#!/usr/bin/python2.7

# Fetch data from BD and analyse.

import json

import urllib

import traceback

import numpy as np

# import pandas as pd

import matplotlib.pyplot as plt

#from scipy import stats

def fetch_raw_data(url):

    try:

        response = urllib.urlopen(url).read().decode('utf-8')

        return json.loads(response)

    except Exception, e:

        err = traceback.format_exc()

        print("fetch_raw_data err: {}".format(err))

# 移动平均算法

def moving_average(f_t):

    if type(f_t) is not np.ndarray:

        raise TypeError\

            ('Expected one dimensional numpy array.')

    if f_t.shape[1] != 1:

        raise IndexError\

            ('Expected one dimensional numpy array, %d dimensions given.' % (f_t.shape[1]))

    f_t = f_t.flatten()

    window = 5

    mode = 'same'

    g_t = np.ones(int(window))/float(window)

    # Deal with boundaries with atleast lag/2 day window

    # ma = np.convolve(f_t,g_t,mode)

    # ma = np.convolve(f_t,g_t,mode)[window-1:-window+1]

    ma = np.convolve(f_t,g_t)[window-1:-window+1]

    return ma

def raw_data():

    start_ts = 1533204000

    stop_ts = 1533222000

    url = 'http://8.8.8.8/path/data?begin_time={}&end_time={}&type=asia'

    url = url.format(start_ts,stop_ts)

    result = fetch_raw_data(url)

    # downloadspeed_lst = result['result']['downloadspeed']

    downloadspeed_lst = result['result']['totaluploadspeed']

    downloadspeed_lst = [ [ele,] for ele in downloadspeed_lst ]

    return downloadspeed_lst

def run(downloadspeed_lst):

    downloadspeed_ndarray = np.array(downloadspeed_lst)

    ma = moving_average(downloadspeed_ndarray)

    return ma

data = raw_data()

ma = run(data)

t = np.arange(4, len(data))

plt.plot(t, data[4:], lw=1.0)

plt.plot(t, ma, lw=1.0)

plt.show()

import numpy as np

from matplotlib import pyplot as plt

def moving_average(array, window=3):

    N = window

    n=np.ones(N)

    weights=n/N

    sma=np.convolve(weights,array)[N-1:-N+1]

    t=np.arange(N-1,len(array))

    plt.plot(t,array[N-1:],lw=1)

    plt.plot(t,sma,lw=2)

    plt.show()

    return sma

numpy.convolve(weights,array)[N-1:-N+1]

weight = [a,b,c]

array = [i,j,k,m,n]

Result：

[ai, bi+aj, ci+bj+ak, cj+bk+am, ck+bm+an, cm+bn, cn][N-1:-N+1]