python數(shù)據(jù)處理2

#==============================================================================

# 第2章 了解數(shù)據(jù) 15

# 2．1 簡(jiǎn)介 16

# 2．2 從CSV文件導(dǎo)入數(shù)據(jù) 16

#==============================================================================

#使用$head some_file.csv

import csv

filename = 'ch02-data.csv'

data = []

try:

#不必?fù)?dān)心在操作完資源之后去關(guān)閉數(shù)據(jù)文件，with語(yǔ)句的上下文管理器會(huì)幫助處理。

with open(filename) as f:

reader = csv.reader(f)

c = 0

for row in reader:

if c == 0:

header = row

else:

data.append(row)

c += 1

except csv.Error as e:

print "Error reading CSV file at line %s: %s" % (reader.line_num, e)

sys.exit(-1)

if header:

print header

print '=================='

for datarow in data:

print datarow

--------------------------------------------------------------------------

加載大數(shù)據(jù)文件，

import numpy

#速度較快

data=numpy.loadtxt('ch02-data.csv',dtype='string',delimiter=',')

for datarow in data:

print datarow

#更好的處理缺失數(shù)據(jù)

data=numpy.genfromtxt('ch02-data.csv',dtype='string',delimiter=',')

for datarow in data:

print datarow

#==============================================================================

# 2．3 從Microsoft Excel文件中導(dǎo)入數(shù)據(jù) 18

#==============================================================================

#讀寫(xiě)操作的支持是通過(guò)不同模塊實(shí)現(xiàn)的，跨平臺(tái)！

$ mkvirtualenv xlrdexample

(xlrdexample)$ pip install xlrd

#在指定工作簿中的工作表中，根據(jù)行數(shù)nrows和列數(shù)ncols讀取單元格的內(nèi)容:

#xlrd模塊按照需要,僅加載文件的部分內(nèi)容到內(nèi)存中

import xlrd

from xlrd.xldate import XLDateAmbiguous

file = 'ch02-xlsxdata.xlsx'

wb = xlrd.open_workbook(filename=file)

#

wb = xlrd.open_workbook(filename=file,on_demand=True)

ws = wb.sheet_by_name('Sheet1')

dataset = []

for r in ws.nrows:

col = []

for c in ws.ncols:

col.append(ws.cell(r, c).value)

#if ws.cell_type(r, c) == xlrd.XL_CELL_DATE:

# try:

# print ws.cell_type(r, c)

# from datetime import datetime

# date_value = xlrd.xldate_as_tuple(ws.cell(r, c).value, wb.datemode)

# print datetime(*date_value)

# except XLDateAmbiguous as e:

# print e

dataset.append(col)

#from pprint import pprint

#pprint(dataset)


#==============================================================================

# 2．4 從定寬數(shù)據(jù)文件導(dǎo)入數(shù)據(jù) 21

#==============================================================================

#性能更重要，或者要解析的文件非常大時(shí)，使用Python中的struct模塊

import struct

import string

mask='9s14s5s'

parse = struct.Struct(mask).unpack_from

print 'formatstring {!r}, record size: {}'

.format(mask, struct.calcsize(mask))

datafile = 'ch02-fixed-width-1M.data'

with open(datafile, 'r') as f:

for line in f:

fields = parse(line)

print 'fields: ', [field.strip() for field in fields]

#==============================================================================

# 2．5 從制表符分隔的文件中讀取數(shù)據(jù) 23

#==============================================================================

"day"? "ammount"

2013-01-24? 323

2013-01-25? 233

2013-01-26? 433

2013-01-27? 555

2013-01-28? 123

2013-01-29? ? 0

2013-01-30? 221

import struct

import string

mask='9s14s5s'

parse = struct.Struct(mask).unpack_from

print 'formatstring {!r}, record size: {}' .format(mask, struct.calcsize(mask))

datafile = 'ch02-fixed-width-1M.data'

with open(datafile, 'r') as f:

for line in f:

fields = parse(line)

print 'fields: ', [field.strip() for field in fields]

------------------------------------------------------------------

"day"? "ammount"

2013-01-24? 323

2013-01-25? 233

2013-01-26? 433

2013-01-27? 555

2013-01-28? 123

? ? 2013-01-29? ? 0

2013-01-30? 221

datafile = 'ch02-data-dirty.tab'

with open(datafile, 'r') as f:

for line in f:

# remove next comment to see line before cleanup

# print 'DIRTY: ', line.split('\t')

# we remove any space in line start or end

line = line.strip()

# now we split the line by tab delimiter

print line.split('\t')?

