timvisee

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

• There are always 24 hours in a day.
• February is always 28 days long.
• Any 24-hour period will always begin and end in the same day (or week, or month).

stephenhardy

-- Remove the history from 
rm -rf .git

-- recreate the repos from the current content only
git init
git add .
git commit -m "Initial commit"

-- push to the github remote repos ensuring you overwrite history
git remote add origin git@github.com:<YOUR ACCOUNT>/<YOUR REPOS>.git

cvan

Instructions

CloudFlare is an awesome reverse cache proxy and CDN that provides DNS, free HTTPS (TLS) support, best-in-class performance settings (gzip, SDCH, HTTP/2, sane Cache-Control and E-Tag headers, etc.), minification, etc.

1. Make sure you have registered a domain name.
2. Sign up for CloudFlare and create an account for your domain.
3. In your domain registrar's admin panel, point the nameservers to CloudFlare's (refer to this awesome list of links for instructions for various registrars).
4. From the CloudFlare settings for that domain, enable HTTPS/SSL and set up a Page Rule to force HTTPS redirects. (If you want to get fancy, you can also enable automatic minification for text-based assets [HTML/CSS/JS/SVG/etc.], which is a pretty cool feature if you don't want already have a build step for minification.)
5. If you

adamkucharski

# Example code for simulation recovery of infection dynamics from deaths for COVID -----------------------------------------

# Load libraries:

# install.packages("EpiNow2", repos = "https://epiforecasts.r-universe.dev")
library(EpiNow2)

# Simulate data and delays -----------------------------------------

# Simulate infections with a sharp drop

swo

import polars as pl

# Making data frames ------------------------------------------
#  with dictionary
df = pl.DataFrame({
    'name': ['foo', 'bar', 'baz'],
    'bar': [0, 1, 2],
    'qux': [0.0, 1.0, 2.0]
})

sbfnk

#' Aggregate daily forecasts to weekly forecasts
#' 
#' Aggregate daily case forecasts from regional_epinow2 to weekly forecasts.
#' 
#' @param inf List output from `EpiNow2::regional_epinow()`. No default.
#' @param week_start Week start day. Default is 1 (Monday).
#' @param summary_quantiles Quantiles for forecast summary. Default is median, plus upper and lower bound for 20\%, 50\% and 90\% forecasts. 
#' @importFrom dplyr group_by summarise filter mutate select case_when contains
#' @importFrom tidyr pivot_wider
#' @importFrom purrr map_df

zsusswein

functions {
  vector convolve(vector a, vector b) {
    int na = num_elements(a);       // Vector lengths
    int nb = num_elements(b);
    int n_zero_a = nb - 1;          // Zero padding lengths
    int n_zero_b = na - 1;

    vector[nb] b_rev = reverse(b); // The reversed b vector

    vector[na + n_zero_a] a_pad;    // Instantiate zero padded vectors

jonathonmellor

wd <- system("echo $(git rev-parse --show-toplevel)", intern = TRUE)
setwd(wd)
source(paste0(wd, "/influenza/scripts/depends.R"))

output_dir <- fs::path(paste0(wd, "/influenza/papers/univariate/outputs"))

# nhs_data path for getting population sizes:
nhs_data_path <- "" # Removed path to influenza data + population catchments per trust

mattjbayly

#' Replace multiple strings across multiple files with original values and replacement values.
#' 
#' files - A character string of file names to work through.
#' f - A character string of original values that you want to replace.
#' r - A character string of replacement values (f->r).
multi_replace <- function(files="", f="", r=""){
	file_line = data.frame() # (optional) tracking table
	#loop through each file separately
		for(j in 1:length(files)){
			nl <- suppressWarnings(readLines(files[j])) # read file line by line

medewitt

# Purpose: This is just a play model to think about time spent on large and big grants/opportunities and 
#    overall likelihood of a successful result (undefined, just general feel).
#    2080 is the hours in a year if working 40 hours (because everyone does that...)

using JuMP
using Ipopt
model = Model(Ipopt.Optimizer)

p1 = .05 # Probability of winning grant 1
sx1 = .4 # Probability of successful work (class 1)