{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6a1428a",
   "metadata": {},
   "source": [
    "# Topic Search\n",
    "Use Sentence transformers to embed the sentences to search for subjects within tweets. \n",
    "\n",
    "This is an experimental approach to see how themes persist in the data over a long period.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a3643ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1fa3f72b170>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import re\n",
    "import string\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "MIN_VAL = 0.3\n",
    "COUNTRY2 = \"NZ\"\n",
    "LOAD_FRESH = True # if False, use the cached version of the data (much quicker!)\n",
    "TOPIC_MODEL = False # Use the topics from the topic model run -- must also use LOAD_FRESH = True\n",
    "\n",
    "import numpy as np\n",
    "from sentence_transformers import SentenceTransformer, util\n",
    "import torch\n",
    "\n",
    "# Set the random seed to a fixed value\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6591fd89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stopwords - Use a more robust list\n",
    "\n",
    "##from nltk.corpus import stopwords\n",
    "##stopwords = stopwords.words('english')\n",
    "\n",
    "stopwords = [ \"0o\", \"0s\", \"3a\", \"3b\", \"3d\", \"6b\", \"6o\", \"a\", \"a1\", \"a2\", \"a3\", \"a4\", \"ab\", \"able\", \"about\", \"above\", \"abst\", \"ac\", \"accordance\", \"according\", \"accordingly\", \"across\", \"act\", \"actually\", \"ad\", \"added\", \"adj\", \"ae\", \"af\", \"affected\", \"affecting\", \"affects\", \"after\", \"afterwards\", \"ag\", \"again\", \"against\", \"ah\", \"ain\", \"ain't\", \"aj\", \"al\", \"all\", \"allow\", \"allows\", \"almost\", \"alone\", \"along\", \"already\", \"also\", \"although\", \"always\", \"am\", \"among\", \"amongst\", \"amoungst\", \"amount\", \"an\", \"and\", \"announce\", \"another\", \"any\", \"anybody\", \"anyhow\", \"anymore\", \"anyone\", \"anything\", \"anyway\", \"anyways\", \"anywhere\", \"ao\", \"ap\", \"apart\", \"apparently\", \"appear\", \"appreciate\", \"appropriate\", \"approximately\", \"ar\", \"are\", \"aren\", \"arent\", \"aren't\", \"arise\", \"around\", \"as\", \"a's\", \"aside\", \"ask\", \"asking\", \"associated\", \"at\", \"au\", \"auth\", \"av\", \"available\", \"aw\", \"away\", \"awfully\", \"ax\", \"ay\", \"az\", \"b\", \"b1\", \"b2\", \"b3\", \"ba\", \"back\", \"bc\", \"bd\", \"be\", \"became\", \"because\", \"become\", \"becomes\", \"becoming\", \"been\", \"before\", \"beforehand\", \"begin\", \"beginning\", \"beginnings\", \"begins\", \"behind\", \"being\", \"believe\", \"below\", \"beside\", \"besides\", \"best\", \"better\", \"between\", \"beyond\", \"bi\", \"bill\", \"biol\", \"bj\", \"bk\", \"bl\", \"bn\", \"both\", \"bottom\", \"bp\", \"br\", \"brief\", \"briefly\", \"bs\", \"bt\", \"bu\", \"but\", \"bx\", \"by\", \"c\", \"c1\", \"c2\", \"c3\", \"ca\", \"call\", \"came\", \"can\", \"cannot\", \"cant\", \"can't\", \"cause\", \"causes\", \"cc\", \"cd\", \"ce\", \"certain\", \"certainly\", \"cf\", \"cg\", \"ch\", \"changes\", \"ci\", \"cit\", \"cj\", \"cl\", \"clearly\", \"cm\", \"c'mon\", \"cn\", \"co\", \"com\", \"come\", \"comes\", \"con\", \"concerning\", \"consequently\", \"consider\", \"considering\", \"contain\", \"containing\", \"contains\", \"corresponding\", \"could\", \"couldn\", \"couldnt\", \"couldn't\", \"course\", \"cp\", \"cq\", \"cr\", \"cry\", \"cs\", \"c's\", \"ct\", \"cu\", \"currently\", \"cv\", \"cx\", \"cy\", \"cz\", \"d\", \"d2\", \"da\", \"date\", \"dc\", \"dd\", \"de\", \"definitely\", \"describe\", \"described\", \"despite\", \"detail\", \"df\", \"di\", \"did\", \"didn\", \"didn't\", \"different\", \"dj\", \"dk\", \"dl\", \"do\", \"does\", \"doesn\", \"doesn't\", \"doing\", \"don\", \"done\", \"don't\", \"down\", \"downwards\", \"dp\", \"dr\", \"ds\", \"dt\", \"du\", \"due\", \"during\", \"dx\", \"dy\", \"e\", \"e2\", \"e3\", \"ea\", \"each\", \"ec\", \"ed\", \"edu\", \"ee\", \"ef\", \"effect\", \"eg\", \"ei\", \"eight\", \"eighty\", \"either\", \"ej\", \"el\", \"eleven\", \"else\", \"elsewhere\", \"em\", \"empty\", \"en\", \"end\", \"ending\", \"enough\", \"entirely\", \"eo\", \"ep\", \"eq\", \"er\", \"es\", \"especially\", \"est\", \"et\", \"et-al\", \"etc\", \"eu\", \"ev\", \"even\", \"ever\", \"every\", \"everybody\", \"everyone\", \"everything\", \"everywhere\", \"ex\", \"exactly\", \"example\", \"except\", \"ey\", \"f\", \"f2\", \"fa\", \"far\", \"fc\", \"few\", \"ff\", \"fi\", \"fifteen\", \"fifth\", \"fify\", \"fill\", \"find\", \"fire\", \"first\", \"five\", \"fix\", \"fj\", \"fl\", \"fn\", \"fo\", \"followed\", \"following\", \"follows\", \"for\", \"former\", \"formerly\", \"forth\", \"forty\", \"found\", \"four\", \"fr\", \"from\", \"front\", \"fs\", \"ft\", \"fu\", \"full\", \"further\", \"furthermore\", \"fy\", \"g\", \"ga\", \"gave\", \"ge\", \