sinhrks

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import matplotlib.animation as animation

# rpy2 経由で R から iris をロード
# import pandas.rpy.common as com
# iris = com.load_data('iris')

sinhrks

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import matplotlib.animation as animation

# rpy2 経由で R から iris をロード
# import pandas.rpy.common as com
# iris = com.load_data('iris')

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library(dplyr)
library(tidyr)
library(ggplot2)
library(gridExtra)
library(animation)

plot_dtw_matrix <- function(ts_a, ts_b, i, j, cost, dist) {
  
  .plot_matrix <- function(m, title, low, high) {
    d <- dplyr::tbl_df(data.frame(m))

sinhrks

set.seed(1)

# 観測系列のサンプルサイズ
n <- 30

# 加速度
a <-rep(0.3, n)

v <- cumsum(0.5 * a)
x <- cumsum(v)

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set.seed(1)

# 観測系列のサンプルサイズ
n <- 100

# 真の値
x <- c(rep(0, n / 4), seq(0, 10, length.out = n / 4),
       rep(10, n / 4), seq(10, 0, length.out = n / 4))
y <- c(seq(0, 10, length.out = n / 4), rep(10, n / 4),
       seq(10, 0, length.out = n / 4), rep(0, n / 4))

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set.seed(1)

# 観測系列のサンプルサイズ
n <- 200

# 真の値が時間変化する
actual <- 3.0 + cumsum(rnorm(n, sd = 0.2))

# 観測される値 (誤差は標準偏差2の正規分布とする)
observed <- actual + rnorm(n, mean = 0, sd = 2)

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set.seed(1)

# 観測系列のサンプルサイズ
n <- 120

# 真の値
actual <- rep(5, out.length = n)

# 観測される値 (誤差は標準偏差2の正規分布とする)
observed <- actual + rnorm(n, mean = 0, sd = 2)

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library(survival)

library(ggplot2)
library(scales)

d.survfit <- survival::survfit(survival::Surv(time, status) ~ sex,
                               data = lung)

fortify.survfit <- function(survfit.data) {
  data.frame(time = survfit.data$time,

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library(forecast)
library(ggplot2)

d <- AirPassengers
d.arima <- forecast::auto.arima(d)
d.forecast <- forecast(d.arima, level = c(95), h = 50)

fortify.forecast <- function(forecast.data) {
  require(dplyr)
  forecasted <- as.data.frame(forecast.data)

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import pandas as pd
import numpy as np
import pandas.util.testing as tm
import matplotlib.pyplot as plt

import pandas as pd
import numpy as np

series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')