jdwebprogrammer

import pandas as pd
import matplotlib.pyplot as plt

# Hex value to analyze
hex_value = 0x12345678

# Break into bytes (big-endian order)
bytes_big_endian = [(hex_value >> (i * 8)) & 0xFF for i in range(3, -1, -1)]  # [0x12, 0x34, 0x56, 0x78]
bytes_little_endian = bytes_big_endian[::-1]  # Reverse for little-endian: [0x78, 0x56, 0x34, 0x12]

jdwebprogrammer

import pandas as pd
import matplotlib.pyplot as plt

# List of numbers from 0 to 15 with decimal, hexadecimal, and binary representations
numbers = [
    (i, f'0x{hex(i)[2:].upper().zfill(2)}', f'{bin(i)[2:].zfill(4)}')
    for i in range(16)
]

# Create DataFrame

jdwebprogrammer

import pandas as pd
import matplotlib.pyplot as plt

# List of common unit conversions
conversions = [
    ('Length', '1 inch', '2.54 cm'),
    ('Length', '1 foot', '0.3048 m'),
    ('Length', '1 mile', '1.60934 km'),
    ('Length', '1 meter', '3.28084 ft'),
    ('Length', '1 kilometer', '0.621371 mi'),

jdwebprogrammer

import pandas as pd
import matplotlib.pyplot as plt

# List of SI prefixes with symbols and exponents, limited to zetta and below
prefixes = [
    ('zetta', 'Z', 21),
    ('exa', 'E', 18),
    ('peta', 'P', 15),
    ('tera', 'T', 12),
    ('giga', 'G', 9),

jdwebprogrammer

from CogFileFame import CogName

client = commands.Bot(command_prefix=".", intents=discord.Intents.all(), case_insensitive=True)

@client.event
async def on_ready():
    await setup(client)

async def setup(client: commands.Bot):
    await client.add_cog(Cogname(client))

jdwebprogrammer

import win32ui
import win32con
import win32gui
from PIL import Image

def png_to_uncompressed_ico(input_file, output_file):
    try:
        # Open the PNG image
        img = Image.open(input_file)

jdwebprogrammer

from PIL import Image

def png_to_ico(input_file, output_file):
    try:
        # Open the PNG image
        img = Image.open(input_file)

        # Convert it to an ICO icon
        img.save(output_file, format="ICO")

jdwebprogrammer

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import NMF

class NMFTopicModel:
    def __init__(self, n_topics=5, max_features=1000):
        self.n_topics = n_topics
        self.max_features = max_features
        self.vectorizer = TfidfVectorizer(max_features=self.max_features, stop_words='english')

jdwebprogrammer

import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

class LDATopicModel:
    def __init__(self, n_topics=5, max_features=1000):
        self.n_topics = n_topics
        self.max_features = max_features
        self.vectorizer = CountVectorizer(max_features=self.max_features, stop_words='english')

jdwebprogrammer

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.models import Model
from tensorflow.keras.losses import mse
from tensorflow.keras import backend as K

class SimpleVAE:
    def __init__(self, input_dim, latent_dim):