"get\", \"gets\", \"getting\", \"gi\", \"give\", \"given\", \"gives\", \"giving\", \"gj\", \"gl\", \"go\", \"goes\", \"going\", \"gone\", \"got\", \"gotten\", \"gr\", \"greetings\", \"gs\", \"gy\", \"h\", \"h2\", \"h3\", \"had\", \"hadn\", \"hadn't\", \"happens\", \"hardly\", \"has\", \"hasn\", \"hasnt\", \"hasn't\", \"have\", \"haven\", \"haven't\", \"having\", \"he\", \"hed\", \"he'd\", \"he'll\", \"hello\", \"help\", \"hence\", \"her\", \"here\", \"hereafter\", \"hereby\", \"herein\", \"heres\", \"here's\", \"hereupon\", \"hers\", \"herself\", \"hes\", \"he's\", \"hh\", \"hi\", \"hid\", \"him\", \"himself\", \"his\", \"hither\", \"hj\", \"ho\", \"home\", \"hopefully\", \"how\", \"howbeit\", \"however\", \"how's\", \"hr\", \"hs\", \"http\", \"hu\", \"hundred\", \"hy\", \"i\", \"i2\", \"i3\", \"i4\", \"i6\", \"i7\", \"i8\", \"ia\", \"ib\", \"ibid\", \"ic\", \"id\", \"i'd\", \"ie\", \"if\", \"ig\", \"ignored\", \"ih\", \"ii\", \"ij\", \"il\", \"i'll\", \"im\", \"i'm\", \"immediate\", \"immediately\", \"importance\", \"important\", \"in\", \"inasmuch\", \"inc\", \"indeed\", \"index\", \"indicate\", \"indicated\", \"indicates\", \"information\", \"inner\", \"insofar\", \"instead\", \"interest\", \"into\", \"invention\", \"inward\", \"io\", \"ip\", \"iq\", \"ir\", \"is\", \"isn\", \"isn't\", \"it\", \"itd\", \"it'd\", \"it'll\", \"its\", \"it's\", \"itself\", \"iv\", \"i've\", \"ix\", \"iy\", \"iz\", \"j\", \"jj\", \"jr\", \"js\", \"jt\", \"ju\", \"just\", \"k\", \"ke\", \"keep\", \"keeps\", \"kept\", \"kg\", \"kj\", \"km\", \"know\", \"known\", \"knows\", \"ko\", \"l\", \"l2\", \"la\", \"largely\", \"last\", \"lately\", \"later\", \"latter\", \"latterly\", \"lb\", \"lc\", \"le\", \"least\", \"les\", \"less\", \"lest\", \"let\", \"lets\", \"let's\", \"lf\", \"like\", \"liked\", \"likely\", \"line\", \"little\", \"lj\", \"ll\", \"ll\", \"ln\", \"lo\", \"look\", \"looking\", \"looks\", \"los\", \"lr\", \"ls\", \"lt\", \"ltd\", \"m\", \"m2\", \"ma\", \"made\", \"mainly\", \"make\", \"makes\", \"many\", \"may\", \"maybe\", \"me\", \"mean\", \"means\", \"meantime\", \"meanwhile\", \"merely\", \"mg\", \"might\", \"mightn\", \"mightn't\", \"mill\", \"million\", \"mine\", \"miss\", \"ml\", \"mn\", \"mo\", \"more\", \"moreover\", \"most\", \"mostly\", \"move\", \"mr\", \"mrs\", \"ms\", \"mt\", \"mu\", \"much\", \"mug\", \"must\", \"mustn\", \"mustn't\", \"my\", \"myself\", \"n\", \"n2\", \"na\", \"name\", \"namely\", \"nay\", \"nc\", \"nd\", \"ne\", \"near\", \"nearly\", \"necessarily\", \"necessary\", \"need\", \"needn\", \"needn't\", \"needs\", \"neither\", \"never\", \"nevertheless\", \"new\", \"next\", \"ng\", \"ni\", \"nine\", \"ninety\", \"nj\", \"nl\", \"nn\", \"no\", \"nobody\", \"non\", \"none\", \"nonetheless\", \"noone\", \"nor\", \"normally\", \"nos\", \"not\", \"noted\", \"nothing\", \"novel\", \"now\", \"nowhere\", \"nr\", \"ns\", \"nt\", \"ny\", \"o\", \"oa\", \"ob\", \"obtain\", \"obtained\", \"obviously\", \"oc\", \"od\", \"of\", \"off\", \"often\", \"og\", \"oh\", \"oi\", \"oj\", \"ok\", \"okay\", \"ol\", \"old\", \"om\", \"omitted\", \"on\", \"once\", \"one\", \"ones\", \"only\", \"onto\", \"oo\", \"op\", \"oq\", \"or\", \"ord\", \"os\", \"ot\", \"other\", \"others\", \"otherwise\", \"ou\", \"ought\", \"our\", \"ours\", \"ourselves\", \"out\", \"outside\", \"over\", \"overall\", \"ow\", \"owing\", \"own\", \"ox\", \"oz\", \"p\", \"p1\", \"p2\", \"p3\", \"page\", \"pagecount\", \"pages\", \"par\", \"part\", \"particular\", \"particularly\", \"pas\", \"past\", \"pc\", \"pd\", \"pe\", \"per\", \"perhaps\", \"pf\", \"ph\", \"pi\", \"pj\", \"pk\", \"pl\", \"placed\", \"please\", \"plus\", \"pm\", \"pn\", \"po\", \"poorly\", \"possible\", \"possibly\", \"potentially\", \"pp\", \"pq\", \"pr\", \"predominantly\", \"present\", \"presumably\", \"previously\", \"primarily\", \"probably\", \"promptly\", \"proud\", \"provides\", \"ps\", \"pt\", \"pu\", \"put\", \"py\", \"q\", \"qj\", \"qu\", \"que\", \"quickly\", \"quite\", \"qv\", \"r\", \"r2\", \"ra\", \"ran\", \"rather\", \"rc\", \"rd\", \"re\", \"readily\", \"really\", \"reasonably\", \"recent\", \"recently\", \"ref\", \"refs\", \"regarding\", \"regardless\", \"regards\", \"related\", \"relatively\", \"research\", \"research-articl\", \"respectively\", \"resulted\", \"resulting\", \"results\", \"rf\", \"rh\", \"ri\", \"right\", \"rj\", \"rl\", \"rm\", \"rn\", \"ro\", \"rq\", \"rr\", \"rs\", \"rt\", \"ru\", \"run\", \"rv\", \"ry\", \"s\", \"s2\", \"sa\", \"said\", \"same\", \"saw\", \"say\", \"saying\", \"says\", \"sc\", \"sd\", \"se\", \"sec\", \"second\", \"secondly\", \"section\", \"see\", \"seeing\", \"seem\", \"seemed\", \"seeming\", \"seems\", \"seen\", \"self\", \"selves\", \"sensible\", \"sent\", \"serious\", \"seriously\", \"seven\", \"several\", \"sf\", \"shall\", \"shan\", \"shan't\", \"she\", \"shed\", \"she'd\", \"she'll\", \"shes\", \"she's\", \"should\", \"shouldn\", \"shouldn't\", \"should've\", \"show\", \"showed\", \"shown\", \"showns\", \"shows\", \"si\", \"side\", \"significant\", \"significantly\", \"similar\", \"similarly\", \"since\", \"sincere\", \"six\", \"sixty\", \"sj\", \"sl\", \"slightly\", \"sm\", \"sn\", \"so\", \"some\", \"somebody\", \"somehow\", \"someone\", \"somethan\", \"something\", \"sometime\", \"sometimes\", \"somewhat\", \"somewhere\", \"soon\", \"sorry\", \"sp\", \"specifically\", \"specified\", \"specify\", \"specifying\", \"sq\", \"sr\", \"ss\", \"st\", \"still\", \"stop\", \"strongly\", \"sub\", \"substantially\", \"successfully\", \"such\", \"sufficiently\", \"suggest\", \"sup\", \"sure\", \"sy\", \"system\", \"sz\", \"t\", \"t1\", \"t2\", \"t3\", \"take\", \"taken\", \"taking\", \"tb\", \"tc\", \"td\", \"te\", \"tell\", \"ten\", \"tends\", \"tf\", \"th\", \"than\", \"thank\", \"thanks\", \"thanx\", \"that\", \"that'll\", \"thats\", \"that's\", \"that've\", \"the\", \"their\", \"theirs\", \"them\", \"themselves\", \"then\", \"thence\", \"there\", \"thereafter\", \"thereby\", \"thered\", \"therefore\", \"therein\", \"there'll\", \"thereof\", \"therere\", \"theres\", \"there's\", \"thereto\", \"thereupon\", \"there've\", \"these\", \"they\", \"theyd\", \"they'd\", \"they'll\", \"theyre\", \"they're\", \"they've\", \"thickv\", \"thin\", \"think\", \"third\", \"this\", \"thorough\", \"thoroughly\", \"those\", \"thou\", \"though\", \"thoughh\", \"thousand\", \"three\", \"throug\", \"through\", \"throughout\", \"thru\", \"thus\", \"ti\", \"til\", \"tip\", \"tj\", \"tl\", \"tm\", \"tn\", \"to\", \"together\", \"too\", \"took\", \"top\", \"toward\", \"towards\", \"tp\", \"tq\", \"tr\", \"tried\", \"tries\", \"truly\", \"try\", \"trying\", \"ts\", \"t's\", \"tt\", \"tv\", \"twelve\", \"twenty\", \"twice\", \"two\", \"tx\", \"u\", \"u201d\", \"ue\", \"ui\", \"uj\", \"uk\", \"um\", \"un\", \"under\", \"unfortunately\", \"unless\", \"unlike\", \"unlikely\", \"until\", \"unto\", \"uo\", \"up\", \"upon\", \"ups\", \"ur\", \"us\", \"use\", \"used\", \"useful\", \"usefully\", \"usefulness\", \"uses\", \"using\", \"usually\", \"ut\", \"v\", \"va\", \"value\", \"various\", \"vd\", \"ve\", \"ve\", \"very\", \"via\", \"viz\", \"vj\", \"vo\", \"vol\", \"vols\", \"volumtype\", \"vq\", \"vs\", \"vt\", \"vu\", \"w\", \"wa\", \"want\", \"wants\", \"was\", \"wasn\", \"wasnt\", \"wasn't\", \"way\", \"we\", \"wed\", \"we'd\", \"welcome\", \"well\", \"we'll\", \"well-b\", \"went\", \"were\", \"we're\", \"weren\", \"werent\", \"weren't\", \"we've\", \"what\", \"whatever\", \"what'll\", \"whats\", \"what's\", \"when\", \"whence\", \"whenever\", \"when's\", \"where\", \"whereafter\", \"whereas\", \"whereby\", \"wherein\", \"wheres\", \"where's\", \"whereupon\", \"wherever\", \"whether\", \"which\", \"while\", \"whim\", \"whither\", \"who\", \"whod\", \"whoever\", \"whole\", \"who'll\", \"whom\", \"whomever\", \"whos\", \"who's\", \"whose\", \"why\", \"why's\", \"wi\", \"widely\", \"will\", \"willing\", \"wish\", \"with\", \"within\", \"without\", \"wo\", \"won\", \"wonder\", \"wont\", \"won't\", \"words\", \"world\", \"would\", \"wouldn\", \"wouldnt\", \"wouldn't\", \"www\", \"x\", \"x1\", \"x2\", \"x3\", \"xf\", \"xi\", \"xj\", \"xk\", \"xl\", \"xn\", \"xo\", \"xs\", \"xt\", \"xv\", \"xx\", \"y\", \"y2\", \"yes\", \"yet\", \"yj\", \"yl\", \"you\", \"youd\", \"you'd\", \"you'll\", \"your\", \"youre\", \"you're\", \"yours\", \"yourself\", \"yourselves\", \"you've\", \"yr\", \"ys\", \"yt\", \"z\", \"zero\", \"zi\", \"zz\" ]\n",
    "stopwords = [i.replace('\"',\"\").strip() for i in stopwords]\n",
    "stopwords = set( [s.lower().strip() for s in stopwords] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9f0be094",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_embeddings(text):\n",
    "    \"\"\"\n",
    "    Split texts into sentences and get embeddings for each sentence.\n",
    "    The final embeddings is the mean of all sentence embeddings.\n",
    "    :param text: str. Input text.\n",
    "    :return: np.array. Embeddings.\n",
    "    \"\"\"\n",
    "    ##return model.encode( text )\n",
    "    return np.mean(\n",
    "        model.encode(\n",
    "            list(set(re.findall('[^!?.]+[!?.]?', text)))\n",
    "        ), axis=0)\n",
    "\n",
    "def clean_txt(text, remove_punct=False):\n",
    "    # Remove URLs\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    # Remove non-ASCII characters\n",
    "    text = text.encode('ascii', 'ignore').decode()\n",
    "    # Remove punctuation - KEEP for get_embedding()\n",
    "    if remove_punct:\n",
    "        text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "    # Remove numbers -- Keep for 5G and related\n",
    "    ##text = re.sub(r'\\d+', '', text)\n",
    "    # Convert to lowercase\n",
    "    #text = text.lower()\n",
    "    text = ' '.join([word.replace('#','').replace('@','').replace('_','') for word in text.split() if ( word.lower() not in stopwords and 'http' not in word )])\n",
