Palash Tyagi Magnus167

In a nutshell, what is a Monte Carlo simulation anyway?

Monte Carlo simulations are typically used to get estimates of results, when the actual problem is too vast to be solved in entirety (or in a reasonable amount of time).

There are 3 examples, each closer to the real world version than the last. Example 1 is the best 😄

Example 1: Estimating the number of potholes in London

NOTICE:

TLDR : Not my original work, I copied it over as I needed it to be in markdown formatting.

This is not my own content, and is merely curated here for accessibilty and sharing. The below article has been copied from https://www.joelsleppy.com/blog/gunicorn-application-preloading/, with all rights to intellectual property reserved by the authors/domain-owners.

Gunicorn Application Preloading

An i9/i7 CPU uses about 150W (watts) of power. Which means that it somehow "dissipates" that much energy, mostly heat. Let's say it dissipates 120W as heat, which is a fair estimate.

Let's say the CPU weights about 60g = 0.06kg. That means that the CPU dissipates :

120W/0.06kg = 2000W/kg of heat.

For argument's sake let's say this is some new age alien tech that only dissipates 1 W as heat. That would imply our CPU dissipates :

1W/0.06kg = 16.7W/kg of heat.

	import json
	import urllib.request
	import sys
	import os

	# get repo and branch from sys.argv
	if len(sys.argv) < 3:
	print("Usage: python getpr.py <repo> <branch>")
	sys.exit(1)

	# Usage:
	# git checkout featureX
	# git pmerge main

	# When merging branch `main` into `featureX`,
	# one often has to pull from `main` to ensure
	# merging into the correct head.
	# Assume you're on branch featureX
	# >> git pmerge main
	# executes:

	# load commit_data.csv
	from typing import List, Optional

	import pandas as pd

	df = pd.read_csv("commit_data.csv")

	# filter where username is Palash Tyagi or Spalash

	df = df[(df["username"] == "Palash Tyagi") \| (df["username"] == "Spalash")]

	# adapted from https://matplotlib.org/stable/gallery/animation/double_pendulum.html#sphx-glr-gallery-animation-double-pendulum-py
	from numpy import sin, cos
	import numpy as np
	import matplotlib.pyplot as plt
	import matplotlib.animation as animation
	from collections import deque

	G = 9.8 # acceleration due to gravity, in m/s^2
	L1 = 1.0 # length of pendulum 1 in m
	L2 = 1.0 # length of pendulum 2 in m

	#include <stdio.h>
	#include <string.h>

	#define MAX 256
	#define INT_BITS 32

	void countingSort(int arr[], int n, int exp) {
	int output[n]; // output array
	int i, count[MAX] = {0};

	"""
	Scans Jupyter Notebooks for functions used from a given package.
	The return
	"""
	from typing import List, Dict, Set, Tuple, Type, Callable
	import ast

	# import functools
	import glob
	import nbformat