arraytools

#!/bin/bash

# Usage: ./yt-int.sh <youtube_url>

if [ -z "$1" ]; then
    echo "Usage: $0 <youtube_url>"
    exit 1
fi

URL="$1"

arraytools

# This method assumes that the files 'casaos.png' and 'shiny-test.txt' exist in the 'www' subdirectory. 
# They don't have to be in the 'www' subdirectory. If they're located elsewhere, modify the file paths accordingly.

library(shiny)

# UI
ui <- fluidPage(
  titlePanel("Image Download Example"),
  
  mainPanel(

arraytools

# 1. Generate sample data
set.seed(123)
sample_data <- rnorm(100, mean = 0, sd = 1) # A sample from a standard normal distribution

# 2. Define the theoretical CDF (for standard normal distribution)
# In R, pnorm() is the CDF for the normal distribution
theoretical_cdf <- function(x) pnorm(x, mean = 0, sd = 1)

# 3. Compute the ECDF of your sample
ecdf_sample <- ecdf(sample_data)

arraytools

$ tree
├── app
│   └── myapp
│       └── app.R
└── compose.yml
$ mkdir -p app/myapp
$ sudo chmod 777 app/myapp # shiny user can write
$ docker compose up

# Click the 'Write Output File' button

arraytools

<?xml version='1.0' encoding='UTF-8' standalone='yes'?>
<sbgn xmlns="http://sbgn.org/libsbgn/0.3">
  <map id="causative.nwt" language="sif">
    <extension>
      <renderInformation 
        xmlns="http://www.sbml.org/sbml/level3/version1/render/version1" id="renderInformation" program-name="sbgnviz" program-version="6.0.1" background-color="#00000000">
        <listOfColorDefinitions>
          <colorDefinition id="color_1" value="#ffffffff"/>
          <colorDefinition id="color_2" value="#323232"/>
          <colorDefinition id="color_3" value="#ff8164"/>

arraytools

1. The Elevator Pitch Summary
Prompt: "Summarise this paper in 3–5 sentences, as if you're explaining it to a colleague in an elevator ride."

2. Key Findings Extraction
Prompt: "List the top 5 key findings or conclusions from this paper, along with a brief explanation of each."

3. Methodology Breakdown
Prompt: "Explain the methodology used in this study in simple terms. What are its strengths and potential limitations?"

4. Literature Review Assistant

arraytools

calculate_total <- function(items) {
  subtotal <- sum(items)
  tax_rate <- getOption("TAX_RATE", default = 0.1)  # Default 10% tax
  total <- subtotal * (1 + tax_rate)
  return(round(total, 2))
}

# Test the function with default tax rate
cart1 <- c(10, 20, 30)
print(calculate_total(cart1))  # Output: 66.00

arraytools

import requests
from requests.auth import HTTPBasicAuth

username = "USERNAME"
app_password = "API_PASSWORD"
base_url = f"https://api.bitbucket.org/2.0/repositories/{username}"

def fetch_repositories(url):
    response = requests.get(url, auth=HTTPBasicAuth(username, app_password))
    response.raise_for_status()  # Raises an error for bad status codes

arraytools

library(shiny)

# Define the UI
ui <- fluidPage(
  titlePanel("Rainbow Color Palette"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("s_value", "Saturation (s):", min = 0, max = 1, value = 1, step = 0.01),
      sliderInput("v_value", "Value (v):", min = 0, max = 1, value = 1, step = 0.01)
    ),

arraytools

library(survival)

# Function to generate survival data
generate_data <- function(sample_size, event_rate_group1, event_rate_group2) {
  group <- c(rep(1, sample_size), rep(0, sample_size))
  event_times <- c(rexp(sample_size, rate = event_rate_group1),
                   rexp(sample_size, rate = event_rate_group2))
  observed <- rep(1, sample_size * 2)  # Assuming all events are observed
  data <- data.frame(group = group, event_times = event_times, observed = observed)
  return(data)