搜索
首页后端开发Python教程使用python绘制人人网好友关系图示例

代码依赖:networkx matplotlib

复制代码 代码如下:

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

import urllib
import urllib2
import cookielib
import re
import cPickle as p
import networkx as nx
import matplotlib.pyplot as plt

__author__ = """Reverland (lhtlyy@gmail.com)"""

# Control parameters,EDIT it here
## Login
username = 'None'
password = 'None'
## Control Graphs, Edit for better graphs as you need
label_flag = True # Whether shows labels.NOTE: configure your matplotlibrc for Chinese characters.
remove_isolated = True # Whether remove isolated nodes(less than iso_level connects)
different_size = True # Nodes for different size, bigger means more shared friends
iso_level = 10
node_size = 40 # Default node size

 
def login(username, password):
    """log in and return uid"""
    logpage = "http://www.renren.com/ajaxLogin/login"
    data = {'email': username, 'password': password}
    login_data = urllib.urlencode(data)
    cj = cookielib.CookieJar()
    opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cj))
    urllib2.install_opener(opener)
    res = opener.open(logpage, login_data)
    print "Login now ..."
    html = res.read()
    #print html

    # Get uid
    print "Getting user id of you now"
    res = urllib2.urlopen("http://www.renren.com/home")
    html = res.read()
    # print html
    uid = re.search("'ruid':'(\\d+)'", html).group(1)
    # print uid
    print "Login and got uid successfully"
    return uid

 
def getfriends(uid):
    """Get the uid's friends and return the dict with uid as key,name as value."""
    print "Get %s 's friend list" % str(uid)
    pagenum = 0
    dict1 = {}
    while True:
        targetpage = "http://friend.renren.com/GetFriendList.do?curpage=" + str(pagenum) + "&id=" + str(uid)
        res = urllib2.urlopen(targetpage)
        html = res.read()

        pattern = '[\\S]*[\\s]\\((.*)\\)'

        m = re.findall(pattern, html)
        #print len(m)
        if len(m) == 0:
            break
        for i in range(0, len(m)):
            no = m[i][0]
            uname = m[i][1]
            #print uname, no
            dict1[no] = uname
        pagenum += 1
    print "Got %s 's friends list successfully." % str(uid)
    return dict1

 
def getdict(uid):
    """cache dict of uid in the disk."""
    try:
        with open(str(uid) + '.txt', 'r') as f:
            dict_uid = p.load(f)
    except:
        with open(str(uid) + '.txt', 'w') as f:
            p.dump(getfriends(uid), f)
        dict_uid = getdict(uid)
    return dict_uid

 
def getrelations(uid1, uid2):
    """receive two user id, If they are friends, return 1, otherwise 0."""
    dict_uid1 = getdict(uid1)
    if uid2 in dict_uid1:
        return 1
    else:
        return 0

 
def getgraph(username, password):
    """Get the Graph Object and return it.
You must specify a Chinese font such as `SimHei` in ~/.matplotlib/matplotlibrc"""
    uid = login(username, password)
    dict_root = getdict(uid) # Get root tree

    G = nx.Graph() # Create a Graph object
    for uid1, uname1 in dict_root.items():
        # Encode Chinese characters for matplotlib **IMPORTANT**
        # if you want to draw Chinese labels,
        uname1 = unicode(uname1, 'utf8')
        G.add_node(uname1)
        for uid2, uname2 in dict_root.items():
            uname2 = unicode(uname2, 'utf8')
            # Not necessary for networkx
            if uid2 == uid1:
                continue
            if getrelations(uid1, uid2):
                G.add_edge(uname1, uname2)

    return G

 
def draw_graph(username, password, filename='graph.txt', label_flag=True, remove_isolated=True, different_size=True, iso_level=10, node_size=40):
    """Reading data from file and draw the graph.If not exists, create the file and re-scratch data from net"""
    print "Generating graph..."
    try:
        with open(filename, 'r') as f:
            G = p.load(f)
    except:
        G = getgraph(username, password)
        with open(filename, 'w') as f:
            p.dump(G, f)
    #nx.draw(G)
    # Judge whether remove the isolated point from graph
    if remove_isolated is True:
        H = nx.empty_graph()
        for SG in nx.connected_component_subgraphs(G):
            if SG.number_of_nodes() > iso_level:
                H = nx.union(SG, H)
        G = H
    # Ajust graph for better presentation
    if different_size is True:
        L = nx.degree(G)
        G.dot_size = {}
        for k, v in L.items():
            G.dot_size[k] = v
        node_size = [G.dot_size[v] * 10 for v in G]
    pos = nx.spring_layout(G, iterations=50)
    nx.draw_networkx_edges(G, pos, alpha=0.2)
    nx.draw_networkx_nodes(G, pos, node_size=node_size, node_color='r', alpha=0.3)
    # Judge whether shows label
    if label_flag is True:
        nx.draw_networkx_labels(G, pos, alpha=0.5)
    #nx.draw_graphviz(G)
    plt.show()

    return G

if __name__ == "__main__":
    G = draw_graph(username, password)
声明
本文内容由网友自发贡献,版权归原作者所有,本站不承担相应法律责任。如您发现有涉嫌抄袭侵权的内容,请联系admin@php.cn
Python:编译器还是解释器?Python:编译器还是解释器?May 13, 2025 am 12:10 AM

