Vinay Varma nvinayvarma189

I want to solve the following problem, but I’m stuck.
Please act as a “cognitive centrifuge.”

Break the problem into exactly four numbered sub-problems.
For each sub-problem, give me one concrete micro-action I can do in under 2 minutes that will either (a) make the problem smaller, or (b) give me new information.
After the four micro-actions, add a one-line “momentum booster” I can say out loud to prime my brain to keep going.
Format the whole thing so I can read it in <15 seconds.

Problem:

Final submission for a OpenWorm GSOC'20 project OpenDevoCell Integration'

Mentors - Dr. Bradley Alicea, Vinay Varma

# Ubuntu
sudo apt-get install gpa seahorse
# MacOS with https://brew.sh/

	"""Hello world, with a genetic algorithm.

	https://twitter.com/matthen2/status/1769368467067621791
	"""

	import random
	import time
	from dataclasses import dataclass
	from itertools import chain
	from typing import Iterable, List

	#!/usr/bin/env bash

	##
	# This configures an S3 bucket ObjectCreated notification for the given Lambda
	# function so that when a file is uploaded the Lambda function is invoked. This
	# is useful when the bucket is not defined in the same CloudFormation template
	# as the function. CloudFormation cannot setup the notification in this case.
	#
	# Note that the AWS CLI is used and will require valid AWS credentials for the
	# account containing the resources. The 'jq' JSON processor is also required

	#!/usr/bin/env python3
	"""List S3 bucket objects in parallel.

	This module contains a parallel implementation of S3 list_objects_v2()
	API call. The implementation lists objects under every distinct prefix
	in parallel. Hence, a speedup is achieved if objects are spread under
	multiple distinct prefixes.
	"""

	import argparse

	#code adapted from http://tillbergmann.com/blog/python-gradient-descent.html

	%matplotlib inline
	import numpy as np
	import seaborn as sns
	import matplotlib.pyplot as plt
	import matplotlib.animation as animation
	from scipy import stats

	from sklearn.datasets.samples_generator import make_regression

	from sklearn.cluster import KMeans
	from sklearn import metrics
	import cv2
	# By Adrian Rosebrock
	import numpy as np
	import cv2


	# Load the image
	image = cv2.imread("red.png")