    "    # Remove extra whitespaces\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text\n",
    "\n",
    "import numpy as np\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "\n",
    "def euclidean_distance(u: np.ndarray, v: np.ndarray) -> float:\n",
    "    \"\"\"Calculates the Euclidean distance between two vectors\"\"\"\n",
    "    return np.linalg.norm(u - v)\n",
    "\n",
    "def manhattan_distance(u: np.ndarray, v: np.ndarray) -> float:\n",
    "    \"\"\"Calculates the Manhattan distance between two vectors\"\"\"\n",
    "    return np.sum(np.abs(u - v))\n",
    "\n",
    "def minkowski_distance(u: np.ndarray, v: np.ndarray, p: int = 2) -> float:\n",
    "    \"\"\"Calculates the Minkowski distance between two vectors\"\"\"\n",
    "    return np.sum(np.abs(u - v) ** p) ** (1 / p)\n",
    "\n",
    "def cosine_similarity(a, b):\n",
    "    \"\"\"\n",
    "    Computes the cosine similarity between two vectors.\n",
    "\n",
    "    Args:\n",
    "    a (numpy array): The first vector.\n",
    "    b (numpy array): The second vector.\n",
    "\n",
    "    Returns:\n",
    "    The cosine similarity between the two vectors.\n",
    "    \"\"\"\n",
    "    cs = np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))\n",
    "    #print( f\"CS:{round(cs,3)}\" )\n",
    "    return cs\n",
    "\n",
    "\n",
    "## FIX mycounter to take a list of all the names and then set proportions of each theme\n",
    "def mycounter( txt, theme_name, all_themes ):\n",
    "    lst = txt.split('|')\n",
    "    mycounts = {}\n",
    "    for atheme in all_themes:\n",
    "        mycounts[ atheme ] = lst.count(atheme)\n",
    "    total = float( sum( mycounts.values() ) )\n",
    "    for atheme in all_themes:\n",
    "        if total > 0:\n",
    "            mycounts[ atheme ] = mycounts[ atheme ] / total\n",
    "        else:\n",
    "            mycounts[ atheme ] = 0.0\n",
    "    return mycounts[ theme_name ]\n",
    "        \n",
    "# Calculate the cosine similarity between each sentence and each theme\n",
    "def get_themes( sentence, sentence_embedding ):\n",
    "    #print(\"----\")\n",
    "    #print(f\"Sentence: {sentence}\")\n",
    "    if len( sentence.strip() ) < 15 or len( clean_txt(sentence,remove_punct=True) ) == 0:\n",
    "        return \"\"\n",
    "    best = []\n",
    "    for j, theme in enumerate(themes):\n",
    "        se = sentence_embedding\n",
    "        te = theme_embeddings[j]\n",
    "        similarity = cosine_similarity(te,se)\n",
    "        if similarity <= MIN_VAL:\n",
    "            continue\n",
    "        if len(best) < 10 or similarity > best[0][0]:\n",
    "            val = ( similarity, theme )\n",
    "            best.append( val )\n",
    "            best = sorted( best, key=lambda x: x[0], reverse=True )\n",
    "            if len( best ) > 10:\n",
    "                best = best[0:10]\n",
    "        #print(f\"    Similarity to '{theme}': {similarity}\")\n",
    "    best = [ resolver[t[1]]  for t in best] # Make nice format\n",
    "    return '|'.join(best)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5c9f5d",
   "metadata": {},
   "source": [
    "## Each country has its own political terms set to account for scandals and leaders\n",
    "The basic themes are all the same. Some changes are made for consistency. Discussions about Canada in Canada do not count as a discussion about an ally. BUT discussions about Canada in the USA or the UK do. Similarly, the names of the major telcos in each country are added to the business category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4afd7b76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Sequence Length: 256\n"
     ]
    }
   ],
   "source": [
    "# Load pre-trained sentence transformer model\n",
    "#model = SentenceTransformer('bert-base-uncased') # better but expensive\n",
    "##model = SentenceTransformer('paraphrase-mpnet-base-v2')\n",
    "model = SentenceTransformer('all-MiniLM-L6-v2', cache_folder=\"cache\") # smaller\n",
    "print(\"Max Sequence Length:\", model.max_seq_length)\n",
    "\n",
    "# ['Allies','Business','Politics','Security','Technology', 'HuaweiPR']\n",
    "key_themes = {\n",
    "    'Business' :['economics','business', 'innovation','GDP','trade','trade war','trade deal','commercial'],\n",
    "    'Security'  :['national security','security','espionage','spying','ban','banning','surveillance','backdoor'],\n",
    "    'Technology':['technology','intellectual property','5G networks',],\n",
    "    'Allies' :['Canada','CA','UK','New Zealand','NZ','Australia','AU','United Kingdom','United States','US','USA','five eyes','alliance',\n",
    "              'Morrison','Trudeau','Trump','Arden','Boris Johnson'],\n",
