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{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "N3LLrMseMqfy"
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"source": [
"!pip install stanza rss-parser"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HX4xSRMYM7I2",
"outputId": "be0177f0-4d5b-4401-92a7-026779f3a903"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: stanza in /usr/local/lib/python3.7/dist-packages (1.4.2)\n",
"Requirement already satisfied: rss-parser in /usr/local/lib/python3.7/dist-packages (0.2.4)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from stanza) (1.15.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from stanza) (2.28.1)\n",
"Requirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from stanza) (1.12.1+cu113)\n",
"Requirement already satisfied: emoji in /usr/local/lib/python3.7/dist-packages (from stanza) (2.1.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from stanza) (1.21.6)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from stanza) (4.64.1)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from stanza) (3.17.3)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3.0->stanza) (4.1.1)\n",
"Requirement already satisfied: lxml>=4.6.5 in /usr/local/lib/python3.7/dist-packages (from rss-parser) (4.9.1)\n",
"Requirement already satisfied: pytest<8.0.0,>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from rss-parser) (7.2.0)\n",
"Requirement already satisfied: pydantic>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from rss-parser) (1.10.2)\n",
"Requirement already satisfied: bs4>=0.0.1 in /usr/local/lib/python3.7/dist-packages (from rss-parser) (0.0.1)\n",
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from bs4>=0.0.1->rss-parser) (4.6.3)\n",
"Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (1.0.0)\n",
"Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (1.0.0)\n",
"Requirement already satisfied: tomli>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (2.0.1)\n",
"Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (22.1.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (21.3)\n",
"Requirement already satisfied: iniconfig in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (1.1.1)\n",
"Requirement already satisfied: importlib-metadata>=0.12 in /usr/local/lib/python3.7/dist-packages (from pytest<8.0.0,>=7.1.2->rss-parser) (4.13.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12->pytest<8.0.0,>=7.1.2->rss-parser) (3.9.0)\n",
"Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2.1.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2.10)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2022.9.24)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->pytest<8.0.0,>=7.1.2->rss-parser) (3.0.9)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import json\n",
"\n",
"with open('/content/icons.json', 'r') as fd:\n",
" icons = json.load(fd)\n",
"\n",
"icons = {name: icons[name] for name in icons if 'solid' in icons[name]['styles']}\n",
"\n",
"from matplotlib.font_manager import FontProperties\n",
