fwSara95h

Extract JavaScript variables from BeautifulSoup objects

INPUTS

• inpX: must be a bs4 document/tag/ResultSet or a string or a list of strings
  • ( target variable must be JSON and seaparated from other variables by ; )
• varName: name of the target variable
  • ( only the first variable found with the specified name will be returned )
• selector: a CSS selector for searching the bs4 document/tag for target script
  • ( if inpX is a script-tag/ResultSet/string/list then selector doesn't matter )
• prepFn: should be a univariate function that takes a string and returns a string
  • ( for modifying the script string before searching for and parsing variable )

fwSara95h

### Variables + zone & region

YOUR_REGION = "" # scenario europe-west1
YOUR_ZONE = "" #scenario europe-west1-b
INSTANCE_NAME = "" #task1 nucleus-jumphost-640
CLUSTER_NAME = "" #task2 [nucleus-backend] [not provided - made up]
APP_PORT_NUMBER = "" #task2 8080
FIREWALL_RULE = "" #task3 accept-tcp-rule-846

fwSara95h

from datetime import date, datetime, timedelta 

def get_midpoint(sd:date, ed:date, eod=True, is_v:bool=True, toRet=None):
    # Calculate the midpoint
    start_date = datetime(sd.year, sd.month, sd.day, 
                          23 if eod else 0, 59 if eod else 0)
    end_date = datetime(ed.year, ed.month, ed.day, 0, 1 if eod else 0)
    total_duration = (end_date - start_date).total_seconds()
    half_duration = total_duration // 2 # integer quotient
    midpoint_dt = start_date + timedelta(seconds=half_duration)

fwSara95h

HTML Heading Nesting Parser

This gist contains a Python tool for converting flat HTML documents—especially those with nested content indicated only by header levels (like <h1>, <h2>, etc.)—into a structured JSON object that reflects the implicit hierarchy.

Originally inspired by this StackOverflow question from @psychicesp, this script is useful for:

• Web scraping content-heavy pages (like menus, outlines, legal docs)
• Explicitly inferring structure from heading levels
• Preserving content hierarchy when working with markdown-to-HTML output

fwSara95h

def yield_key_paths(objC, valT, kPath=None, validTypes=(tuple,list,dict,set)):
    ''' Traverse objC data structure recursively to yield path/s to valT'''
    if not isinstance(kPath, list): kPath = []
    if objC == valT: yield kPath

    if not isinstance(objC, validTypes): return 
    for k,v in (objC.items() if isinstance(objC,dict) else enumerate(objC)):
        yield from yield_key_paths(v, valT, kPath+[k], validTypes)

        

fwSara95h

## [ specifying data type of value with dType is optional ]
# a generator for the values
def yield_by_nested_keys(obj:dict, *keys, dType=None):
    if not keys or not isinstance(obj,(dict,list,set,tuple)): return
    if not isinstance(obj, dict):
        for v in obj:
            for y in yield_by_nested_keys(v,*keys,dType=dType): yield y
        return
    if len(keys)==1 and keys[0] in obj:
        if dType is None or isinstance(obj[keys[0]], dType):

fwSara95h

Réussir TCF Canada - Data Scraper & Task Tracker

View the notebook on Google Colab (if you want to run the code interactively).

This project contains an experimental scraper and data processing notebook for building practice tools for the TCF Canada exam, focusing on Expression Orale and Expression Écrite tasks.

📌 Overview

• Goal: Collect, organize, and categorize prompts for TCF Canada speaking and writing practice.
• Use Case: Forms the foundation for a web app or study aid that randomizes practice tasks and ensures coverage across different categories.

fwSara95h