si16-cat-walking/notebooks/stich.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af317257-97a8-46db-ac92-e06bdef22ed3",
   "metadata": {},
   "source": [
    "# Stitch\n",
    "Stitch receives as input a text as a string type, and replaces all the occurrences of a target word, with a character or a word that is repeated as many times as the length of the target. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8c280e90-22dc-4e59-90fe-3bdf97da06b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f26e528c-1d61-4796-a42a-a3d5d0edc574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# text, target, and replacement are string types\n",
    "def stich(text, target, replacement):\n",
    "    target = target.lower()\n",
    "    txt = word_tokenize(text)\n",
    "    new = []\n",
    "    \n",
    "    for w in txt:\n",
    "        if w == target:\n",
    "            w = len(w)*replacement\n",
    "            new = new + [w]\n",
    "        elif w == target[0].upper() + target[1:]:\n",
    "            w = len(w)*replacement\n",
    "            new = new + [w]\n",
    "        elif w== target.upper():\n",
    "            w = len(w)*replacement \n",
    "            new = new + [w]\n",
    "        else:\n",
    "            new = new + [w]\n",
    "    text = ' '.join(new)\n",
    "    final= text.replace(' .','.').replace(' ,',',').replace(' :',':').replace(' ;',';').replace('< ','<').replace(' >','>').replace(' / ','/').replace('& ','&')\n",
    "    return final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5443c5-e6d3-4dca-b196-7f8c0204814f",
   "metadata": {},
   "source": [
    "This function in itself could be understood as a filter to process and alter texts. By targeting specific words and stitching them, with a character or a word that is repeated as many times as the length of the target , the user of the tool can intervene inside a text. One could break down the meaning of a text or create new narrative meanings by exposing its structure, taking out or highlighting specific and meaningful words and detaching such text from its original context. \n",
    "This tool offers a broad spectrum of possibilities in which it can be used, from a very political and subversive use, to a more playful and poetic one."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e054e805-9abb-4751-af1f-0039975520d6",
   "metadata": {},
   "source": [
    "# Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32c73229-7914-4b16-8054-a74a6869ede8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'     is     '"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stich(\"life is life\",\"life\",\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9d273092-5cda-4d75-ad1c-9473bf65e69a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'**** is ****'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stich(\"life is life\",\"life\",\"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0805582-fa73-43b5-b6f9-01cfedd4003d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}