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Capturing rain in photos can be challenging due to the movement and varying light conditions. Here are some tips to help you take better rain photos with your iPhone:

1. Use Burst Mode

Why: Rain is in constant motion. Using burst mode allows you to take multiple shots quickly, increasing the chances of capturing a clear raindrop or the perfect moment.
How: Hold down the shutter button to activate burst mode.

2. Adjust Exposure

Why: Rain can cause your photos to be either too dark or too light. Adjusting the exposure can help balance this out.
How: Tap on the screen to focus, then slide your finger up or down to increase or decrease the exposure.

	# This is the same function, written two different ways in Python

	def oddEvenA(number:int) -> str:
	if number % 2 == 0:
	odd_even = "even"
	else
	odd_even = "odd"

	return odd_even

	# file: bingo_card_generator.py
	# ---------------------------------------------------------
	# author: Adge Denkers
	# github: @adgedenkers
	# created: 2025-02-01
	# updated: 2025-02-01
	# version: 1.0.0

	import numpy as np
	from openpyxl import Workbook

	from openpyxl import load_workbook
	from openpyxl.styles import Font, PatternFill

	def modify_excel(input_path, output_path):
	# Load the workbook and select the relevant sheet
	workbook = load_workbook(input_path)
	sheet1 = workbook['Sheet1']

	# Step 1: Freeze header row and columns A-F
	sheet1.freeze_panes = 'G2'

	#!/bin/bash
	# File: ~/src/setup_nginx_and_services.sh

	set -e # Exit immediately if a command exits with a non-zero status

	# Variables
	DOMAIN_DASHBOARD="dashboard.denkers.co"
	DOMAIN_API="api.denkers.co"
	NGINX_CONF="/etc/nginx/sites-available/denkers.co"
	SYSTEMD_DIR="/etc/systemd/system"

	# -----------------------------------------------------------------------------
	# List sports schedules
	# -----------------------------------------------------------------------------

	async def fetch_schedule_data() -> List[Dict[str, Any]]:
	"""
	Asynchronously fetch and parse the sports schedule data.
	"""
	url = "https://www.schedulegalaxy.com/schools/159/teams/58255"
	params = {

	# BBallSchedule.py
	#
	# Author: AdgeDenkers
	# created: 2024-10-28
	# updated: 2024-11-13
	# Version: 1.1
	# Note: Get the current dates event if any


	import requests

	{
	"manufacturer": "Samsung",
	"product_type": "Household Microwave Oven",
	"manufactured_in": "42000 Pelabuhan Klang, Selangor, W. Malaysia",
	"manufacture_date": "March 2022",
	"model_number": "ME19R7041FS",
	"serial_number": "OBQKTVTT301045Z",
	"power": {
	"voltage": "120 Vac",
	"frequency": "60 Hz",

	import pandas as pd

	def compare_excel_sheets(file1, file2, sheet1, sheet2, columns_to_compare, unique_id_column):
	# Load the excel files
	df1 = pd.read_excel(file1, sheet_name=sheet1)
	df2 = pd.read_excel(file2, sheet_name=sheet2)

	# Merge the dataframes on 'Position Number'
	merged_df = pd.merge(df1, df2, on=unique_id_column, suffixes=('_file1', '_file2'))

	1. Data Volume
	Description: The amount of data being processed.
	Impact: Larger volumes of data require more processing time and resources.
	2. Data Complexity
	Description: The complexity of the data structure (e.g., multiple tables, nested data).
	Impact: More complex data requires more sophisticated analysis techniques and tools, increasing workload.
	3. Frequency of Data Updates
	Description: How often data is updated or refreshed.
	Impact: Frequent updates can increase the workload as data needs to be continuously processed and analyzed.
	4. Analysis Type