"fp = FontProperties(fname='/content/Font Awesome 6 Free-Solid-900.otf')\n",
"\n",
"plt.figure(figsize=(10, 10))\n",
"plt.text(.5, .5, chr(int(icons[\"thumbs-up\"]['unicode'], 16)), fontproperties=fp, size=100, ha='center', va='center');"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 596
},
"id": "ExYW95v5TGE3",
"outputId": "720b4603-7742-4f77-ca17-eae388bba83b"
},
"execution_count": 3,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"!pip install transformers"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4uKevvbRfBbw",
"outputId": "75d71d50-a9c3-4c56-b228-ff1b5e644552"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.23.1)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.1)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n",
"Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.28.1)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n",
"Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.1.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModel\n",
"tokenizer = AutoTokenizer.from_pretrained(\"cointegrated/LaBSE-en-ru\")\n",
"model = AutoModel.from_pretrained(\"cointegrated/LaBSE-en-ru\")\n",
"sentences = [\"Hello World\", \"Привет Мир\"]\n",
"encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt')\n",
"with torch.no_grad():\n",
" model_output = model(**encoded_input)\n",
"embeddings = model_output.pooler_output\n",
"embeddings = torch.nn.functional.normalize(embeddings)\n",
"print(torch.dot(embeddings[0], embeddings[1]))\n",
"\n",
"def embed(text):\n",
" encoded_input = tokenizer([text], padding=True, truncation=True, max_length=64, return_tensors='pt')\n",
" with torch.no_grad():\n",
" model_output = model(**encoded_input)\n",
" embeddings = model_output.pooler_output\n",
" embeddings = torch.nn.functional.normalize(embeddings)\n",
" return embeddings[0].cpu().numpy()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 173,
"referenced_widgets": [
"8465293723d5451f96e36a932a9d87dc",
"cfa58ba6958a49e8892202686efa5c75",
"e99d42402b8246718e92f788f7ed08e7",
"c5040d6ab0b840048e493a8d31709ebf",
"199dc95d49454ef5bb7ba14ebcd165a3",
"d19264152df64497b6c0f5545788c2f8",
"6c84b6fd00e64cd0bf9c9929407c4765",
"92943f2dd49349f7acb50fcaf591cb9f",
"552f0cfd77024ebdb446192adcf1120f",
"b1f975a419d14268bf1e7d7040cbb195",
"d40798a5013c48c085b82e25e9b2314f"
]
},
"id": "P2xZTY5Ie26X",
"outputId": "0136bf3a-bfcb-4f49-b233-c5c22af6c154"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Moving 0 files to the new cache system\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"0it [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8465293723d5451f96e36a932a9d87dc"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of the model checkpoint at cointegrated/LaBSE-en-ru were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.bias']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"tensor(0.9229)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from rss_parser import Parser\n",
"from requests import get\n",
"\n",
"xml = get('https://lenta.ru/rss')\n",
"\n",
"parser = Parser(xml=xml.content, limit=10)\n",
