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/*
@title Strudeldudel
@by Lennard Voogdt
@license CC BY-NC-SA
*/

// let cpm = 77
setcps(78/60)

register('withAsdr', x => x.attack(saw.range(0, 0.05).fast(6)).decay(rand.range(0.1, 0.3)).sustain(0))

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import math
from collections import Counter

# primes chosen to be the 4, 6, 8, 12, 20, 100 primes to match the dice roll
# with this it is at least possible to get a reproducible sequence, for each one
# with length desired

DICE = [4, 6, 8, 12, 20, 100]
PRIME_NUMBERS = [11, 13, 19, 29, 41, 211]

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Important: Background information

Overview

This page will look up trainer battle AI procedures for the Generation IV Pokémon games – Pokémon Diamond, Pearl, Platinum, HeartGold, and SoulSilver. Trainer AI between each of these games is nearly identical, with only Diamond and Pearl having a single small difference from Platinum, HeartGold, and SoulSilver (in Icy Wind / Mud Shot / Rock Tomb!).

I recommend you read over this whole article at least once to be made aware of all the strange quirks and edge cases that affect the AI procedures.

The Battle AI uses a scoring system to determine which move to use (and which target to use it on, if applicable). Each move starts with a score of 100 (but this starting point is arbitrary), which is then added or subtracted to by the different parts of the scoring procedure.

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{
    "description": "Remap mouse keys",
    "manipulators": [
        {
            "from": {
                "key_code": "spacebar",
                "modifiers": { "mandatory": ["right_control"] }
            },
            "to": [
                {

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[
  {
    "isrc": "USUAN1100001",
    "title": "C-Funk",
    "instruments": [
      "Bass",
      "Kit",
      "Guitar",
      "Bari Sax",
      "Tenor Sax",

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from itertools import cycle
# the colors cycle in 3 different "rates"


def linspace(start, stop, num):
    """Generate num evenly spaced numbers over the interval [start, stop]."""
    if num < 2:
        return [start]
    step = (stop - start) / (num - 1)
    return [start + step * i for i in range(num)]

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Speech to Text

The original Whisper model is a good speech to text transcription model which is used in many places: https://huggingface.co/openai/whisper-large-v3

Text to Speech

WhisperSpeech is a good text to speech model with voice cloning: https://huggingface.co/WhisperSpeech/WhisperSpeech that uses an MIT license (unlike coqui and suno). It isn't the "best" model, but for its size it is very, very good.

Other alternatives is a purely ONNX driven model as sponsored by txtai: https://huggingface.co/NeuML/ljspeech-jets-onnx

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write a glowing review for my real estate agent who sold my apartment and provided attention to our particular requests and wants

Subject: A Stellar Real Estate Experience with [Agent's Name]

I recently had the pleasure of working with [Agent's Name] from [Real Estate Agency] to sell my apartment, and I cannot express enough how impressed I am with the level of service and professionalism I received throughout the entire process.

From our initial consultation to the successful closing, [Agent's Name] demonstrated a deep understanding of the real estate market and a genuine commitment to helping me achieve the best possible outcome. Their expertise and guidance were invaluable, making what could have been a stressful experience remarkably smooth and efficient.

One of the standout qualities of [Agent's Name] was their excellent communication skills. They kept me informed every step of the way, promptly responding to my inquiries and providing regular updates on the progress of the sale. This level of tran

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[Background]
Color=39,40,34

[BackgroundIntense]
Color=65,67,57

[Foreground]
Color=225,225,218

[ForegroundIntense]

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# The goal of this is to try to make use of Shap to explain a tree built in river.
# https://github.com/online-ml/river/issues/437

from functools import reduce
import operator

import numpy as np
import pandas as pd
import pprint
from sklearn import datasets