YiLi225

## With asyncio.TaskGroup
async def run_errands():
    async with asyncio.TaskGroup() as tg:
        for errand, (start_time, time_to_finish) in errandsDict.items():
            tg.create_task(errands_log(errand, 
                                       start_time, 
                                       time_to_finish))

YiLi225

(base) C:\Desktop>python async.py
(Pick Up Kids) starting at 6am
(Pick Up Kids) done at 7am
======;
(Return Packages) starting at 9am
(Return Packages) done at 10am
======;
(Grocery Shopping) starting at 11am
(Grocery Shopping) done at 13am
======;

YiLi225

## Errands: [start_time, duration]
errandsDict = {
    'Grocery Shopping': [11, 2],
    'Return Packages': [9, 1],
    'Pick Up Kids': [6, 1],
}

async def errands_log(task, start_time, time_to_finish):
    await asyncio.sleep(start_time)
    print(f"({task}) starting at {start_time}am")

YiLi225

from typing import Any, Type, TypeVar
from dataclasses import dataclass

curType = TypeVar('curType', bound='Rectangle')

class Rectangle:
    def __init__(self, length: float) -> None:
        self.length = length

    @classmethod

YiLi225

from bs4 import BeautifulSoup
from urllib.request import urlopen, Request
from collections import defaultdict
tagInfo = defaultdict(list)

for tag in tags:
    startLink = "https://medium.com/tag/"+tag
    
    response = requests.get(startLink, allow_redirects=True)
    page = response.content

YiLi225

## Install: !pip install -U layer
import layer
df = layer.get_dataset('layer/wikitext/datasets/wikitext-103-train').to_pandas()
doc_set = [i for i in df.sentence.str.lower()]

YiLi225

# Install: !pip install sentence_transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('distilbert-base-nli-mean-tokens')

# sentences = [doc_set[9234], doc_set[9239], doc_set[1131966]]
sentence_embeddings = model.encode(sentences)

# Calculate cosine distance of the embeddings
from scipy.spatial import distance
print(1 - distance.cosine(sentence_embeddings[0], sentence_embeddings[1]))

YiLi225

##### Generate the wordcloud #####
my_freq_grams = freq_grams

curMask = np.array(Image.open(pathToYourPic))
wc = WordCloud(background_color='white',
               stopwords=stopwords,
                      width=800, 
                      height=600, 
                      relative_scaling=.6, 
                      max_font_size=60, 

YiLi225

from wordcloud import WordCloud, ImageColorGenerator
from nltk.corpus import stopwords
from nltk.util import ngrams
import nltk

def replace(match):
    return swMapping[match.group(0)]
## Define stopwords
curSW = stopwords.words('english')
curSW += ['unk']

YiLi225

## Top 5 next words
['dishes', 'and', ',', '-', 'recipes']

## Comparing with the original text
falafel has become popular among vegetarians and vegans , 
as an alternative to meat @-@ laden street foods , 
and is now sold in packaged mixes in health @-@ food stores .
falafel 's versatility has allowed for the reformulating of recipes for meatloaf , 
sloppy joes and spaghetti and meatballs into vegetarian [dishes] .