"feed = parser.parse()\n",
"\n",
"for item in feed.feed:\n",
" print(item.title)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ibxlmNnpNFTe",
"outputId": "318c70ae-d89f-41a7-c0dc-f874841c0168"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Популярный комик раскрыл отношение к эмигрировавшим россиянам\n",
"Трусова рассказала о расставании с Тутберидзе\n",
"Клуб РПЛ задумался о возвращении трижды уволенного тренера\n",
"Собчак оценила свое положение словами «да, я в беде»\n",
"Экс-участница «Ранеток» раскрыла диагноз дочери\n",
"В ЛНР заявили об отказе иностранных наемников воевать на стороне Украины\n",
"Латвия снесла часть мемориала «Курган Дружбы» на границе с Россией\n",
"Подозреваемого в нападении на РОВД в Херсоне задержали\n",
"Сенатор предложил потребовать от Лондона заплатить за подрыв «Северных потоков»\n",
"Самый старый узник Гуантанамо вернулся на родину\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import stanza\n",
"pipeline = stanza.Pipeline('ru', processors=['lemma', 'pos', 'tokenize', 'depparse'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 364,
"referenced_widgets": [
"51e95f402311485a8e63028c875c78be",
"f2e7252ab1ec4f5786f9d8e5a30140a1",
"ac3595e9e377452080649898771423b4",
"135cc30437944a92907b15dcbf962e5e",
"2438d1e08d5645f8af905ff298fc83a4",
"856b72440d8d4ea2ac9a09dd968f3018",
"0a0c9900e1324855babcdff0b0419235",
"8340bafff8b44c4cb4d244f5d2a54ee8",
"32e71b7b34d047a98bddecf4f3459004",
"12e0ee1bab18479f9bbb4973e0d209c1",
"c398c07b56734c62b30cb34c0df98794"
]
},
"id": "dxCt6Y8aObMC",
"outputId": "89eda8a2-6c07-481d-9594-7458f0f9c5a6"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:stanza:Checking for updates to resources.json in case models have been updated. Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.4.1.json: 0%| …"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "51e95f402311485a8e63028c875c78be"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:stanza:Loading these models for language: ru (Russian):\n",
"=========================\n",
"| Processor | Package |\n",
"-------------------------\n",
"| tokenize | syntagrus |\n",
"| pos | syntagrus |\n",
"| lemma | syntagrus |\n",
"| depparse | syntagrus |\n",
"=========================\n",
"\n",
"INFO:stanza:Use device: cpu\n",
"INFO:stanza:Loading: tokenize\n",
"INFO:stanza:Loading: pos\n",
"INFO:stanza:Loading: lemma\n",
"INFO:stanza:Loading: depparse\n",
"INFO:stanza:Done loading processors!\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pipeline(feed.feed[0].title)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uf6bOKf8PG13",
"outputId": "a430503f-d047-4ae1-e072-80156d1733a8"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[\n",
" [\n",
" {\n",
" \"id\": 1,\n",
" \"text\": \"Популярный\",\n",
" \"lemma\": \"популярный\",\n",
" \"upos\": \"ADJ\",\n",
" \"feats\": \"Case=Nom|Degree=Pos|Gender=Masc|Number=Sing\",\n",
" \"head\": 2,\n",
" \"deprel\": \"amod\",\n",
" \"start_char\": 0,\n",
" \"end_char\": 10\n",
" },\n",
" {\n",
" \"id\": 2,\n",
" \"text\": \"комик\",\n",
" \"lemma\": \"комик\",\n",
" \"upos\": \"NOUN\",\n",
" \"feats\": \"Animacy=Anim|Case=Nom|Gender=Masc|Number=Sing\",\n",
" \"head\": 3,\n",
" \"deprel\": \"nsubj\",\n",
" \"start_char\": 11,\n",
" \"end_char\": 16\n",
" },\n",
" {\n",
" \"id\": 3,\n",
" \"text\": \"раскрыл\",\n",
" \"lemma\": \"раскрыть\",\n",
" \"upos\": \"VERB\",\n",
" \"feats\": \"Aspect=Perf|Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act\",\n",
" \"head\": 0,\n",
" \"deprel\": \"root\",\n",
" \"start_char\": 17,\n",
" \"end_char\": 24\n",
" },\n",
" {\n",
" \"id\": 4,\n",
" \"text\": \"отношение\",\n",
" \"lemma\": \"отношение\",\n",
" \"upos\": \"NOUN\",\n",
" \"feats\": \"Animacy=Inan|Case=Acc|Gender=Neut|Number=Sing\",\n",
" \"head\": 3,\n",
" \"deprel\": \"obj\",\n",
" \"start_char\": 25,\n",
" \"end_char\": 34\n",
" },\n",
" {\n",
" \"id\": 5,\n",
" \"text\": \"к\",\n",
" \"lemma\": \"к\",\n",
" \"upos\": \"ADP\",\n",
" \"head\": 7,\n",
" \"deprel\": \"case\",\n",
" \"start_char\": 35,\n",
" \"end_char\": 36\n",
" },\n",
" {\n",
" \"id\": 6,\n",
" \"text\": \"эмигрировавшим\",\n",
" \"lemma\": \"эмигрировать\",\n",
" \"upos\": \"VERB\",\n",
" \"feats\": \"Aspect=Perf|Case=Dat|Number=Plur|Tense=Past|VerbForm=Part|Voice=Act\",\n",
" \"head\": 7,\n",
" \"deprel\": \"amod\",\n",
" \"start_char\": 37,\n",
" \"end_char\": 51\n",
" },\n",
" {\n",
" \"id\": 7,\n",
" \"text\": \"россиянам\",\n",
" \"lemma\": \"россиянин\",\n",
" \"upos\": \"NOUN\",\n",
" \"feats\": \"Animacy=Anim|Case=Dat|Gender=Masc|Number=Plur\",\n",
" \"head\": 4,\n",
" \"deprel\": \"nmod\",\n",
" \"start_char\": 52,\n",
" \"end_char\": 61\n",
" }\n",
" ]\n",
"]"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"from tqdm import tqdm\n",
"\n",
"names = [key for key in sorted(icons.keys())]\n",
"names_embeddings = np.row_stack([\n",
" embed(key) for key in tqdm(names)\n",
"]).reshape(len(names), -1)\n",
"names_embeddings.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TfuAr9jXwrM8",
"outputId": "c0e5ce62-3f37-4ed9-f607-0a7d107818c5"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 1388/1388 [01:51<00:00, 12.43it/s]\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1388, 768)"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.neighbors import NearestNeighbors\n",
"\n",
"base = NearestNeighbors(n_neighbors=1).fit(names_embeddings)\n",
"test_index = base.kneighbors([embed('Самолет')], return_distance=False)[0][0]\n",
"\n",
"names[test_index]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "IqUMkGo_xbE0",
"outputId": "0f7190ba-de4b-4c44-9337-b6f56907ff5b"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'plane'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"def get_image_set(text):\n",
" parts = pipeline(text).sentences[0].words\n",
" parts = [\n",
" (part.text, part.upos) for part in parts if part.upos in ['NOUN', 'VERB', 'ADJ', 'PROPN']\n",
" ]\n",
" draw = []\n",
" for lemma, upos in parts:\n",
" index = base.kneighbors([embed(lemma)], return_distance=False)[0][0]\n",
" icon = names[index]\n",
" lemma = (lemma, icon)\n",
" if upos == 'VERB':\n",
" draw.append(['action', [lemma]])\n",
" else:\n",
