#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np

# 等同于MATLAB中的smooth函數，但是平滑窗口必須為奇數。

# yy = smooth(y) smooths the data in the column vector y ..
# The first few elements of yy are given by
# yy(1) = y(1)
# yy(2) = (y(1) + y(2) + y(3))/3
# yy(3) = (y(1) + y(2) + y(3) + y(4) + y(5))/5
# yy(4) = (y(2) + y(3) + y(4) + y(5) + y(6))/5
# ...

def smooth(a,WSZ):
  # a:原始數據，NumPy 1-D array containing the data to be smoothed
  # 必須是1-D的，如果不是，請使用 np.ravel()或者np.squeeze()轉化 
  # WSZ: smoothing window size needs, which must be odd number,
  # as in the original MATLAB implementation
  out0 = np.convolve(a,np.ones(WSZ,dtype=int),'valid')/WSZ
  r = np.arange(1,WSZ-1,2)
  start = np.cumsum(a[:WSZ-1])[::2]/r
  stop = (np.cumsum(a[:-WSZ:-1])[::2]/r)[::-1]
  return np.concatenate(( start , out0, stop ))

# another one，邊緣處理的不好

"""
def movingaverage(data, window_size):
  window = np.ones(int(window_size))/float(window_size)
  return np.convolve(data, window, 'same')
"""

# another one，速度更快
# 輸出結果 不與原始數據等長，假設原數據為m，平滑步長為t，則輸出數據為m-t+1

"""
def movingaverage(data, window_size):
  cumsum_vec = np.cumsum(np.insert(data, 0, 0)) 
  ma_vec = (cumsum_vec[window_size:] - cumsum_vec[:-window_size]) / window_size
  return ma_vec
"""