    "    'Huawei PR' :['android','apple','autodesk','autonomous','battery','camera','cities','fastcharger','fitness','giveaway','headset','industrial','iot','laptop','latest','lens','market','matebook','matepad','mateview','monitor','neweggcan','notebook','oracle','patent','phone','prizes','projector','revenue','smartwatch','supported','tablet''tracker','wifi']\n",
    "\n",
    "}\n",
    "\n",
    "if COUNTRY2 == \"CA\":\n",
    "    key_themes['Politics'] = ['politics', 'leadership','Charest','PierrePoilievre','Poilievre','Trudeau','Justin Trudeau', 'Liberal','Conservative','JeanCharest','JustinTrudeau','Scheer']\n",
    "    key_themes['Business'].extend([\"Telus\",\"Bell\",'Quebecor','Shaw','Rogers',\"Ericsson\",\"Samsung\",\"Nokia\"])\n",
    "    key_themes['Allies'].remove('CA')\n",
    "    key_themes['Allies'].remove('Canada')\n",
    "    key_themes['Allies'].remove('Meng Wanzhou')\n",
    "    key_themes['Allies'].remove('Trudeau')\n",
    "\n",
    "if COUNTRY2 == \"AU\":\n",
    "    key_themes['Politics'] = ['politics', 'leadership','Labour','Liberal','Conservative','MPs','sponsor','travel','overseas travel','Scott Morrison']\n",
    "    key_themes['Business'].extend([\"Telstra\",\"Optus\",'TPG','VHA',\"Ericsson\",\"Samsung\",\"Nokia\"])\n",
    "    key_themes['Allies'].remove('Australia')\n",
    "    key_themes['Allies'].remove('AU')\n",
    "    key_themes['Allies'].remove('Morrison')\n",
    "\n",
    "if COUNTRY2 == \"US\":\n",
    "    key_themes['Politics'] = ['politics', 'leadership','Liberal','Conservative','GOP','Democrat','Republican','Democratic Party','Trump','Donald Trump','DonaldTrump']\n",
    "    key_themes['Business'].extend([\"Verizon\",\"T-Mobile\",'AT&T',\"Ericsson\",\"Samsung\",\"Nokia\"])\n",
    "    key_themes['Allies'].remove('USA')\n",
    "    key_themes['Allies'].remove('US')\n",
    "    key_themes['Allies'].remove('United States')\n",
    "    key_themes['Allies'].remove('Trump')\n",
    "\n",
    "if COUNTRY2 == \"NZ\":\n",
    "    key_themes['Politics'] = ['politics','leadership','Labour','National','Greens','Maori Party','ACT','Jacinda Arden','PM','Arden','Hipkins','NZ First']\n",
    "    key_themes['Business'].extend([\"Spark\",\"Vodaphone\",'2degrees',\"Ericsson\",\"Samsung\",\"Nokia\"])\n",
    "    key_themes['Allies'].remove('New Zealand')\n",
    "    key_themes['Allies'].remove('NZ')\n",
    "    key_themes['Allies'].remove('Arden')\n",
    "\n",
    "if COUNTRY2 == \"UK\":\n",
    "    key_themes['Politics'] = ['politics', 'leadership','Labour','Liberal','Conservative','MPs','BorisJohnson','Boris Johnson','rebellion','Tory']\n",
    "    key_themes['Business'].extend([\"BT\",\"Virgin\",'Sky','Vodaphone',\"Ericsson\",\"Samsung\",\"Nokia\"])\n",
    "    key_themes['Allies'].remove('UK')\n",
    "    key_themes['Allies'].remove('United Kingdom')\n",
    "    key_themes['Allies'].remove('Boris Johnson')\n",
    "\n",
    "# TEST - probably remove this key_themes\n",
    "##key_themes = {}\n",
    "# Canada - topics\n",
    "# Huawei as security threat\n",
    "##key_themes['Security Threat'] = ['5g','bell','canada','china','compensation','conservative','crimes','decision','enforcement','equipment','ericsson','fed','hack','justintrudeau','kovrig','leader','liberal','meng','pierrepoilievre','premier','putin','security''signal','spies','surveillance','taxpayers','telecom','telus','trudeau','uyghur','wanzhou']\n",
    "# Huawei as a legitimate company. i.e. normal PR\n",
    "##key_themes['Huawei PR'] = ['android','apple','autodesk','autonomous','battery','camera','cities','fastcharger','fitness','giveaway','headset','industrial','iot','laptop','latest','lens','market','matebook','matepad','mateview','monitor','neweggcan','notebook','oracle','patent','phone','prizes','projector','revenue','smartwatch','supported','tablet''tracker','wifi']\n",
    "\n",
    "    \n",
    "# resolver is a dictionary that takes any search term and looks up the general topic it belongs to\n",
    "resolver = {} \n",
    "for key in key_themes.keys():\n",
    "    vlst = key_themes.get( key, [] )\n",
    "    for item in vlst:\n",
    "        resolver[ item ] = key\n",
    "\n",
    "# Make a single list of thematic words to search for\n",
    "themes = []\n",
    "for lst in key_themes.values():\n",
    "    themes.extend( lst )\n",
    "\n",
    "# Get embeddings for the themes\n",
    "theme_embeddings = model.encode(themes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64e7398b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a441612f",
   "metadata": {},
   "source": [
    "### Read the topic results and lookup each tweet to determine the themes it touches on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6dc24c07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clearance Sale HUAWEI P40 Pro 5G SmartPhone 6.58inch Octa Core 8GB 256GB 50MP Cameras Bluetooth 5.1 Face Unlock WiFi6 Cell Phone|Cellphones| -... SayedSdt\n",
      "Getting sentence embeddings...\n",
