JCGoran

Since tmux is not available by default on Piz Daint, here's a short guide on how to install and use it there (as of 2020-01-16).

Load the necessary modules:

module load daint-gpu EasyBuild-custom/cscs

Now you can build tmux using:

eb tmux-2.9.eb -r

bricksroo

<template>
  <div id="app">
    <!-- <img src="./assets/logo.png">
    <HelloWorld msg="Welcome to Your Vue.js App"/> -->
    <div class="reveal">
      <div class="slides">
        <section>Single Horizontal Slide</section>
        <section>
          <section>Vertical Slide 1</section>
          <section>Vertical Slide 2</section>

Qix-

#!/usr/bin/env bash

# Released into the Public Domain.
#
# Original implementation in C by Brad Conte (brad@bradconte.com) <https://github.com/B-Con/crypto-algorithms>
# Ported to Bash (lol) by Josh Junon (josh@junon.me) <https://github.com/qix->
#
# Yes, it's absolutely as slow as it looks.
#
# The only external dependency it has is on a utility called `od`,

AustinRochford

klmr

A little experiment using restarts.

(And while we’re at it, let’s torture R’s syntax a little.)

![screenshot][]

In the following we will be using R’s “restarts” feature to implement the state machine that drives generators in languages such as Python. Generators allow lazily generating values on demand: a consumer invokes a generator, and consumes values as they are produced. A new value is only produced once the previous one has been consumed.

vicegd

<section>
	<h2>Tablas con información</h2>
	<table>
    	<thead><tr>
            <th>Ciudad</th>
            <th>Año de fundación</th>
            <th>Población</th>
        </tr></thead>
        <tbody><tr>
            <td>Gijón</td>

rohanp

#cython: wraparound=False, boundscheck=False, cdivision=True, overflowcheck=False
#cython: profile=False, nonecheck=False, cdivision_warnings=True

import numpy as np
cimport numpy as np
from libc.math import sqrt, exp
from sklearn.utils.extmath import fast_dot
from sklearn.metrics.pairwise import pairwise_distances

def diffusion_map(X, n_components=2, metric="euclidean"):

hrbrmstr

library(rgeos)
library(rgdal) # needs gdal > 1.11.0
library(ggplot2)

# map theme
devtools::source_gist("https://gist.github.com/hrbrmstr/33baa3a79c5cfef0f6df")

map = readOGR("readme-swiss.json", "cantons")
map_df <- fortify(map)

thigm85

require(MASS)
require(ggplot2)
require(scales)
require(gridExtra)

pca <- prcomp(iris[,-5],
              center = TRUE,
              scale. = TRUE) 

prop.pca = pca$sdev^2/sum(pca$sdev^2)

romainfrancois

#include <Rcpp.h>
using namespace Rcpp;

class B {
    public:
        B (SEXP b);
};
 
template <class T> class A {
    public: