Rodolfo Carvajal rocarvaj

How to allocate resources?
Suppose you need 16 cores. Here are some use cases:

you use mpi and do not care about where those cores are distributed: --ntasks=16
you want to launch 16 independent processes (no communication): --ntasks=16
you want those cores to spread across distinct nodes: --ntasks=16 and --ntasks-per-node=1 or --ntasks=16 and --nodes=16
you want those cores to spread across distinct nodes and no interference from other jobs:--ntasks=16 --nodes=16 --exclusive
you want 16 processes to spread across 8 nodes to have two processes per node: --ntasks=16 --ntasks-per-node=2
you want 16 processes to stay on the same node: --ntasks=16 --ntasks-per-node=16
you want one process that can use 16 cores for multithreadingi: --ntasks=1 --cpus-per-task=16

Procesos

McDonald's Burger War Salvo; Is 'Made for You' the Way Folks Want to Have It?, The New York Times.
Artículo acerca de cómo McDonald's cambió su proceso de elaboración de hamburguesas a uno que prioriza la frescura y flexibilidad, llamado "Made for You".
The tube at a standstill: why TfL stopped people walking up the escalators, The Guardian. Follow up: The London escalator trial shows how our fear of change defeats logic.
El Metro de Londres decidió poner a prueba una idea que va en contra de una de las costumbres más arraigadas entre los usuarios de dicho servicio.

Revenue Management

[Airline pricing secrets: How carrier

Create script my-script.pyx
Create setup.py with

from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext

ext_modules = [Extension("hello", ["hello.pyx"])]

Pandas tips

Create constant column in dataframe
```
df['name_of_new_column'] = 10
```
http://stackoverflow.com/questions/24039023/add-column-with-constant-value-to-pandas-dataframe
Aggregate using multiple columns.

Print from line n on

tail -n +2 file.csv

From the man page:

-n, --lines=N
     output the last N lines, instead of the last 10
...

	\documentclass{article}
	\pagenumbering{gobble}
	\usepackage[a4paper,hmargin={0mm,3mm},vmargin=5mm]{geometry}
	\usepackage{tikz}
	\begin{document}
	\begin{tikzpicture}[scale=.5]
	\foreach \x in {0,...,41}
	\foreach \y in {0,...,57}
	{
	\fill[gray!75] (\x,\y) circle (0.06cm);

	# Subplots
	fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15,5))
	average_change[['primal', 'primal_dsoff', 'dual', 'dual_dsoff']].plot(ax=axes[0])
	axes[0].set_ylim(-0.0, 0.4)
	axes[0].axhline(0.0, ls='dashed')
	axes[0].xaxis.set_ticks(list(average_change.index))
	axes[0].set_title("Improvement of bounds and gap of best clone")
	axes[0].set_xlabel("Number of clones (k)")
	axes[0].set_ylabel("Average improvement")

	# Merging a pandas groupby result back into DataFrame
	# Ref: http://stackoverflow.com/a/29642002/162264

	df['maxval'] = df.groupby(by=['idn']).transform('max')

	#Print full dataframe
	def print_full(x):
	pd.set_option('display.max_rows', len(x))
	print(x)
	pd.reset_option('display.max_rows')

	#!/bin/sh
	#
	# Usage: sleeper.sh [time]]
	# default for time is 60 seconds

	# -- our name ---
	#$ -N Sleeper1
	#$ -S /bin/sh
	# Make sure that the .e and .o file arrive in the
	# working directory

	int GetPresolvedProblem(CPXENVptr env,
	string problemfilename,
	string solutionfilename,
	CPXLPptr &presolvedproblem,
	vector<double> &presolvedsolution,
	double &presolvetime)
	{

	int status;
	vector<double> originalsolution;