      "Get sentence embeddings... done\n"
     ]
    }
   ],
   "source": [
    "if LOAD_FRESH:\n",
    "    # Open main file\n",
    "    if TOPIC_MODEL:\n",
    "        df = pd.read_csv(open(f\"../topics/topics_{COUNTRY2}.csv\",'r')) # For topic data\n",
    "    else:\n",
    "        df = pd.read_csv(open(f\"../location/full_{COUNTRY2}.csv\",'r')) # For full timeline\n",
    "        #pass # I don't want to load this by accident -- this takes forever to run!\n",
    "        \n",
    "    df['text']    = df['cleaned_text'].apply( clean_txt ) # Clean text\n",
    "    sentences_all = df['cleaned_text'].values\n",
    "    sentences     = df['text'].values\n",
    "    print( sentences[0] ) # Display first sentence for sanity check\n",
    "\n",
    "    # Get embeddings for the sentences\n",
    "    print(\"Getting sentence embeddings...\")\n",
    "    df['sentence_embedding'] = df['text'].apply( get_embeddings )\n",
    "    print(\"Get sentence embeddings... done\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2ac687f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer_central_phrase(embeddings, tokenizer, sentences):\n",
    "    # Compute pairwise cosine similarities between sentence embeddings\n",
    "    similarity_matrix = cosine_similarity(embeddings)\n",
    "\n",
    "    # Compute average cosine similarity to all other sentence embeddings for each sentence embedding\n",
    "    avg_similarities = np.mean(similarity_matrix, axis=1)\n",
    "\n",
    "    # Find the sentence embedding with the highest average cosine similarity\n",
    "    central_index = np.argmax(avg_similarities)\n",
    "    central_embedding = embeddings[central_index]\n",
    "\n",
    "    # Convert central_embedding to token IDs\n",
    "    central_ids = np.argmax(central_embedding, axis=1).tolist()\n",
    "\n",
    "    # Convert token IDs to tokens and then decode into text\n",
    "    central_tokens = tokenizer.convert_ids_to_tokens(central_ids)\n",
    "    central_phrase = tokenizer.convert_tokens_to_string(central_tokens)\n",
    "\n",
    "    # Retrieve corresponding sentence from original sentences array\n",
    "    central_sentence = sentences[central_index]\n",
    "\n",
    "    return central_phrase, central_sentence\n",
    "\n",
    "\n",
    "#infer_central_phrase()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86aab6b8",
   "metadata": {},
   "source": [
    "### Write out results to a temp file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "05832fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "if LOAD_FRESH: \n",
    "    # Use the resolver dict made above to get the number of themes in a sentence\n",
    "    df['results'] = df.apply( lambda x: get_themes( x.text, x.sentence_embedding ), axis=1 )\n",
    "    for t in key_themes.keys():\n",
    "        df[t] = df.apply(lambda x: mycounter(x.results,t,key_themes.keys()), axis=1 )\n",
    "\n",
    "    #df.drop( 'results', axis=1, inplace=True )\n",
    "    #df.drop( 'sentence_embedding', axis=1, inplace=True )\n",
    "    if TOPIC_MODEL:\n",
    "        print(\"Writing topic data\")\n",
    "        df.to_csv(open(f\"out_topic_{COUNTRY2}.csv\",'w',newline='\\n')) # Write results out to file for later processing\n",
    "    else:\n",
    "        df.to_csv(open(f\"out_full_{COUNTRY2}.csv\",'w',newline='\\n')) # Write results out to file for later processing\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c565bff",
   "metadata": {},
   "source": [
    "### Read the temporary file and prepare for the Excel pivot visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b9e20903",
   "metadata": {},
   "outputs": [],
   "source": [
    "##COUNTRY2 = \"UK\"\n",
    "\n",
    "key_themes_lst = ['Business','Politics','Security','Technology','Allies','Huawei PR'] # Main themes defined\n",
    "\n",
    "if not LOAD_FRESH:\n",
    "    if TOPIC_MODEL:\n",
    "        print(\"Reading topic model cached data\")\n",
    "        #df = pd.read_csv(open(f\"out_topic_{COUNTRY2}.csv\",'r')) # Read cached data from topic model run\n",
    "        df = pd.read_csv(open(f\"topics_{COUNTRY2}.csv\",'r')) \n",
    "        key_themes_lst.append( 'topic' ) # Some additional columns to keep\n",
    "    else:\n",
    "        print(\"Reading full time cached data\")\n",
    "        df = pd.read_csv(open(f\"out_full_{COUNTRY2}.csv\",'r')) # READ cached file!\n",
    "\n",
    "\n",
    "key_themes_lst.append( 'name' )\n",
    "key_themes_lst.append( 'date' )\n",
    "\n",
    "# Read the output file\n",
    "df = df[ key_themes_lst ] # just keep the key_theme columns (and extras)\n",
    "# Convert date column to datetime object. Set it to the start of the week period.\n",
    "df[\"week\"] = pd.to_datetime(df[\"date\"]).dt.to_period('W').dt.start_time\n",
    "df[\"month\"] = pd.to_datetime(df[\"date\"]).dt.to_period('M').dt.start_time\n",
