification
  description: |
    ## Frigate Mobile App Notification

    This blueprint will send a notification to your device when a Frigate event for the selected camera is fired. The notification will initially include the thumbnail of the detection, but will update to include actionable notifications allowing you to view the saved clip/snapshot when available, or silence the notification for a configurable amount of time.

    With this blueprint, you may send the notification to multiple devices by leaving "Device" blank and instead use a [notification group][1].

    ### Required entities:

jduckles

#!/bin/bash

# Make a PDF look scanned. 
# Extracted from https://github.com/baicunko/scanyourpdf and modified for smaller output files (compression lower density).

INPUT_FILE=$1
/usr/local/bin/convert -density '80' ${INPUT_FILE}  -colorspace 'gray' -linear-stretch '3.5%x10%' \
	-blur '0x0.5' -attenuate '0.25' +noise  Gaussian -rotate 0.5 \
    -compress lzw -quality 50  scanned_${INPUT_FILE}

jduckles

#! /bin/bash

# Python Script to pull down title of web page and enter it into a markdown snippet. 

~/bin/url2mdlink.py %clipboard

jduckles

#!/bin/bash

for i in $(cat list_of_items.txt); do 
  # This echoes out each item
  echo $i;
done

jduckles

# The website is itself a Git repo
git clone https://github.com/datacarpentry/datacarpentry.github.io
# enter the directory after we clone the repo
cd datacarpentry.github.io
# enter the directory where posts are, posts are text in Markdown
cd _posts
# Lets look (using grep) for the line in all posts that contains the "Subheadline" 
# subheadline seems to be used to flag posts including community elements 
grep -r "subheadline:" * |grep -i community
# grep -r looks recursively through directories, and grep -i makes the match case-insensitive (ie. community = CommunIty)

jduckles

library(tidyverse) 
library(jsonlite)
# Look for manifest in bucket "example-public-bucket" 
data_manifest <- as_tibble(fromJSON("https://abacusworkflows.blob.core.windows.net/example-public-bucket/manifest.json")) 
# Select down from manifest to just records with shortname "nz_stats_livestock"
livestock_record <- data_manifest %>% filter(shortname == "nz_stats_livestock")
# Open first file of nz_stats_livestock record
livestock_data <- read_csv(livestock_record$files[[1]])
# Cleanup dates
livestock_data <- livestock_data %>% mutate(Year = as.numeric(str_remove_all(Year, "As at June 30 ")))

jduckles

#!/usr/bin/python
from struct import pack, unpack         #Some unpacking for the WAV encoding
from FFT import *       		#Some FFT goodness
from Numeric import *                   #Gotta have speedy math
import wave                             #For opening WAV files
from pylab import *	             	#For ploting
from matplotlib.widgets import Slider, Button, RadioButtons

import os
from time import time

jduckles

mydata1 <- data.frame(letter = c("a","b","c","a","b","c"))
mydata2 <- data.frame(letter = c("a","b","c","a","b","c","a","b","a"))
stuff <- list(mydata1,mydata2)


myawesome <- function(df) {
  levels <- c("a","b","c")
  as.factor(df$letter)
}