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# coding: utf-8

def synthesize_text(text, path):
    import os
    from google.cloud import texttospeech
    from pydub import AudioSegment

    client = texttospeech.TextToSpeechClient()

    input_text = texttospeech.types.SynthesisInput(text=text)

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# coding: utf-8

import re
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.feature_extraction import DictVectorizer as DV
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

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# データの入手
# Data obtained from http://biostat.mc.vanderbilt.edu/DataSets
data = read.csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv",
                stringsAsFactors=F, na.strings=c("","NA"))

# データの確認
# survived: 1（生存），0（死亡）
# pclass: 乗客の社会経済的地位（1:上流，2:中流，3:下流）
# name: 氏名
# sex: 性別

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# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

fp = FontProperties(fname=r'C:\WINDOWS\Fonts\YuGothic.ttf', size=14)

plt.bar([1, 2], [5, 10], 0.25)
plt.bar([1.25, 2.25], [4, 8], 0.25, color='darkorange')
plt.xlim((0.75, 2.75))