NassimBentarka

#!/usr/bin/env python3       
import random
import matplotlib as plt
import time
import os
import struct

_random_source = open("/dev/random", "rb")

def random_bytes(len):

NassimBentarka

%Auteur: Nassim BENTARKA
%Ecrit le 15/05/2018
%INPUT:
%Nombre de Reynolds: R ; et Rugosité relative: K
%Le nombre d'itérations
%OUTPUT:
%Coefficient de friction lambda: F

function F=colebrook(R,K)

NassimBentarka

#!/bin/bash
#Author: Nassim BENTARKA (NBN) @nassimosaz
#You can implement this tool into your system by copying the script into ~/bin/ directory as fast-video-trim
if [ $# -ne 4 ]; then
    printf "\n${0}: usage: Fast-Video-Trim.sh [BEGIN_TIME HH:MM:SS:] [END_TIME HH:MM:SS] [INPUT_FILE] [OUTPUT_FILE]\n\n"
    exit 1
fi
TIME1=$1
TIME2=$2
IN=$3

NassimBentarka

% This technique is mainly used to optimize the running time of the algorithm, it transfoms exponential time caused by the recursion
% into polynomial time.
% Run this file on Matlab (with the csv files in the same dir) 
clear
clc
n=input("N= ");
memo=dlmread('data.csv');    %Read memoized values array
memo_index=dlmread('index.csv');    %Read indexes of the memoized values
fib(1)=1;
fib(2)=1;