{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python itertools\n",
    "> Swarm Intelligence from social interaction\n",
    "\n",
    "- toc: false \n",
    "- badges: true\n",
    "- hide: true\n",
    "- comments: true\n",
    "- image: images/copied_from_nb/my_icons/static_pso.png\n",
    "- categories: [jupyter, optimisation, visualisation]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-requisits "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pass functions as variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_two(x):\n",
    "    return x+2\n",
    "\n",
    "def do_something_then_add_three(something_to_do_first, x):\n",
    "    # first call something_to_do_first with the input, then add 3\n",
    "    return something_to_do_first(x) + 3\n",
    "\n",
    "# We pass add_two (note the lack of brackets beside it)\n",
    "do_something_then_add_three(add_two, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An iterator is an object representing a stream of data. You can call `next` on an iterator to get the next value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdefghijklmnopqrstuvwxyz'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from string import ascii_lowercase\n",
    "ascii_lowercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<str_iterator at 0x7fad0bcb2d10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iter(ascii_lowercase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphabet_iterator = iter(ascii_lowercase)\n",
    "next(alphabet_iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(alphabet_iterator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_underscore(x,y): \n",
    "    return f'{x}_{y}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "alphabet_iterator = iter(ascii_lowercase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1,3,5,6]\n",
    "list(itertools.islice(my_list, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we want to create a list of tuples with ( position_in_alphabet, letter ) starting at (1, 'a') like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'a'), (2, 'b'), (3, 'c')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(1, 'a'), (2, 'b'), (3, 'c')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'a'),\n",
       " (2, 'b'),\n",
       " (3, 'c'),\n",
       " (4, 'd'),\n",
       " (5, 'e'),\n",
       " (6, 'f'),\n",
       " (7, 'g'),\n",
       " (8, 'h'),\n",
       " (9, 'i'),\n",
       " (10, 'j'),\n",
       " (11, 'k'),\n",
       " (12, 'l'),\n",
       " (13, 'm'),\n",
       " (14, 'n'),\n",
       " (15, 'o'),\n",
       " (16, 'p'),\n",
       " (17, 'q'),\n",
       " (18, 'r'),\n",
       " (19, 's'),\n",
       " (20, 't'),\n",
       " (21, 'u'),\n",
       " (22, 'v'),\n",
       " (23, 'w'),\n",
       " (24, 'x'),\n",
       " (25, 'y'),\n",
       " (26, 'z')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alphabet_tuples = []\n",
    "for i in range(len(ascii_lowercase)):\n",
    "    alphabet_tuples.append((i+1, ascii_lowercase[i]))  \n",
    "alphabet_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'a'),\n",
       " (2, 'b'),\n",
       " (3, 'c'),\n",
       " (4, 'd'),\n",
       " (5, 'e'),\n",
       " (6, 'f'),\n",
       " (7, 'g'),\n",
       " (8, 'h'),\n",
       " (9, 'i'),\n",
       " (10, 'j'),\n",
       " (11, 'k'),\n",
       " (12, 'l'),\n",
       " (13, 'm'),\n",
       " (14, 'n'),\n",
       " (15, 'o'),\n",
       " (16, 'p'),\n",
       " (17, 'q'),\n",
       " (18, 'r'),\n",
       " (19, 's'),\n",
       " (20, 't'),\n",
       " (21, 'u'),\n",
       " (22, 'v'),\n",
       " (23, 'w'),\n",
       " (24, 'x'),\n",
       " (25, 'y'),\n",
       " (26, 'z')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(count(start=1), ascii_lowercase))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as ipd\n",
    "import numpy as np\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'°º¤ø,¸¸,ø¤º°`°º¤ø,¸¸,ø¤º°`°º¤ø,¸¸,ø¤º°`'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "def ascii_wav(times=3):\n",
    "    wave = \"°º¤ø,¸¸,ø¤º°`\"\n",
    "    return ''.join(itertools.repeat(wave, times))\n",
    "\n",
    "ascii_wav()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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