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mcwitt / jekyll-post.py
Last active August 29, 2015 14:20 — forked from cscorley/jekyll.py
try:
from urllib.parse import quote # Py 3
except ImportError:
from urllib2 import quote # Py 2
import os
import sys
c = get_config()
c.NbConvertApp.export_format = 'markdown'
c.MarkdownExporter.template_file = 'jekyll-post'
const product = lists =>
lists.reduce(
(result, list) => list.reduce(
(tuples, e) => tuples.concat(result.map(t => t.concat(e))), []),
[[]]
)
console.log(product([[1,2,3], ['a','b','c'], ['foo','bar','baz']]))
;;; package -- Summary
;;; Commentary:
;;; Code:
;;; Path to additional elisp files
(add-to-list 'load-path "~/.emacs.d/elisp/")
;;; Minimal UI
(scroll-bar-mode -1)
(tool-bar-mode -1)
import scala.annotation.tailrec
sealed trait Option[+U] {
def foldLeft[B](z: B)(f: (B, U) => B): B
def map[V](f: U => V): Option[V]
def flatMap[V](f: U => Option[V]): Option[V]
}
final case object None extends Option[Nothing] {
override def foldLeft[B](z: B)(f: (B, Nothing) => B): B = z
- [Install Git for Windows](#org3d5723d)
- [Generate ssh key pair](#org9959fe7)
- [<a id="org5914e59"></a> Clone `password-store` repository from GitLab](#orgb4c5a66)
- [Install [pass-winmenu](https://github.com/geluk/pass-winmenu).](#org777c403)
- [<a id="org4c41407"></a> Set up `gpg`](#orge119e17)
- [Test decrypting a password](#orgca8e572)
- [Mark key pair as trusted](#org3cd2994)
- [(Optional) Delete expired gpg keys](#org9f729b2)

Install Git for Windows

  1. Go to https://git-scm.com/download/win. Download will start automatically.
  2. Run the downloaded installer
  3. Choose all default options

Generate ssh key pair

  1. Open the "Git Bash" application. (You can also use the Cygwin terminal if Cygwin is installed.)
if [ -z "$HM_XPROFILE_SOURCED" ]; then
. ~/.xprofile
fi
unset HM_XPROFILE_SOURCED
systemctl --user start hm-graphical-session.target
/nix/store/bcvl84gww9kz98clg9rs25y0xx64klsn-xsetroot-1.1.2/bin/xsetroot -xcf /nix/store/672s20vpzqddhdh42frvzf326qak1rlq-vanilla-dmz-0.4.4/share/icons/Vanilla-DMZ/cursors/left_ptr 48
Name: Folding@home COVID-19 datasets
Description: |
Folding@home is generating a significant amount of molecular dynamics simulation
data to identify cryptic pockets, understand antibody binding mechanisms, and identify
new small molecule therapeutics for human and SARS-CoV-2 proteins associated with COVID-19 disease.
This repository contains several major datasets from this effort.
All Folding@home COVID-19 simulation datasets are indexed in more detail at http://covid.molssi.org/simulations
Documentation: https://docs.opendata.aws/foldingathome-covid19/readme.html
Contact: https://foldingathome.org/diseases/infectious-diseases/covid-19/