------------------------------------------------------------------

#==============================================================================

# 2．6 從JSON數(shù)據(jù)源導(dǎo)入數(shù)據(jù) 24

#==============================================================================

$pip install requests

import requests

import json

url = 'https://github.com/timeline.json'

r = requests.get(url)

json_obj = r.json()

print json.dumps(json_obj,sort_keys=True,indent=4)

data1 = {'b':789,'c':456,'a':123}

print json.dumps(data1,sort_keys=True,indent=4)

=============================

1.對(duì)簡(jiǎn)單數(shù)據(jù)類型的encoding 和 decoding：

使用簡(jiǎn)單的json.dumps方法對(duì)簡(jiǎn)單數(shù)據(jù)類型進(jìn)行編碼.

在json的編碼過(guò)程中，會(huì)存在從python原始類型向json類型的轉(zhuǎn)化過(guò)程:

Python? -->>>>>---? JSON? -->>>>>--- Python?

dict object dict

list,tuple array list

str,unicode string unicode

int,long,float number (int) int,long

number (real) float

True true True

False false Flase

None null None

#coding=utf-8

import json

from decimal import Decimal

obj = [[1,2,3],123,123.123,'abc',{'key1':(1,2,3),'key2':(4,5,6)}]

encodedjson = json.dumps(obj)

print repr(obj)

print encodedjson

#[[1, 2, 3], 123, 123.123, 'abc', {'key2': (4, 5, 6), 'key1': (1, 2, 3)}]

#[[1, 2, 3], 123, 123.123, "abc", {"key2": [4, 5, 6], "key1": [1, 2, 3]}]

------------------------------------------------------------------------------

json.dumps方法提供了很多好用的參數(shù)可供選擇，

比較常用的有sort_keys（對(duì)dict對(duì)象進(jìn)行排序，我們知道默認(rèn)dict是無(wú)序存放的），

separators，indent等參數(shù)。

data1 = {'b':789,'c':456,'a':123}

data2 = {'a':123,'b':789,'c':456}

d1 = json.dumps(data1,sort_keys=True)

print d1 #{"a": 123, "b": 789, "c": 456}

d2 = json.dumps(data2)

print d2 #{"a": 123, "c": 456, "b": 789}

d3 = json.dumps(data2,sort_keys=True)

print d3 #{"a": 123, "b": 789, "c": 456}

print d1==d2 False

print d1==d3 True

#indent參數(shù)是縮進(jìn)的意思，它可以使得數(shù)據(jù)存儲(chǔ)的格式變得更加優(yōu)雅。

print json.dumps(data1,sort_keys=True,indent=4)

#{

#? ? "a": 123,

#? ? "b": 789,

#? ? "c": 456

#}

------------------------------------------------------------------------

loads方法返回了原始的對(duì)象，但是仍然發(fā)生了一些數(shù)據(jù)類型的轉(zhuǎn)化。例‘a(chǎn)bc’轉(zhuǎn)化為了unicode類型

decodejson = json.loads(encodedjson)

print type(decodejson)

print decodejson[4]['key1']

print decodejson

#<type 'list'>

#[1, 2, 3]

#[[1, 2, 3], 123, 123.123, u'abc', {u'key2': (4, 5, 6), u'key1': (1, 2, 3)}]

jstring='{"name":"xue","price":12.50}'

decodejson=json.loads(jstring,parse_float=Decimal)

print decodejson

#{u'price':Decimal(12.50),u'name':u'xue'}

------------------------------------------------------------------------

json主要是作為一種數(shù)據(jù)通信的格式存在的，

而網(wǎng)絡(luò)通信是很在乎數(shù)據(jù)的大小的，無(wú)用的空格會(huì)占據(jù)很多通信帶寬，所以適當(dāng)時(shí)候也要對(duì)數(shù)據(jù)進(jìn)行壓縮。

separator參數(shù)可以起到這樣的作用，該參數(shù)傳遞是一個(gè)元組，包含分割對(duì)象的字符串。

#coding=utf-8

import json

data={'a': 123, 'c': 456, 'b': 789}

#DATA: {'a': 123, 'c': 456, 'b': 789}

print 'repr(data) :', len(repr(data))