Python是解释型语言,但也包含编译过程。1)Python代码先编译成字节码。2)字节码由Python虚拟机解释执行。3)这种混合机制使Python既灵活又高效,但执行速度不如完全编译型语言。

python用于循环与循环时:何时使用哪个?python用于循环与循环时:何时使用哪个?May 13, 2025 am 12:07 AM

useeAforloopWheniteratingOveraseQuenceOrforAspecificnumberoftimes; useAwhiLeLoopWhenconTinuingUntilAcIntiment.ForloopSareIdeAlforkNownsences,而WhileLeleLeleLeleLoopSituationSituationSituationsItuationSuationSituationswithUndEtermentersitations。

Python循环:最常见的错误Python循环:最常见的错误May 13, 2025 am 12:07 AM

pythonloopscanleadtoerrorslikeinfiniteloops,modifyingListsDuringteritation,逐个偏置,零indexingissues,andnestedloopineflinefficiencies

对于循环和python中的循环时:每个循环的优点是什么?对于循环和python中的循环时:每个循环的优点是什么?May 13, 2025 am 12:01 AM

forloopsareadvantageousforknowniterations and sequests,供应模拟性和可读性;而LileLoopSareIdealFordyNamicConcitionSandunknowniterations,提供ControloperRoverTermination.1)forloopsareperfectForeTectForeTerToratingOrtratingRiteratingOrtratingRitterlistlistslists,callings conspass,calplace,cal,ofstrings ofstrings,orstrings,orstrings,orstrings ofcces

Python:深入研究汇编和解释Python:深入研究汇编和解释May 12, 2025 am 12:14 AM

pythonisehybridmodelofcompilationand interpretation:1)thepythoninterspretercompilesourcececodeintoplatform- interpententbybytecode.2)thepytythonvirtualmachine(pvm)thenexecuteCutestestestesteSteSteSteSteSteSthisByTecode,BelancingEaseofuseWithPerformance。

Python是一种解释或编译语言,为什么重要?Python是一种解释或编译语言,为什么重要?May 12, 2025 am 12:09 AM

pythonisbothinterpretedAndCompiled.1)它的compiledTobyTecodeForportabilityAcrosplatforms.2)bytecodeisthenInterpreted,允许fordingfordforderynamictynamictymictymictymictyandrapiddefupment,尽管Ititmaybeslowerthananeflowerthanancompiledcompiledlanguages。

对于python中的循环时循环与循环:解释了关键差异对于python中的循环时循环与循环:解释了关键差异May 12, 2025 am 12:08 AM

在您的知识之际,而foroopsareideal insinAdvance中,而WhileLoopSareBetterForsituations则youneedtoloopuntilaconditionismet

循环时:实用指南循环时:实用指南May 12, 2025 am 12:07 AM

ForboopSareSusedwhenthentheneMberofiterationsiskNownInAdvance,而WhileLoopSareSareDestrationsDepportonAcondition.1)ForloopSareIdealForiteratingOverSequencesLikelistSorarrays.2)whileLeleLooleSuitableApeableableableableableableforscenarioscenarioswhereTheLeTheLeTheLeTeLoopContinusunuesuntilaspecificiccificcificCondond

See all articles

热AI工具

Undresser.AI Undress

Undresser.AI Undress

人工智能驱动的应用程序,用于创建逼真的裸体照片

AI Clothes Remover

AI Clothes Remover

用于从照片中去除衣服的在线人工智能工具。

Undress AI Tool

Undress AI Tool

免费脱衣服图片

Clothoff.io

Clothoff.io

AI脱衣机

Video Face Swap

Video Face Swap

使用我们完全免费的人工智能换脸工具轻松在任何视频中换脸!

热门文章

热工具

安全考试浏览器

安全考试浏览器

Safe Exam Browser是一个安全的浏览器环境,用于安全地进行在线考试。该软件将任何计算机变成一个安全的工作站。它控制对任何实用工具的访问,并防止学生使用未经授权的资源。

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

功能强大的PHP集成开发环境

记事本++7.3.1

记事本++7.3.1

好用且免费的代码编辑器

VSCode Windows 64位 下载

VSCode Windows 64位 下载

微软推出的免费、功能强大的一款IDE编辑器

MinGW - 适用于 Windows 的极简 GNU

MinGW - 适用于 Windows 的极简 GNU

这个项目正在迁移到osdn.net/projects/mingw的过程中,你可以继续在那里关注我们。MinGW:GNU编译器集合(GCC)的本地Windows移植版本,可自由分发的导入库和用于构建本地Windows应用程序的头文件;包括对MSVC运行时的扩展,以支持C99功能。MinGW的所有软件都可以在64位Windows平台上运行。