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def get_institutions(q):
    page=1
    if q:
        url=f'https://inspirehep.net/api/institutions?q={q}&size=250&page={page}'
    else:
        url=f'https://inspirehep.net/api/institutions?size=250&page={page}' 
    r=requests.get(url)
    time.sleep(sleep)
    i=r.json()['hits']['hits']
    total=r.json().get('hits').get('total')

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def dictionarizearray(x,list_of_columns):
    """
    Assigns a dictionary key to each of the m-columns
    of the (n,m) x array

    EXAMPLE in some code which loads this function:
    >>> names=['var1','var2',...,'varN']
    >>> xnames=dictionarizearray(x,names)
    >>> xmask=x[xnames['var1']<xnames['var2']]
    #instead of

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#!/usr/bin/env bash
#Postinstall for Ubuntu 9.10 -- 14.04
#Install as root!
#after sudo su
echo "================================================================"
echo "Update and upgrade the system"
sudo apt-get install aptitude 
sudo aptitude update
sudo aptitude -y safe-upgrade
echo "================================================================"

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@article{Alloul:2013bka,
      author         = "Alloul, Adam and Christensen, Neil D. and Degrande,
                        Céline and Duhr, Claude and Fuks, Benjamin",
      title          = "{FeynRules  2.0 - A complete toolbox for tree-level
                        phenomenology}",
      journal        = "Comput. Phys. Commun.",
      volume         = "185",
      year           = "2014",
      pages          = "2250-2300",
      doi            = "10.1016/j.cpc.2014.04.012",

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import re
def split_names(s,exceptions=['Gil', 'Lew', 'Liz', 'Paz', 'Rey', 'Rio', 'Roa', 'Rua', 'Sus', 'Zea'],
                nacionality='Colombiana'):
    """
    Extract the parts of the full name `s` in the format ([] → optional):
    
    [SMALL_CONECTORS] FIRST_LAST_NAME [SMALL_CONECTORS] [SECOND_LAST_NAME] NAMES
    
    If len(s) == 3 → Not Ibero-America name is asked
    

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def hIndex(citations):
    """
    https://github.com/kamyu104/LeetCode/blob/master/Python/h-index.py
    :type citations: List[int]
    :rtype: int
    
    # Given an array of citations (each citation is a non-negative integer)
    # of a researcher, write a function to compute the researcher's h-index.
    #
    # According to the definition of h-index on Wikipedia: 

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import requests
import pandas as pd

r=requests.get('https://infogram.com/covid-2019-ins-colombia-1hnq41zg9ord63z')

head='"data":[[["ID de caso",'
tail=']]]'
js='{}{}{}'.format( head.split(':')[-1],
                    r.text.split(head)[-1].split(tail)[0],
                    tail