#repr(data): 30

print 'dumps(data) :', len(json.dumps(data))

#dumps(data): 30

print 'dumps(data, indent=2):', len(json.dumps(data, indent=4))

#dumps(data, indent=2)? : 46

print 'dumps(data, separators):', len(json.dumps(data, separators=(',',':')))

#dumps(data, separators): 25

print json.dumps(data, separators=(',',':'))

#{"a":123,"c":456,"b":789}

------------------------------------------------------------------------

另一個(gè)比較有用的dumps參數(shù)是skipkeys，默認(rèn)為False。 dumps方法存儲(chǔ)dict對(duì)象時(shí)，key必須是str類型，如果出現(xiàn)了其他類型的話，那么會(huì)產(chǎn)生TypeError異常，

如果開(kāi)啟該參數(shù)，設(shè)為T(mén)rue的話，則會(huì)比較優(yōu)雅的過(guò)度。

data = {'b':789,'c':456,(1,2):123}

print json.dumps(data,skipkeys=True)

#{"c": 456, "b": 789}

------------------------------------------------------------------------

=======================

2.處理自己的數(shù)據(jù)類型

#方法二：繼承JSONEncoder和JSONDecoder類，覆寫(xiě)相關(guān)方法

import json

class Person(object):

def __init__(self,name,age):

self.name = name;

self.age = age;

def __repr__(self):

print (('Person Object name : %s , age:%d')%(self.name,self.age))

class MyEncoder(json.JSONEncoder):

def default(self,obj):

#convert object to a dict

d = {}

d['__class__'] = obj.__class__.__name__

d['__module__'] = obj.__module__

d.update(obj.__dict__)

return d

class MyDecoder(json.JSONDecoder):

def __init__(self):

json.JSONDecoder.__init__(self,object_hook=self.dict2object)

def dict2object(self,d):

#convert dict to object

if'__class__' in d:

class_name = d.pop('__class__')

module_name = d.pop('__module__')

module = __import__(module_name)

class_ = getattr(module,class_name)

args = dict((key.encode('ascii'), value) for key, value in d.items()) #get args

inst = class_(**args) #create new instance

else:

inst = d

return inst

if __name__ == '__main__':

p = Person('Peter',22)

p = Person('Peter',22)

#print p

d =MyEncoder().encode(p)

print d

o =MyDecoder().decode(d)

print? type(o)

#==============================================================================

# 2．7 導(dǎo)出數(shù)據(jù)到JSON、CSV和Excel 27

#==============================================================================

#Excel讀操作

pip install xlwt

def import_data(import_file):

? ? mask = '9s14s5s'

? ? data = []

? ? parse = struct.Struct(mask).unpack_from

? ? i =1

? ? with open(import_file, 'r') as f:

? ? ? ? for line in f:

? ? ? ? ? ? fields = parse(line)

? ? ? ? ? ? data.append(list([f.strip() for f in fields]))

? ? ? ? ? ? i=i+4

? ? ? ? ? ? if i >100:

? ? ? ? ? ? ? ? return data

? ? return data

-----------------------------------------------------------------

def write_data(data, export_format):

? ? if export_format == 'csv':

? ? ? ? return write_csv(data)

? ? elif export_format == 'json':

? ? ? ? return write_json(data)

? ? elif export_format == 'xlsx':

? ? ? ? return write_xlsx(data)

? ? else:

? ? ? ? raise Exception("Illegal format defined")

-----------------------------------------------------------------

def write_csv(data):

? ? writer = csv.writer(file('export_csv.csv', 'wb'))

? ? for row in data:

? ? ? ? writer.writerow(row)

? ? return "export_csv.csv"

-----------------------------------------------------------------

def write_json(data):

? ? with open("export_json.json", "a") as file_obj:

? ? ? ? file_obj.write(json.dumps(data))

? ? return "export_json.json"

-----------------------------------------------------------------

def write_xlsx(data):

? ? from xlwt import Workbook

? ? book = Workbook()

? ? sheet1 = book.add_sheet("Sheet 1")

? ? row = 0

? ? for line in data:

? ? ? ? col = 0

? ? ? ? for datum in line:

? ? ? ? ? ? sheet1.write(row, col, datum)

? ? ? ? ? ? col += 1

? ? ? ? row += 1

? ? ? ? # We have hard limit here of 65535 rows

? ? ? ? # that we are able to save in spreadsheet.

