tsu-nera

import speech_recognition as sr

r = sr.Recognizer()
mic = sr.Microphone()

with mic as source:
    r.adjust_for_ambient_noise(source)
    audio = r.listen(source)

print(r.recognize_google(audio, language='ja-JP'))

tsu-nera

tsu-nera

from sklearn.datasets import load_boston
import pandas as pd

import xgboost as xgb
from tensorboard_logger import configure, log_value
from sklearn.cross_validation import train_test_split


def logspy(env):
    log_value("train", env.evaluation_result_list[0][1], step=env.iteration)

tsu-nera

import pychromecast

def play_mp3(mp3_url):
    cast.wait()
    mc = cast.media_controller
    mc.play_media(mp3_url, 'audio/mp3')
    mc.block_until_active()

chromecast_name = "つーねらの部屋"
chromecasts = pychromecast.get_chromecasts()

tsu-nera

#!/usr/bin/python2
import subprocess
import time

exec_path = "/home/tsu-nera/tmp/a.out"
core_path = "/home/tsu-nera/tmp/core"
bt_path = "/home/tsu-nera/tmp/backtrace"

core_time = subprocess.check_output(['ls', '-l', core_path]).split()
bt_time = subprocess.check_output(['ls', '-l', bt_path]).split()

tsu-nera

#!/usr/bin/python2
import subprocess
import re

try:
    res = subprocess.check_output(['ps', '-e'])
except:
    print "Error."
lines = res.splitlines()[1:]
pidname = dict()

tsu-nera

tsu-nera

package gof.command;

import java.util.Stack;

class CommandSample3 {

    ////////////////////
    // Command
    ////////////////////
    abstract public static class Command {

tsu-nera

import Leap, sys
import paho.mqtt.client as mqtt
from Leap import CircleGesture

client = mqtt.Client()
client.connect("localhost",1883,60)

class SampleListener(Leap.Listener):
    def on_connect(self, controller):
        controller.enable_gesture(Leap.Gesture.TYPE_CIRCLE);

tsu-nera

#!/usr/bin/env python3

import paho.mqtt.client as mqtt
from pynput.keyboard import Key, Listener

client = mqtt.Client()
client.connect("localhost",1883,60)

def on_press(key):
    print("{} is pressed".format(key))