" if len(draw):\n",
" if draw[-1][0] != 'action':\n",
" draw[-1][-1].append(lemma)\n",
" continue\n",
" draw.append(['object', [lemma]])\n",
" return draw\n",
"\n",
"test_set = get_image_set(\"Вася пошел на злую рыбалку\")\n",
"test_set"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ve0Wt1JezsBf",
"outputId": "66954a70-10ca-4674-9bb9-efd28f92304f"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[['object', [('Вася', 'cow')]],\n",
" ['action', [('пошел', 'jedi')]],\n",
" ['object', [('злую', 'smog'), ('рыбалку', 'fish')]]]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"def draw_text(text):\n",
" image_set = get_image_set(text)\n",
" plt.title(text)\n",
" x = 0.1\n",
" for sort, objects in image_set:\n",
" if sort == 'action':\n",
" plt.arrow(x, 0.8, 0.1, 0, fc=\"k\", ec=\"k\", head_width=0.01, head_length=0.05);\n",
" plt.text(x + 0.05, 0.8 - 0.1, objects[0][0], ha='center', va='center');\n",
" plt.text(\n",
" x + 0.05, 0.8 - 0.2,\n",
" chr(int(icons[objects[0][1]]['unicode'], 16)),\n",
" fontproperties=fp, size=20,\n",
" ha='center', va='center'\n",
" );\n",
" else:\n",
" for index, y in enumerate(range(len(objects))):\n",
" plt.text(x + 0.05, 0.8 - y / 5 - 0.1, objects[index][0], ha='center', va='center');\n",
" plt.text(\n",
" x + 0.05, 0.8 - y / 5,\n",
" chr(int(icons[objects[index][1]]['unicode'], 16)),\n",
" fontproperties=fp, size=20,\n",
" ha='center', va='center'\n",
" );\n",
" x += 0.25\n",
" plt.axis('off')\n",
" plt.tight_layout();\n",
" plt.show()\n",
"\n",
"draw_text(\"Вася пошел на злую рыбалку\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "PiGNv5so5HMb",
"outputId": "d03d609e-01b6-4c30-af97-43f47d8a2623"
},
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"for item in feed.feed:\n",
" print(draw_text(item.title));"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "GKjwFSIrDq9E",
"outputId": "044f4966-96d4-42d1-fdfc-61dd5e7bb663"
},
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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LzaUvkppAi3emn0vJ60Dp54Mja7VQ5fhujIhz83D7RUTdG1kkHQN8JCKaOZnoN5R+4rNfRGxSdiwVkiZFxPCy4+jLmrnj2Mzax0HAnZ094+5LcnP115h996u1Nz9lsZuciM36iFyTFI1vTCzTR6nzMy/S76Xfr9NvFkn7ky43nR8RN7cwNushbrXoPjdNm5mZlchvXzIzMyuRE7GZmVmJnIjNzMxK5ERsZmZWIidiMzOzEjkRm5mZlciJ2MzMrEROxGZmZiVyIjYzMyuRE7GZmVmJnIjNzMxK5ERsZmZWIidiMzOzEjkRm5mZlciJ2MzMrEROxGZmZiVyIjYzMyuRE7GZmVmJnIjNzMxK5ERsZmZWIidiMzOzEjkRm5mZlciJ2MzMrEROxGZmZiVyIjYzMyuRE7GZmVmJnIjNzMxKNH/ZAZjZvEnSIGA9YB1gJvAqcCfweEREmbGZtRN5fzCzVpO0EXAxsHyN3vcBIyPitd6Nyqw9ORGbWUtJWgu4h8YtbqtHxMO9FJJZW3PTtJm12sGkY8v7wAHAn3P5yqRm6uechM1mcyK2WSStCJwJbAYMauGkZwA3AftFxNMtnK61p0pz9MSIOKtQ/hIwvoR4zNqa75q2orOArWltEiZP79OkJG/zvsfy/09IOlbSkqVGY9bmfI3YZpE0nc4l4ZuAG5sYbiSwOTAjIj7Q+cisL5G0MumGrIVz0bvAZcA5wA0R8V5ZsZm1Iydim0VSvY1hOlArgR4XEaObmO5o4FiAiFBX47O+Q9IupLumqz0BHBURV/RySGZty03T1pFjgOWA1zsaUNIqkh6UNLyng7L2JWlp4JQ6vVcBLpf0jV4Myayt+WYta2Qq8IuIeF3SmcB3qgeQNBh4HFimUPwfSQDvAFv0RqB9XW7OPY10wvMC8DLpARivAq9V/2/z5t0DgWVz91nANYCA4cBRwDDgZEnnRMTbpURo1kaciK2Rc3ISHgL8EjgMGFAcICKmSTqdORPxKsC2wPWkG3c+20vxtoykBYG3SgzhfdLTqESN/VTSO6TE/AqwbkRM693wGlon/