? ? ? ? if row > 65535:

? ? ? ? ? ? print >> sys.stderr, "Hit limit of # of rows in one sheet (65535)."

? ? ? ? ? ? break

? ? book.save("export_xlsx.xls")

? ? return "export_xlsx.xls"


#==============================================================================

# 2．8 從數(shù)據(jù)庫(kù)導(dǎo)入數(shù)據(jù) 31

#==============================================================================

$pip install sqlite3

import sqlite3

import sys

script_path = "/root/PycharmProjects/demo/world.sql"

db ='/root/PycharmProjects/demo/world.db'

# if DB is not defined ,create memory database

#db = ":memory:"

try:

con = sqlite3.connect(db)

with con:

cur = con.cursor()

with open(script_path,'rb') as f:

cur.executescript(f.read())

print("Finish")

except sqlite3.Error as err:

print "Error occured: %s" % err

-----------------------------------------------------------------

import sqlite3

import sys

db = "/root/PycharmProjects/demo/world.db"

try:

? ? con = sqlite3.connect(db)

? ? with con:

? ? ? ? cur = con.cursor()

? ? ? ? query = 'SELECT ID, Name, Population FROM City ORDER BY Population DESC LIMIT 1000'

? ? ? ? con.text_factory = str

? ? ? ? cur.execute(query)

? ? ? ? resultset = cur.fetchall()

? ? ? ? # extract column names

? ? ? ? col_names = [cn[0] for cn in cur.description]

? ? ? ? print "%10s %30s %10s" % tuple(col_names)

? ? ? ? print "="*(10+1+30+1+10)

? ? ? ? for row in resultset:

? ? ? ? ? ? print "%10s %30s %10s" % row

except sqlite3.Error as err:

? ? print "[ERROR]:", err

#==============================================================================

# 2．9 清理異常值 36

#==============================================================================

yum install libpng

pip install matplotlib

#==============================================================================

# 2．10 讀取大塊數(shù)據(jù)文件 42

#==============================================================================

#即使相當(dāng)大的文件也可輕松操作（按需加載）

with open(bigfile_path,'r') as bigfile:

for line in bigfile

#...

#按文件塊依次讀取，而不需要將整個(gè)文件讀取到內(nèi)存中

import sys

filename = "ch02-fixed-width-1M.data"

with open(filename, 'rb') as hugefile:

chunksize = 1000

readable = ''

# if you want to stop after certain number of blocks

# put condition in the while

while hugefile:

start = hugefile.tell()

for _ in range(start, start + chunksize):

file_block= hugefile.next()

print file_block

readable = readable + file_block

stop = hugefile.tell()

print ('readable? ? %s')%(readable)

print 'reading bytes from %s to %s' % (start, stop)

print 'read bytes total:', len(readable)

raw_input()

#大文件的讀取其他方案

#并行方法，如MapReduce

#多進(jìn)程處理，

#==============================================================================

# 2．11 讀取流數(shù)據(jù)源 44

#==============================================================================

##讀取一個(gè)實(shí)時(shí)變化的文件

#應(yīng)用：例如輸入是一個(gè)類文件對(duì)象或者一個(gè)遠(yuǎn)程HTTP資源，

#就可以從遠(yuǎn)程服務(wù)讀取輸入信息，并持續(xù)地解析它，然后實(shí)時(shí)地更新圖表，或者更新到中間隊(duì)列、緩沖或者數(shù)據(jù)庫(kù)

#在更為復(fù)雜的數(shù)據(jù)管道中，需要啟用消息隊(duì)列。達(dá)到的連續(xù)數(shù)據(jù)會(huì)被放在隊(duì)列里一段時(shí)間，然后才能被我們接收到。

import time

import os

import sys

with open('stream.data','r') as file:

? ? # Move to the end of file

? ? filesize = os.stat(filename)[6]

? ? file.seek(filesize)

? ? while True:

? ? ? ? where = file.tell()

? ? ? ? # try reading a line

? ? ? ? line = file.readline()

? ? ? ? # if empty, go back

? ? ? ? if not line:

? ? ? ? ? ? time.sleep(1)

? ? ? ? ? ? file.seek(where)

? ? ? ? else:

? ? ? ? ? ? # , at the end prevents print to add newline, as readline()

? ? ? ? ? ? # already read that.

? ? ? ? ? ? print line,

----------------------------------------------------------------------------

#coding=utf-8

#第一個(gè)用來(lái)讀取文件中的字節(jié)

def FileStream(filename):

? ? try:

? ? ? ? f = open(filename)

? ? ? ? for line in f:

? ? ? ? ? ? for byte in line:

? ? ? ? ? ? ? ? yield byte

? ? except StopIteration, e:

? ? ? ? f.close()

? ? ? ? return

#，第二個(gè)用來(lái)過(guò)濾流中的字節(jié)，

def FilterStream(source, condition):

? ? try:

? ? ? ? while True:

? ? ? ? ? ? byte = source.next()

? ? ? ? ? ? if condition(byte):

? ? ? ? ? ? ? ? yield byte

? ? except StopIteration, e:

? ? ? ? return

#第三個(gè)將流進(jìn)的數(shù)據(jù)打印出來(lái)。

def PrintStream(source):

? ? try:

? ? ? ? while True:

? ? ? ? ? ? byte = source.next()

? ? ? ? ? ? print byte

? ? except StopIteration, e:

? ? ? ? return

PrintStream(FilterStream(FileStream('stream.data'), str.islower))

#==============================================================================

# 2．12 導(dǎo)入圖像數(shù)據(jù)到NumPy數(shù)組 46

#==============================================================================

$pip install? scipy

#############Lena圖

#SciPy將這幅圖打包在了misc模塊中，因此可以很簡(jiǎn)單的重用這幅圖

import scipy.misc

import matplotlib.pyplot as plt

lena = scipy.misc.lena()

plt.gray()

plt.imshow(lena)

plt.colorbar()

plt.show()

print lena.shape? ? ? ? #(512, 512)

print lena.max()? ? ? ? #245

print lena.dtype? ? ? ? #int32

##########PIL

pip install PIL --allow-external PIL --allow-unverified PIL

##########放大圖像

import matplotlib.pyplot as plt

import scipy

import numpy

# because the image we loaded is RGB image,

# http://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale

bug = scipy.misc.imread('stinkbug.png')

# if you want to inspect the shape of the loaded image

# uncomment following line

print bug.shape? ? #(375, 500, 3)

bug = bug[:,:,0]# convert to gray

# show original image

plt.figure()

plt.gray()

plt.subplot(121)

plt.imshow(bug)

# show 'zoomed' region

zbug = bug[100:350,140:350]#指定放大[100:350,140:350]之間的部分矩陣

plt.subplot(122)

plt.imshow(zbug)

plt.show()

###########對(duì)于大圖像推薦使用numpy.memmap來(lái)做圖像的內(nèi)存映射

import numpy

image=numpy.memmap('stinkbug.png', dtype=int, mode='r+',shape=(375,500))

#專注于圖像處理的專業(yè)軟件包

scikit-image:http://scikit-image.org/

#==============================================================================

# 2．13 生成可控的隨機(jī)數(shù)據(jù)集合 51

#==============================================================================

############生成一個(gè)簡(jiǎn)單的隨機(jī)數(shù)樣本

import pylab

import random

SAMPLE_SIZE = 100

random.seed() # seed random generator,if no argument provided,uses system current time

real_rand_vars = [] # store generated random values here

# we don't need iterator value, so we can put call it '_'

for _ in range(SAMPLE_SIZE):

? ? new_value = random.random() # get next random value

? ? real_rand_vars.append(new_value)

# create histogram from data in 10 buckets

pylab.hist(real_rand_vars, 10)

pylab.xlabel("Number range")

pylab.ylabel("Count")

# show figure

pylab.show()

