JnBrymn

print("STARTED")
import time
from adafruit_circuitplayground import cp

notes = {
    "C": {
        "frequency": 523.25,
        "color": (0, 0, 3)
    },
    "D": {

JnBrymn

JnBrymn

JnBrymn

JnBrymn

#!/usr/bin/env python3
"""
This processes TiddlyWiki dumps to stick them into a format that Bear can import.

Full steps:
* First in your TiddlyWiki under the Tools menu click "export all>Static HTML".
* Next, run this command `process_tiddly_export --tiddler_dump_file=somewhere/tiddlers.html  --output_directory=/tmp/some_empty_folder/ --extra_tags=any,tags,you,want` it will
  * process the static HTML file into one file per tiddler
  * each file will start with <h1>your tiddler title</h1>
  * next it will list any #tags on the original tiddler as well as and extra tags you supplied

JnBrymn

package main

import (
	"fmt"
	"math/rand"
	"net/http"
	"os"
	"strconv"
	"time"
)

JnBrymn

/* MarkovModel creates a Markov model of text (or tokens) and allow you to generate new
 * text from the model. It takes two optional arguments:
 *
 * tokenizer - a function that takes a string and returns an array of tokens
 *   defaults to a tokenizer that breaks on whitespace and lowercases everything
 * shingle_n - the number of tokens that make up a state in the markov model
 *   the higher the number the more realistic the generated data, but the more 
 *   training data required
 *   defaults to 1
 * join_str - string used to join text together

JnBrymn

package main

import (
	"fmt"
	"github.com/Shopify/sarama" //GIT hash = b3d9702dd2d2cfe6b85b9d11d6f25689e6ef24b0
	"time"
)

var groupName = "trash"
var topicName = "event"

JnBrymn

JnBrymn

from collections import defaultdict
import random
        
class MarkovModel(object):
    """
    Takes iterator of tokens and makes a markov model of the tokens. n is the "order" of the model
    None is a special token that serves as a sort of delimiter of phrases.
    """
    @classmethod
    def _tokenizer(cls,text,token_delim):