78/IvYr9pD0LHAhsCCwGnB3L8dm1naciK2e94HTJH0Y+EFE7CXpcmDn6gEj4mRJHwBOINWALgKmAV+LiJdz7bhPiYi3JX0JWLGXZvlxYB9S8g3SScCbpBrya8CU/PcCs2vKlVryjF6KsVmVn6itIem7pBO6/+ay1QvD+claZvhmLSuoulnryoj4kqQVgA9HxHWSNgBuLwwz62YtSUsATwIDSTd2HRwRv8n9RuObtTqUf+bzRkS8U3Ys3SFpDVJNt3KD30zgWlKT9Hq5bGJEfLyE8Mzajm/Wsnp+LmkBUhPis5KWjYjxwL/qDL8m6YENHyDVpof0Tpjzjoh4sa8nYYCIeBD4DPBoLhoAbMPsJPw+sFcJoZm1JSdiq+cRYBTwcP67R9JiubuWUaQm1f2B64BR+UYu64ci4mZgDWAH4A/AG4Xe89EH7xsw6ylumrZZqpqm3wEWqBrkHVKNt9K8XGyaHgi8HxEz8+cFKrU7N01bvofgi8BJpLunA1giIl4uMy6zduCbtazoXdI1Xpg7CdcqG5mT7CzFG7MK3SPz/3a7qch6SURMB8ZKeg+4nHQytxLpZ1pm/ZoTsRXdQud+97t5/mvWzZ0Lx/oiSTuRWkAeAv5CulTxEqkmvH9h0Gd6PTizNuSmaZtF0kqknx9twuyacSvMAMaRfs70VAuna21I0iXU+JlblSsj4ku9EY9Zu3MiNrOWyq/T/AnphqyhNQb5PfDNiJjaq4GZtSknYjPrEfkGvk2AEcBCpAeSjMs/bzKzzInYzMysRP4dsZmZWYmciM3MzErkRNyPSbpR0qOSJkh6WtKYsmMyM+tvnIj7NwG7RcQI4JiygzEz64+ciPu3+YE5XjAvaTtJ4yXdK+mfkpaSNDjXmidImiFpYu5eV9K5kv4j6QFJ90taM0/nRknr5u4TJE0tzOO3kh7O05jZq0vcBEl75WW5T9L5eRl3LPR/QNLw3L2HpDvyspwhaUAu/6yke/I0riuMO1rSs3n4qYV19EdJd0t6UNIBvbvEnSNppKSrCp8nSRpWbxmq10UT29OOtedsNm/yk7X6t0VI7w0uugXYMCJC0n7AURHxHdJPUJA0CdgiIqbkzwBHRsRluWl7S+CBysTyq/22KnxeC9gIWCMi3i8m6HaQX+E3CtgoIqbkF138vM6wqwG7ABtHxLuSfg3sLulvwO+AzSLiP3kaFQOAUyLi55JuLJTvGxGv5Bdl3Cnp8jZ+DvP7zH7eeNFcy0A62Z9jXUTENBpvT2b9ihNx/7Yk8GJV2XLAJZKWBgYB/2liOj+V9GPSCyE2qOr3A+BE4KL8eWae7iDSSyTazZbApZXEkBMLpGUclYdZJf/fCliHlHQABpPW54bAzRHxn8o0CtMfDDxfY76HStohdy8PfJj2fQ7zM8BqxRd7ZLWWYQnqr4t6Kuv6ZeCgiPh3C2M3aztumu6nJC0ORES8VtXrl8CYiFgL+Dq1X/5Q7ciI+DDwQ+C4QvlwYM2I+EulICIeAsYCL0qaQEpMfcGRETEiX09/IpcJ+H2lPCI+WnkbVQPLAM8VCySNBLYGPhURnwDupbn1XoqIeJL0asN78ne4DOnFHq1ahiPzer4IGN3tgM3anBNxPyRpAeBHwJ9q9B4CPJu7v9rJSb8BDCt8Pjb/VXsdOD0fbKubxst2PbBTPlGhqlm52nXAjrn5HUmL5cc73g5slp/dPWsakoYBmwLjq6YzBHg1It6W9DFSjbqtRcSoiFg9f4fPkZ6cVWsZaq6LJr1Majkxm6e5abp/OoJ04Dy4Rr/RwKWSXiUlpZWamF6lKTGA/Qrlz+QXxM8iaWPgM8A2XYi7x0XEg5J+BNyUbyS7t8GwD+XlvlbSfKTXSH4jIm7PNytdkctfBD5Nuv4+OiKqm6avAQ6U9DDwKCl59TXXAF+pXoaIeKnOumjkeEmHkS51fL0HYzZrC37EpZmZWYncNG1mZlYiJ2IzM7MSORGbmZmVyDdrmXWBpEWBvZjzLnFIN2yNA26JiPd7PTAz63N8s5ZZF0i6gfTb2XpOjoj/7aVwzKwPcyI26wJJ9R7zWDQiIu7rjXjMrO/yNWKzrqkk4eMiQpU/4COk31MDfLuc0MysL3EiNmuhiHgM+HP+uKukD9UbVsmmkpbpnejMrB05EZu13s/y/0HUfnpZxUnAzcC1PR5RD5A0XOlVmTMkRQv+npN0SNnLZdbbnIjNmiTpA5L2lTS6UDy8xqC3Anfk7oPyawGrp/UV4Mj8cUor4+xFZ5HeQDWwRdNbGvhFfgyqWb/hRGzWvD1Iyaf4IouvViVmIt0BeUr+OCyPN0t+J/NZ+eNrwNd6IthesFGNsoeAC4D3ulBWsUmrAjTrC5yIzZq3fJ3yY6uTMXAFMCl3H6Y533h/BrAg6aauXSPiCfqm02qUbRMRewIXdqEM4AVSgjbrN5yIzVrj2OL1TtKDPYbnfquT3jhVsUH+f0ZEXNOLMbba0cAzxYKIeDp3TupsWfb9iHgWs37EidisdxTfMVzZ7/5bRiAtFMz+qVYrp2nWr/gRl2bN686J6xZVzdPzgtFUNddLWj4iJgMrdrYsO17SNRHxQs+FbdZe/GQtsyZI2p10bXehFk72uIgY3cLp9SpJM5n75OQB4B5gV2bfTd1sWcUPIuKEnojZrB25RmzWAUmbAufhSznVaq2PNfNfV8oqfFyyfsUHFrOOfZ+0r0wHvgAMKj7Wst4fsAhwXGE6387lZmazOBGbdaxyo9UFwHjgMUlflTRI0gOSjgaQ9A9JZ+Tu3wGXkxLxk3n8DZiHNHMy0sW/0WUvm1lvchOQWcem5/+VGyrmY/ZJbHX3gNw9oFBeGW9m/v8KsBjwRUmnRcRrPRF0T5M0qYcmfVpE1PqNstk8yYnYrGN3AtsCuwNXAitHROWJUKtXBoqIrQrd+0paCBgFrJKLx+f/ZwNHACOAqyX9T0RM7dlF6BHVdzy3ytAemq5ZW3LTtFnHjiHVZgcDVwPvNvMSA2Aq8MM8jSeY/VjLo4GrcvdGwJm9tygtNaPQvTVwXVX/rpZNx6wfcSI260BE3At8BXi1i5O4H/hCRLydpzcD2An4Z+