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Douglas Duhaime duhaime

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from machine import Pin
import serial
# serial:
# create an output pin on pin #8
out_pin = Pin(8, Pin.OUT)
# set the output pin to high mode
duhaime /
Created Jul 16, 2020
Mido port streaming
import mido
with mido.open_input('mido_port', virtual=True) as in_port:
for msg in in_port:
# -----------------------------------------------------------------------------
# From Numpy to Python
# Copyright (2017) Nicolas P. Rougier - BSD license
# More information at
# -----------------------------------------------------------------------------
from matplotlib.animation import FuncAnimation
from collections import namedtuple
import matplotlib.pyplot as plt
import numpy as np
import random
duhaime /
Created May 29, 2020
Flask and Starlette Servers for GPT-2
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'))
os.system('mv models/117M/* checkpoint/run1/')
from dateutil.relativedelta import relativedelta
from datetime import date, datetime
import json
import requests
import calendar
import time
import sys
end =
start = end - relativedelta(years=3)
duhaime /
Last active May 23, 2020
play midi file
# 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()
duhaime / geb.txt
Created May 12, 2020
Goedel Escher Bach plaintext
View geb.txt
This file has been truncated, but you can view the full file.
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
View fetch from localhost
// NB: To load from localhost one can:
.then(response => response.blob())
.then(async image => {
const img = document.createElement('img');
img.src = URL.createObjectURL(image);
img.onload = async () => {}
View create fvecs for ZJULearning AtSNE
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]
View rect-packing.ipynb
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