duhaime

from machine import Pin
import serial

# serial: https://pythonhosted.org/pyserial/pyserial.html#overview

# create an output pin on pin #8
out_pin = Pin(8, Pin.OUT)

# set the output pin to high mode
out_pin.value(1)

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import mido

with mido.open_input('mido_port', virtual=True) as in_port:
  for msg in in_port:
    print(msg)

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# -----------------------------------------------------------------------------
# From Numpy to Python
# Copyright (2017) Nicolas P. Rougier - BSD license
# More information at https://github.com/rougier/numpy-book
# -----------------------------------------------------------------------------
from matplotlib.animation import FuncAnimation
from collections import namedtuple
import matplotlib.pyplot as plt
import numpy as np
import random

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from flask import Flask, jsonify, request
import gpt_2_simple as gpt2
import tensorflow as tf
import os

if not os.path.exists('checkpoint'):
  os.makedirs(os.path.join('checkpoint', 'run1'))
  gpt2.download_gpt2(model_name='117M')
  os.system('mv models/117M/* checkpoint/run1/')

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from dateutil.relativedelta import relativedelta
from datetime import date, datetime
import json
import requests
import calendar
import time
import sys

end = date.today()
start = end - relativedelta(years=3)

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# conda install -c cogsci pygame
import pygame, base64

def midi_to_base64(midi_filename):
  return base64.encodestring(open(midi_filename, 'rb').read())

def play_music(midi_filename):
  '''Stream music_file in a blocking manner'''
  clock = pygame.time.Clock()
  pygame.mixer.music.load(midi_filename)

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Introduction:
A Musico-Logical Offering
Author: FREDERICK THE GREAT, King of Prussia, came to power in 1740. Although he is emembered in history books mostly for his military astuteness, he was also devoted to he life of the mind and the spirit. His court in Potsdam was one of the great centers of ntellectual activity in Europe in the eighteenth century. The celebrated mathematician

duhaime

// NB: To load from localhost one can:

fetch(url)
  .then(response => response.blob())
  .then(async image => {
    const img = document.createElement('img');
    img.src = URL.createObjectURL(image);
    img.onload = async () => {}
  })

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import numpy as np
import sys
import struct

dim = 50
a = np.load('stacked-vecs-{}.npy'.format(dim))

with open('stacked.fvecs', 'wb') as out:
  for i in a:
    line = [float(j) for j in i]

duhaime