6/breDLMexpOvfTwGPAhNy91PdKLsFuKg3F8KsbP4dsVmTJA0DNic1Rw/oYHCAt0m/lR0XEXPV8vJbmfYAbo+Iia2M1cz6DidiMzOzErlp2szMrEROxGZmZiVyIjYzMyuRE7GZmVmJnIjN+iFJw/PvnQ/MnwdIelbSuZKWkHS5pDvz38Z5mNGSpkkamj9vk6cxMn/eVdLE/NjPkwrzmirpVEkPSrpO0hK5/EZJ6+buEyRNzd0L5+HuydP7YlXc0yRNkPS0pDG5fP8c63059gV7ZUWatYATsVn/9Tiwfe7+LDA5d58OnBoR6wFfZs4HjtwP7Ja79wLuBpC0DHASsCXpiWHrSapMeyHgrohYA7iJ9PvjWSQtCWxVKHoH2CEiPglsAZxSeJfzAOCxiBhBetBKxRURsV5EfAJ4GPhaZ1aEWZmciM36r+nA45LWAPYEzs/lWwNjJE0A/gwsKmnh3O/PwBckLU56j/ALuXw94MaIeCk//vNCYLPc733gktx9AbBJVRw/AE4sfBZwoqT7SQ89WRZYKvcbTErU1daUNE7SRNKjSNdoch2Ylc7Pmjbr384BjiIdC/6by+YDNoyIORJerpROBx4Dfgz8Adi3C/MsPrxgOLBmRBwyu9LL7sASwDoR8W5+ucQCud8ywHM1pnkusH1E3Cdpb2BkF+IyK4VrxGb9WETcDSxJSsgV1wKHVD5IGlE12lnAJ4G/FMruADaXNEzSAGBXUjM0pOPMjrl7N9JjLCuOpaqpGhgCvJiT8BbM+XKJnYBbayzKIsDzkgaSErlZn+FEbNbPRcQ2EXF1oehQYF1J90t6CDiwavgJEbFuRLxbKHse+C5wA3AfcHdE/Cn3fgtYX9IDpGvIPyxM7pmIuLkqpAvz/CeSrkM/AiDpZNL15l/VWIwfkN5udWtleLO+wo+4NLMeJWlqRCzc8ZBm/ZNrxGZmZiVyjdjMzKxErhGbmZmVyInYzMysRE7EZmZmJXIiNrMeJWkDSbtLWqDjoc36Hz9Zy8x6jKSPAbfnj6sBo0oMx6wtuUZsZj1p6UL3cqVFYdbGnIjNrEskfTO/OvH4BoPdSH5DE/DzBtPaXNLjkq6WNKiVcZq1O/+O2My6RNLzwIeAmcCQiHirxjBrA/fkj6dFxLfrTOtiYJf88TMR8Y8eCNmsLblGbGZddR7pFYdvMvvtSNWKL3Q4UNJS1QPkdw1Xnlv9CHBbK4M0a3dOxGbWJRHxv8DXgaHAnZLWKvbPteEvFooWAI6oGmZB0juK9wCuA1aPiKk9GbdZu3EiNrPuqFz/XQm4TdIOMKuWe1yN4Q+WtEQeZgVgHOnViAB3ha+VWT/ka8Rm1mX5t8FTgQGF4rGkn0Z+qc5od5Can3cHhhXKd4uIi3oiTrN25kRsZt2S3zO8RgsmtUZEPNSC6Zj1KW6aNrPuur8F05gO/LsF0zHrc5yIzay7WpGIH4yI91owHbM+x4nYzLrrvjaZhlmf5ERsZt3VihqxE7H1W07EZtZdzwGvdHMarUjmZn2SE7GZdUv+7W93a7ROxNZvORGbWSv8tRvjjo+Il1sWiVkf40RsZq1wOnAG6eEezZoJ3Ars2SMRmfURfqCHmZlZiVwjNjMzK5ETsZmZWYmciM3MzErkRGxmDUmaWuheWtLjkrYrMyazeYkTsZk1RdIipJ8pnRQRfyk7HrN5hROxmXVI0kDgCuDPEfG7XLavpNMKw+wv6VRJwyU9IulCSQ9LukzSgnmYSZImSpqQ/wZJ2k7SeEn3SvqnpKXysKMlHVGY/lWSRvbqgpv1AidiM2vG2cDmwEWFsrHAdjlJA+yThwP4KPDriFgNeAM4uDDeFhExIv/NAG4BNoyItYGLgaN6cDnM2o4TsZl1ZCFgcWBv4FeVwoiYClwPbCvpY8DAiJiYe0+OiFtz9wXAJg2mvxzwd0kTgSOBNQr9vl2pPQObtmJhzNqNE7GZdWQ6sFNE/AF4T9LuhXN+dUAAAAvUSURBVH5nkhL0PsA5hfLqJwU1enLQL4ExEbEW8HVggUK/Uyu1Z2BcF+M3a2tOxGbWkfci4q3c/Q3gR5KGAETEeGB5YDfmbLZeQdKncvdupObneoYAz+bur7YsarM+wonYzJoWEY+Tar4nForHArdGxKuFskeBb0h6GPgg8JsGkx0NXCrpbmBKayM2a39+1rSZdYukq0hNyNflz8OBqyJizTLjMusrXCM2sy6RNFTSv4FplSRsZp3nGrGZmVmJXCM2MzMrkROxmXWLpHUlnStpiKRjJB1Udkxmfcn8ZQdgZn2TpDWBtYCVST87mg4cANwq6VFgQET8o8QQzfoEXyM2sy6RdBnwZeAZ0tOxKl4ElgSeiYjly4jNrC9x07SZddUo4H7mTMKQkvCLwH69HpFZH+REbGadJmkX0iMnP15nkCWByyT9sPeiMuubnIjNrCvGAMM6GGZh4BlJH5T0E0l/knRU4W1NSFo2vzrxj5IOljSgR6M2a0O+RmxmnSapmQPHLcBWwB3AJwrl1wLbAssC/wKWLvT7ZUQc2qo4zfoC14jNrKdcAWxGSsK3A1sAE4DPAD8nPaN6aeD3wJbAm8BBkhaoOTWzeZQTsZm12kxg+4g4FVgwl71KqhlvD7wMfBNYD7iBdFPXI8A0YAD+WaX1M26aNrNO66BpeizwFeCnwMHA4E5O/iXg4Ii4rIvhmfUprhGbWas9CqwOfIf6SfjbpCbpWpYAft0DcZm1JTcBmVmrvU9Kpo1MAIY26N/R+GbzDNeIzczMSuREbGZmViI3TZtZy0XEjYA6GOxGYHRPx2LW7pyIzazlJI0ATmswyGHACGDvegNExMjWRmXWnpyIzawnDAU276D/8A6GMesXfI3YzMysRE7EZtYV73TQf1o3p9/d8c36DCdiM+uKMxr0Gw7cC9zVjen/phvjmvUpfsSlmXWaJAFrAovX6P1GRNwjaRCwNrWfrlV5oMfwGv2mAA+GD07WTzgRm5mZlchN02ZmZiVyIjYzMyuRE7GZ1SXpAUkPSZog6VlJo3twXitIOl/SHXm+w3pqXmbtxA/0MLOObBMRT0k6Ali4J2YgaQHgIuBo4CbfqGX9iWvEZtbIQGB6sUDSSEk3S7pa0qOSfitpPkn7SjqtMNz+kk6VtLykeyWtmMun5v8fkXSXpCWALUl3V48BJko6qTCdqdVB5Rrz8B5YXrNe50RsZo0sArxZo3x94BBgdWAV4EvAWGA7SQPzMPsAZ0fEZGB/YKykRQEkLQ78AdgrIl4ivX94WWAL0jOo15O0fY8tlVkbcSI2s5okDQAWiYi3avS+IyKejIiZpCblTSJiKnA9sK2kjwEDI2IiQETcBTwJXEI67lwB3BsRD1VmB/w9Il6KiPeAC4HNcr/B+Rr1fZJ+IcnHLZuneIM2s3pWBv5dp1/1NdzK5zNJb1TaBzin0lPSusAypFcfDgYuBT4uafU8yBsN4pgWESOAdYCPA1s3vQRmfYATsZnVszNwW51+60taKddOdwFuAYiI8cDywG6kmjJ5mF8A34yIk4C3ImIMcCjpmjDA3cCWkoblmviuwE3FGeaa8uvAoNYtoln5fNe0mc1F0kHACcBTkjbJxUsAA4B7gDtJSXRV4AbgysLoY4EREfFq/nwgcFulmboiIsZLelzSnhFxfv5p1M3ATODqiPhTHnSwpFtIN45NAv4O/KSVy2tWJj/i0szmkpPipIg4t1Y5sGNEbFtn3KuAUyPiup6N0mze4KZpM2sJSUMl/Zt0TddJ2KxJrhGb2VwkzQ9Eviu6w3Iz6zonYjMzsxK5adrMzKxETsRmZmYl8s+XzKwlJG0KbErnf+f7HHBJRLze+qjM2p+vEZtZt0k6DDi1G5N4Evikk7H1R26aNrNW+N9ujr8y6QldZv2OE7GZtcKH8v/jIkLFP+C4ykDV/XL/imV6NWKzNuFEbGZmViInYjMzsxI5EZuZmZXIidjMzKxE/h2xmbXSyPyGpjnKKh01+pn1e07EZtZKm+e/eo7trUDM+go3TZuZmZXIidjMWqk7vyM265eciM3MzErkRGxmZlYiJ2IzM7MS+a5pM2uFd4GBdO/nSzNaHpVZH+DXIJpZt0m6gULC7aLNImJcC8Ix61PcNG1mrfA14EZSzbizXgC+7SRs/ZVrxGZmZiVyjdjMzKxETsRmZmYlciI2MzMrkROxmZlZiZyIzczMSuREbGZzkTRc0jRJE/LfeZImSTpZ0kRJd0haNQ+7naTxku6V9E9JS+Xy0ZKOKEzzKkkjc/fUQvk4SVc1Mc4Ski6XdGf+27g31oVZT3MiNrN6noiIEflvr1z2ekSsBYwBTstltwAbRsTawMXAUc3OQNLngSFNDn46cGpErAd8GTiz2fmYtTM/4tLMOuOiwv9Tc/dywCWSlgYGAf8pDP9tSXvk7pWAn1V6SBJwNHAisAcd2xpYPY0GwKKSFo6IqQ3GMWt7rhGbWWdEje5fAmNyTfnrwAKFYU6t1KqB6idn7Up6GtcLTc57PlLNu1JLX9ZJ2OYFTsRm1hm7FP7flruHAM/m7q82OZ35gMOAkzsx72uBQyofJI3oxLhmbctN02bWGR+UdD8wnVSjBRgNXCrpVeB6UhN0RwYDl0fEa4Wm5opvSNo+d69OqnGvBRwK/CrPf37gZuDAbiyLWVvws6bNrCmSJgHrRsSUXp7vjRExsjfnadab3DRtZu3urLIDMOtJrhGbmZmVyDViMzOzEjkRm5mZlciJ2MzMrEROxGZmZiVyIjazlpH0c0kzJUWNvyclXSxps7LjNGsnvmvazFpG0nvAgA4GC+AnwNHhA5CZE7GZtY6kygHlbuCuYi9gZWAz0oshAPaKiPN7MTyztuREbGYtU0jEx0XE6Br91wSuAZYFXgVW8IsbrL/zNWIz6zUR8QBweP74QeCTJYZj1haciM2st/2z0L12aVGYtQknYjPrbYsXut8uLQqzNuFEbGa97YBC932lRWHWJvw+YjPrcUovHV6R9P7g7+TiO4A7SwvKrE34rmkza5nCXdMdeRPYPCLu7cl4zPoCN02bWW/7N/BZJ2GzxDViM2uZBg/02BpYJXdvFhHjejUwszbmRGxmLVPvgR6S1gHGkx5/+QjwyYiY1vsRmrUfN02bWY+LiLuBE/PHjwHHlxiOWVtxIjaz3nICcH/uPlzSxmUGY9YunIjNrFdExAxgb+A90ksgzpW0UKlBmbUBJ2Iza6X38//3avXMd0r/KH9cFdi+N4Iya2dOxGbWSr8CJgFXNBjmROAfpMdbPtwLMZm1Nd81bWa9TtJ8wICIeLfsWMzK5kRsZmZWIjdNm5mZlciJ2MxqkjRT0gRJD0i6VNKCJcezq6Txkm6RtEaZsZi1kpumzawmSVMjYuHcfSFwd0T8vOSwzOY5rhGbWTPGkX5uhKTDcy35AUmHVQaQtJek+yXdJ+n8XLaUpCtz2X2SNpI0XNIDuf9ASU9KGpM/LyHpckl35r+Nc/loSUfk7q0lhaR1e3kdmPUIv4/YzBqSND+wDXBNfmb0PsAGpIdyjJd0EzADGAVsFBFTJC2WR/8FcFNE7CBpALAw8MHC5A8AphY+nw6cGhG3SFoB+DuwWlVIxwCPt3QhzUrkRGxm9QyWNCF3jwPOAg4CroyItwAkXQFsCgRwaURMAYiIV/J4WwJ75bKZwOuSPpjHXYiU1H8NrJmH3xpYXVIlhkUlLVz5IOnLwJ3AOi1fWrOSOBGbWT3TImJEsaCQIFvhW8D/kWrTFfMBG0bEOzXmOwA4EtgWuKyVgZiVydeIzawzxgHbS1ow12h3yGXXAztJWhyg0DR9HakWjaQBkobk8iGkx1ueXTX9a4FDKh8kFU8E9gD+Wql1m80rnIjNrGkRcQ9wLnAH6f3CZ0bEvRHxIOkZ0jdJug+o3F39LWALSROBu4HVc/lywCkRUf1M6kOBdfNNXw8BBxb6LVWYrtk8wz9fMjMzK5FrxGZmZiVyIjYzMyuRE7GZmVmJnIjNzMxK5ERsZmZWIidiMzOzEjkRm5mZlciJ2MzMrET/D5eDxiLj7CIsAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"None\n"
]
}
]
}
]
}
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