Ando takuya-andou

## ConvertToQR.py
# -*- coding: utf-8 -*-
import sys
import qrcode

# QRコードを作る場合
URLarray = ['http://google.com','http://yahoo.co.jp']
for i, URL in enumerate(URLarray):
    img = qrcode.make(URL)  # ''内の文字をQRコードに変換
    img.save('qr_img' + str(i) + '.png')  # QRコードに名前をつけて保存

## GoogleShortener.py
# coding:utf-8
import sys
import re
from urllib2 import urlopen as U, Request as R
from json import loads as J

API_KEY = "ここはGoogleのAPIを取得して使う"

API = "https://www.googleapis.com/urlshortener/v1/url?key=" + API_KEY

## piano.js
const DashButton = require('dash-button');
const {exec} = require('child_process');

const do1 = new DashButton("xx:xx:xx:xx:xx:xx"); //じゃがりこ
const re = new DashButton("xx:xx:xx:xx:xx:xx"); //パンテーン
const mi = new DashButton("xx:xx:xx:xx:xx:xx"); //ヘルシア
const fa = new DashButton("xx:xx:xx:xx:xx:xx"); //カロリーメイト
const so = new DashButton("xx:xx:xx:xx:xx:xx"); //エヴィアン
const ra = new DashButton("xx:xx:xx:xx:xx:xx"); //アマゾンベーシック
const si = new DashButton("xx:xx:xx:xx:xx:xx"); //シーブリーズ

## byebyeSteve.py
# coding: utf-8
from scapy.all import *
from picraft import World, Vector, Block
world = World("MineCraftServer IP")

def arp_display(pkt):
  if pkt[ARP].op == 1:
    if pkt[ARP].hwsrc == 'Your AmazonDashButton's Mac Address':
            world.player.pos = Vector(0, 300, 0)

## LineBotBeaconAPI.php
<?php

$json_string = file_get_contents('php://input');
$json_object = json_decode($json_string);
$AndoUserID = '<<LineUserID>>';
error_log("callback start.");
foreach ($json_object->events as $event) {
    if('message' == $event->type){
        if ($event->source->userId == $AndoUserID) {
            if ($event->message->text =='おは'){

## classifier_from_little_data_script_3.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

## googlenet.py
from scipy.misc import imread, imresize

from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, merge, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD
from googlenet_custom_layers import PoolHelper,LRN


def create_googlenet(weights_path=None):

## file0.txt
Gdk-CRITICAL **: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
Traceback (most recent call last):
  File "app.py", line 8, in <module>
    from labellio import Classifier, Config, ImageLoader, Label
  File "/usr/local/lib/python2.7/dist-packages/labellio/__init__.py", line 2, in <module>
    from labellio.classifier import Classifier
  File "/usr/local/lib/python2.7/dist-packages/labellio/classifier.py", line 6, in <module>
    from labellio.net import Net
  File "/usr/local/lib/python2.7/dist-packages/labellio/net.py", line 4, in <module>
    import caffe

## file0.txt
import {Component, AfterViewInit} from 'angular2/core';

@Component({
  selector: 'home',
  templateUrl: './components/home/home.html'
})
export class HomeCmp implements AfterViewInit {


    ngAfterViewInit() {

## haiku_input.txt
あきつ湧き宙にとどまる地獄谷
いつきに夏ふと人間が宙にあり
かいつぶり硝子の沓を宙に見す
かはらけの宙とんでゆく二月かな
からくりの唐子宙飛ぶ飛騨の春
かるがるとおんぶばつたの宙をとぶ
かるた取り紫式部宙に舞ふ
くろぐろと富士は宙吊り冬霞
こたへなき雪山宙に労働歌
こと切れしごとく枯蔓宙吊りに