############生成虛擬價(jià)格增長(zhǎng)數(shù)據(jù)的時(shí)序圖

import pylab

import random

# days to generate data for

duration = 100

mean_inc = 0.2 # mean value

std_dev_inc = 1.2 # standard deviation

x = range(duration) # time series

y = []

for i in x:

? ? next_delta = random.normalvariate(mean_inc, std_dev_inc)

? ? price_today += next_delta

? ? y.append(price_today)

pylab.plot(x,y)

pylab.xlabel("Time")

pylab.ylabel("Value")

pylab.show()

###################不同的分布顯示直方圖

# coding: utf-8

import random

import matplotlib

import matplotlib.pyplot as plt

SAMPLE_SIZE = 1000

buckets = 100# histogram buckets

plt.figure()

matplotlib.rcParams.update({'font.size': 7})# we need to update font size just for this example

###[0.0, 1.0)之間隨機(jī)分布

plt.subplot(621)

plt.xlabel("random.random")

res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.random())

plt.hist(res, buckets)

###均勻分布[a,b]

plt.subplot(622)

plt.xlabel("random.uniform")

a = 1,b = SAMPLE_SIZE,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.uniform(a, b))

plt.hist(res, buckets)

###三角形分布

plt.subplot(623)

plt.xlabel("random.triangular")

low = 1, high = SAMPLE_SIZE, res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.triangular(low, high))

plt.hist(res, buckets)

###beta分布

plt.subplot(624)

plt.xlabel("random.betavariate")

alpha = 1, beta = 10,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.betavariate(alpha, beta))

plt.hist(res, buckets)

###指數(shù)分布

plt.subplot(625)

plt.xlabel("random.expovariate")

lambd = 1.0 / ((SAMPLE_SIZE + 1) / 2.),res = []

for _ in xrange(1, SAMPLE_SIZE): res.append(random.expovariate(lambd))

plt.hist(res, buckets)

###gamma分布

# The probability distribution function is:

#Conditions on the parameters are alpha > 0 and beta > 0.

#? ? ? ? ? x ** (alpha - 1) * math.exp(-x / beta)

# pdf(x) =? --------------------------------------

#? ? ? ? ? ? math.gamma(alpha) * beta ** alpha

plt.subplot(626)

plt.xlabel("random.gammavariate")

alpha = 1,beta = 10,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.gammavariate(alpha, beta))

plt.hist(res, buckets)

###對(duì)數(shù)正態(tài)分布Log normal distribution。mu is the mean, and sigma is the standard deviation.

plt.subplot(627)

plt.xlabel("random.lognormvariate")

mu = 1,sigma = 0.5,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.lognormvariate(mu, sigma))

plt.hist(res, buckets)

###正態(tài)分布 Normal distribution. mu is the mean, and sigma is the standard deviation.

plt.subplot(628)

plt.xlabel("random.normalvariate")

mu = 1,sigma = 0.5,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.normalvariate(mu, sigma))

plt.hist(res, buckets)

###帕累托分布，Pareto distribution. alpha is the shape parameter.

plt.subplot(629)

plt.xlabel("random.paretovariate")

alpha = 1,res = []

for _ in xrange(1, SAMPLE_SIZE):res.append(random.paretovariate(alpha))

plt.hist(res, buckets)

plt.tight_layout()

plt.show()

###########隨機(jī)數(shù)

1)用seed初始化偽隨機(jī)數(shù)生成器，這樣random()方法就能生成相同的期望隨機(jī)值。

比較有用，比預(yù)先生成隨機(jī)數(shù)并保存到到文件中要好。

2)避免隨機(jī)生成的序列重復(fù)，推薦使用random.SystemRandom,其底層使用os.urandom。

os.urandom提供更多熵源的訪問(wèn),seed()和setstate()沒(méi)有影響，則樣本不可重現(xiàn)了。

例子:想要一些隨機(jī)單詞

#Linux，針對(duì)Unix系統(tǒng)

import random

with open('/usr/share/dict/words') as f:

words=f.readlines()

words=[w.rstrip() for w in words]

for w in random.sample(words,5):

print w

#Windows

http://norvig.com/big.txt

#==============================================================================

# 2．14 真實(shí)數(shù)據(jù)的噪聲平滑處理 58

#==============================================================================

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