    "\n",
    "\n",
    "# Write the results to be used in an Excel template for visualization\n",
    "df.to_csv(open(f'piv_{COUNTRY2}.csv','w',newline='\\n'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3e499243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Business', 'Politics', 'Security', 'Technology', 'Allies', 'Huawei PR',\n",
       "       'name', 'date', 'week', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f8cda3",
   "metadata": {},
   "source": [
    "# Pivot dataframe and visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6267bd92",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by='month', ascending=True)\n",
    "pf = pd.pivot_table( df, index=['month'] ,aggfunc=np.sum )\n",
    "pf = pf.reset_index()\n",
    "\n",
    "if 'topic' in pf.columns:\n",
    "    pf.drop('topic', axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1a183d",
   "metadata": {},
   "source": [
    "### Visualization of timeline using stacked area plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f66379e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "751.7499999999999\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib widget\n",
    "%matplotlib inline\n",
    "\n",
    "# CNet timeline https://www.cnet.com/news/privacy/huawei-ban-timeline-detained-cfo-makes-deal-with-us-justice-department/\n",
    "\n",
    "\n",
    "ax = pf.plot.area(x='month', stacked=True)\n",
    "#plt.stackplot(pf['month'], pf['Business'],pf['Security'],pf['Politics'],pf['Allies'],pf['Technology'],  labels=['Bus','B','C'])\n",
    "ymin, ymax = plt.ylim() \n",
    "TEN_PERC = round( ymax * 0.10)\n",
    "THREE_PERC = round( ymax * 0.03)\n",
    "TOP_LABEL = ymax - TEN_PERC\n",
    "MID_LABEL = round( ymax / 2 ) + TEN_PERC\n",
    "LOW_LABEL = ymin + THREE_PERC\n",
    "print( TOP_LABEL )\n",
    "\n",
    "# Ban dates for all the five eyes\n",
    "dd = {'2022-05-17':('CA',''),'2018-07-11':('AU',''),\"2019-05-15\":('US','r'),\"2020-07-14\":('UK','')}\n",
    "for dt, i in dd.items():\n",
    "    align_dir = \"right\" if i[1] != 'r' else 'left'\n",
    "    ax.axvline( pd.to_datetime(dt), color='black', linestyle=':', lw=1, alpha=0.5) # Canada\n",
    "    ax.annotate(f' {i[0]} \\n ban ', xy=(dt, TOP_LABEL ) ,horizontalalignment=align_dir, fontsize=10 )\n",
    "\n",
    "# Australia events\n",
    "if COUNTRY2 in ['AU','CA','US','UK']: \n",
    "    ax.axvline( pd.to_datetime('2021-09-24'), color='black', linestyle=':', lw=1) # Canada 2Michaels returned\n",
    "    ax.annotate(' Two\\n Michaels\\n Return', xy=('2021-09-24', MID_LABEL)   )\n",
    "    ax.axvline( pd.to_datetime('2018-12-01'), color='white', linestyle='--', lw=1) # Canada Meng Wanzhou arrested\n",
    "    ax.annotate(' Huawei\\n CFO\\n arrested', xy=('2018-12-01', LOW_LABEL), color='white', fontweight='bold'    )\n",
    "\n",
    "# UK events\n",
    "if COUNTRY2 in ['UK','CA','US','NZ']: \n",
    "    ax.axvline( pd.to_datetime('2020-01-28'), color='red', linestyle=':', lw=1 , alpha=0.5) # UK Allows Huawei to build network\n",
    "    ax.annotate(' UK to \\n allow \\n Huawei ', xy=('2020-01-28', MID_LABEL), color='red', horizontalalignment='right'    )\n",
    "    # Jan. 29, 2020  - Europe allows Huawei for 5G through security guidelines\n",
    "    ax.axvline( pd.to_datetime('2020-01-29'), color='red', linestyle=':', lw=1, alpha=0.5) # Jan. 29, 2020  - Europe allows Huawei for 5G through security guidelines\n",
    "    ax.annotate(' EU to \\n allow \\n Huawei ', xy=('2020-01-29', TOP_LABEL*.85), color='red' ,horizontalalignment='right'   )\n",
    "    # Feb 11, 2020 - US finds Huawei has backdoor access to mobile networks globally, report says\n",
    "    ax.axvline( pd.to_datetime('2020-02-11'), color='red', linestyle=':', lw=1, alpha=0.5) # Feb 11, 2020 - US finds Huawei has backdoor access to mobile networks globally, report says\n",
    "    ax.annotate(' Huawei \\n backdoors \\n found ', xy=('2020-02-11', MID_LABEL), color='red'  )\n",
    "    if COUNTRY2 != 'NZ':\n",
    "        ax.axvline( pd.to_datetime('2020-03-10'), color='white', linestyle='--', lw=1) # UK MP rebellion\n",
    "        ax.annotate(' UK\\n MPs \\n lose \\n vote', xy=('2020-03-10', LOW_LABEL), color='white', fontweight='bold'   )\n",
    "\n",
    "# US events    \n",
    "if COUNTRY2 in ['US','CA','AU']:\n",
    "    # Nov 22, 2019 - Huawei banned from US subsidies\n",
    "    #ax.axvline( pd.to_datetime('2019-11-22'), color='red', linestyle=':', lw=1, alpha=0.5) # Jan. 29, 2020  - Europe allows Huawei for 5G through security guidelines\n",
    "    #ax.annotate(' US \\n subsidies \\n ban ', xy=('2019-11-22', TOP_LABEL*.85), color='red' ,horizontalalignment='right'   )\n",
    "    ax.fill_between(['2019-10-16', '2020-02-11'], [ymax,ymax], color='gray', alpha=0.2) # ratcheting up of tensions\n",
    "    ax.axvline( pd.to_datetime('2020-12-04'), color='red', linestyle=':', lw=1) # US deal to release Meng?\n",
