Romain Beaumont rom1504

In general that's the procedure to follow for a new game :

Check if somebody already did something even partially, if so read their code, take what you can, learn the general structure of things
If nothing exists, boot up Wireshark, look if the protocol is encrypted, textual, binary, you might have some luck at this point
If Wireshark failed, download the vanilla game binaries, identify what language it was, if java based (or c#) decompile, try to find the packet classes. If native you can try using idapro but it's going to be harder
Assuming some of the above worked, document the protocol
Implement the low level (tcp, udp, encryption, compression, splitting)
Implement parsing and serialisation (we use protodef for this)
Implement login sequence At this point you have something on the level of node-minecraft-protocol

	from multiprocessing import Pool
	from tqdm import tqdm
	import csv
	import cv2
	import os
	import urllib.request
	import hashlib


	IMAGE_SIZE = 1280


	from multiprocessing import Pool
	from tqdm import tqdm
	import csv
	import cv2
	import os
	import urllib.request
	import hashlib

	chat = (o) => bot.chat(JSON.stringify(o))

	craft = (name, count=1) => {
	let crafting_table = bot.findBlock({matching:mcData.blocksByName.crafting_table.id, maxDistance:5})
	console.log(crafting_table)
	let item = mcData.itemsByName[name]
	console.log(item)
	let recipes = bot.recipesFor(item.id, item.metadata, count, crafting_table)
	console.log(recipes)
	bot.craft(recipes[0], count, crafting_table)

	async function f() {
	console.log('a')
	await new Promise(resolve => setTimeout(resolve, 0))
	}
	f().then(() => console.log('c'))
	console.log('b')

	import faiss
	import numpy as np
	import re
	import time
	def index_factory(d: int, index_key: str, metric_type: int):
	"""
	custom index_factory that fix some issues of
	faiss.index_factory with inner product metrics.
	"""
	if metric_type == faiss.METRIC_INNER_PRODUCT:

	{
	"minecraft:crafting_shaped": {
	"type": "minecraft:crafting_shaped",
	"group": "stained_terracotta",
	"pattern": [
	"###",
	"#X#",
	"###"
	],
	"key": {

	from math import sqrt
	from scipy import stats
	from scipy.stats import norm, skew
	from scipy.special import boxcox1p
	import pandas as pd
	train = pd.read_csv('train.csv')
	test = pd.read_csv('test.csv')
	train_ID = train['Id']
	test_ID = test['Id']
	# Nous retirons la colonne Id qui n'est pas utile pour l'entrainement

	import tensorflow as tf
	from tensorflow import keras
	import numpy as np
	from keras.layers import Dense, Activation
	from keras.models import Sequential

	model = Sequential([
	Dense(40, input_shape=(1,)),
	Activation('relu'),
	Dense(12),