    "    ax.annotate(' Trump \\n alludes to \\n release of \\n Huawei \\n CFO ', xy=('2020-12-04', MID_LABEL), color='red' , horizontalalignment='left'  )\n",
    "\n",
    "# AU events    \n",
    "if COUNTRY2 in [\"AU\"]:\n",
    "    ax.axvline( pd.to_datetime('2018-08-27'), color='red', linestyle=':', lw=1) # Turnbull resigns\n",
    "    ax.annotate(' Turnbull\\n resigns ', xy=('2018-08-24', (TOP_LABEL*0.9)), color='red'   )\n",
    "    ax.axvline( pd.to_datetime('2018-06-06'), color='red', linestyle=':', lw=1) # FB shared data with Huawei scandal\n",
    "    ax.annotate(' FB \\n shares \\n data ', xy=('2018-06-06', MID_LABEL), color='red', horizontalalignment='right'   )\n",
    "    # UK announces to allow Huawei into network before backpedalling\n",
    "    ax.axvline( pd.to_datetime('2020-02-28'), color='red', linestyle=':', lw=1) # UK Allows Huawei to build network\n",
    "    ax.annotate(' UK \\n allows \\n Huawei ', xy=('2020-02-28', MID_LABEL), color='red' , horizontalalignment='right'  )\n",
    "\n",
    "# NZ events    \n",
    "if COUNTRY2 == \"NZ\":\n",
    "    # https://www.sparknz.co.nz/news/GCSB_declines_Spark_proposal_Huawei/\n",
    "    ax.axvline( pd.to_datetime('2018-11-28'), color='white', linestyle='--', lw=1, alpha=1) # GCSB blocks use of Huawei by Spark\n",
    "    ax.annotate(' GCSB \\n block ', xy=('2018-11-28', LOW_LABEL*.5), color='white', horizontalalignment='right',fontsize=10, weight='bold'  )\n",
    "    # https://www.stuff.co.nz/business/112486350/huawei-nz-we-know-the-rumours-gossip-and-innuendo-about-us?rm=m\n",
    "    ax.axvline( pd.to_datetime('2019-05-06'), color='white', linestyle='--', lw=1) # Huawei PR\n",
    "    ax.annotate(' Huawei \\n PR ', xy=('2019-05-06', 40), color='white', weight='bold'   )\n",
    "    # Reports of NZ tensions with China\n",
    "    ax.axvline( pd.to_datetime('2019-02-12'), color='white', linestyle='--', lw=1) # \n",
    "    ax.annotate(' NZ-China  \\n tension  ', \n",
    "                bbox=dict(boxstyle=\"round\", fc=\"1\"), # foreground color?\n",
    "                xytext=('2019-01-01', MID_LABEL*1.25), \n",
    "                color='black', horizontalalignment='right', \n",
    "                xy=('2019-02-12', 400), \n",
    "                arrowprops=dict(\n",
    "                arrowstyle=\"->\",\n",
    "                            #connectionstyle=\"bar\",\n",
    "                    connectionstyle=\"arc\",\n",
    "                            #ec=\"k\",\n",
    "                            shrinkA=0, shrinkB=5\n",
    "                ) \n",
    "               )\n",
    "\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plt.title(f'Themes about Huawei discussed over time ({COUNTRY2})')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Theme discussed')\n",
    "\n",
    "\n",
    "import matplotlib\n",
    "fig = matplotlib.pyplot.gcf()\n",
    "fig.set_size_inches(7.5, 5)\n",
    "\n",
    "plt.savefig(f'stacked_area_{COUNTRY2}.pdf', bbox_inches='tight')  # saves the current figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f5bb5b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55981559",
   "metadata": {},
   "outputs": [],
   "source": [
    "set( df.topic )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32768b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This only works when the data has topics from the BERTopic model\n",
    "if TOPIC_MODEL:\n",
    "    print( \"Running!\" )\n",
    "    # Now lets see overlap between categories and \n",
    "    key_themes_lst = ['Allies','Business','Politics','Security','Technology']\n",
    "    df = df.sort_values(by='month', ascending=True)\n",
    "    sdf = pd.pivot_table( df, index=['topic','name'] ,aggfunc=np.sum )\n",
    "    sdf['row_sum'] = sdf[key_themes_lst].sum(axis=1)\n",
    "    #sdf.drop('index', axis=1, inplace=True)\n",
    "    #sdf.drop('topic', axis=1, inplace=True)\n",
    "    sdf.drop('row_sum', axis=1, inplace=True)\n",
    "    sdf[key_themes_lst] = sdf[key_themes_lst].div(sdf[key_themes_lst].sum(axis=1), axis=0) # normalize\n",
    "\n",
    "    sdf[key_themes_lst] = sdf[key_themes_lst].round(decimals=2\n",
    "                                                )\n",
    "    #sdf.style.set_precision(2).background_gradient(cmap='RdYlGn',axis=1,vmin=0, vmax=1)\n",
    "    sdf_styled = sdf.style.set_precision(2).background_gradient(cmap='Blues',axis=1,vmin=0, vmax=1)\n",
    "    sdf_styled\n",
    "\n",
    "    # Write out the styled table to disk\n",
    "    #!python -m pip install dataframe_image\n",
    "    import dataframe_image as dfi\n",
    "    dfi.export(sdf_styled, f'{COUNTRY2}_theme_table_styled.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "70658be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf_styled"
   ]
  }
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