def shan_chu():
'''
删除学生条目
'''
l = Rf.read_file()
# print(l)
sc = input('输入需要删除的学生的姓名：')
for i in l:
if i['姓名'] == sc:
print('您要删除的是：', i['姓名'], '请确认(y/n)')
qr = input()
if qr == 'y':
shan_chu = l.pop(l.index(i))
print('删除了：', shan_chu)
# from imp import reload
# reload(get_path)
Wf.write_file(l, 'w')
return
else:
print('已放弃操作')
ts.tishi('即将返回目录', 4)
return
print('没有此人')
ts.tishi('返回目录', 5)
def train(self, training_data_file):
self.train_file_str = read_file(training_data_file)
self.train_pfs = preprocess_line(self.train_file_str)
self.train_counts = count_ngrams(3, self.train_pfs)
self.train_discounts = gt_discount(self.train_counts)
self.train_probs = estimate_probs(self.train_discounts)
write_file(self.train_probs)
def save_data(self):
content = self.text.get("1.0", tk.END)
if not content.split():
return False
#now = colored(full_date(), "cyan") #change color during seasons??
now = full_date() #think about: only time if multiple posts one day
write_file(self.name, now, response=False) #consider "\n"+now
write_file(self.name, content, response=False)
self.text.delete("1.0", tk.END) #clear all data in text box
return True
def train(self, training_data_file):
self.train_file_str = read_file(training_data_file)
self.train_pfs = preprocess_line(self.train_file_str)
self.train_counts = count_trigrams(3, self.train_pfs)
ans = raw_input("Smooth the counts? [y/n]")
if ans.lower() == 'y':
self.train_discounts = gt_discount(self.train_counts)
self.train_probs = estimate_probs(self.train_discounts)
else:
self.train_probs = estimate_probs(self.train_counts)
write_file(self.train_probs)
def main():
url = sys.argv[1] #first argument is going to be url
ip = start_host(url) #uses start_host functioni
wordlist = sys.argv[2]
print('IP Address found:' + ip)
path = os.getcwd() #defines path as current working directory
project_dir = path + '/RobustRecon: ' + ip
try:
os.mkdir(project_dir)
print('RobustRecon directory created.')
except:
print('RobustRecon directory already exists')
print("[+] Starting Web Application Firewall using wafw00f...")
waf_detect = start_wafw00f(ip)
write_file(project_dir + '/waf_detect.txt', waf_detect)
print('waf_detect.txt created')
print("[+] Starting to get a Estimate on the CMS...")
cms = (estimate_cms(ip))
write_file(project_dir + '/cms.txt', cms)
print('cms.txt created')
print("[+] Starting to bruteforce directory paths...")
brute = (bruteforce(ip, wordlist))
write_file(project_dir + '/directorybruteforce.txt', brute)
print('directorybruteforce.txt created')
print("[+] Starting to run a nikto scan")
nikto = start_nikto(url)
write_file(project_dir + '/nikto.txt', nikto)
print('nikto.txt created')
def ChiSquared_TypeandPepbondPoi(self, subspects, orginalpois, computeflag='length'):
"""
compute the chi-squared statistic for Type and Pepbond‘s poisition
Args:
-subspects: a set of subspectrum
-orginalpois: a ndarray of subbins
-computeflag: choose compute mode, include length and mass
Return:
-chiValues : a list of chisquared value
"""
choicepois = [1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1] # by people need to change
# choicepois = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] # by people
orgpois = [choicepois[i] and orginalpois[i] for i in range(len(orginalpois))]
orgpois = filter((lambda x: x>0), orgpois)
ionpbptables = self.generateIonPepbondpoiTable(subspects,orgpois, computeflag)
keyChiV = {}
chiValues = []
chi = ChiSquared()
for key in ionpbptables:
# print key
table = np.array(ionpbptables[key].T)
chiValue = chi.OrgainChiSquard(table)
chiValues.append(chiValue)
keyChiV[key] = chiValue
fw = write_file()
filename = "SubSpectrumData/"+"pepbondpoi_Chivalue"
fw.writeFile_cp(filename, keyChiV)
return chiValues
def top_by_country():
#Create the directory to store the results
if not path.exists(directory):
makedirs(directory)
#path of the output result
file_path = directory + "/" + "country_top50_" + date + ".txt"
#Sort the ISO code list of the countries to get a sorted result file
#The list is sorted by ISO code and not by country
current = (s for s in sorted(list(countries), key=lambda c: c.alpha_2))
result = []
#Browse the sorted list of ISO code to compute the result sorted by code
for country in current:
try:
c = country.alpha_2
#First check if there are streams from the current country
#countries_list[c] contains all the listening of c
#Counter() will count all the repetition of each song id
#write_file function will return a string which will be stored in result
#result is the variable which will be written in the output file
if (c in countries_list):
result.append(write_file(Counter(countries_list[c]), c))
except:
print("Error with", c.name)
#We make only one write operation to reduce the execution time
open(file_path, "w").write("\n".join(result))
print("Top 50 songs by country has been written for the", log_name, "file")
def ChiSquared_TypeandType(self, subspects, orginalpois):
"""
compute the chi-squared statistic for Type and Type
Args:
-subspects: a set of subspectrum
-orginalpois: a ndarray of subbins
Return:
-chiValues : a list of chisquared value
"""
choicepois = [
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1
] # by people
# choicepois = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] # by people
orgpois = [
choicepois[i] and orginalpois[i] for i in range(len(orginalpois))
]
orgpois = filter((lambda x: x > 0), orgpois)
iontables = self.generateIonTable(subspects, orgpois)
fw = write_file()
filename = "SubSpectrumData/" + "typetype_iontable"
fw.writeFile_cp(filename, iontables)
chiValues = {}
chi = ChiSquared()
for key in iontables:
table = np.array(iontables[key].T)
chiValue = chi.OrgainChiSquard(table)
chiValues[key] = chiValue
return chiValues
def ChiSquared_TypeandType(self, subspects, orginalpois):
"""
compute the chi-squared statistic for Type and Type
Args:
-subspects: a set of subspectrum
-orginalpois: a ndarray of subbins
Return:
-chiValues : a list of chisquared value
"""
choicepois = [1,0,1,1,1,0,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1] # by people
# choicepois = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] # by people
orgpois = [choicepois[i] and orginalpois[i] for i in range(len(orginalpois))]
orgpois = filter((lambda x: x>0), orgpois)
iontables = self.generateIonTable(subspects,orgpois)
fw = write_file()
filename = "SubSpectrumData/"+"typetype_iontable"
fw.writeFile_cp(filename, iontables)
chiValues = {}
chi = ChiSquared()
for key in iontables:
table = np.array(iontables[key].T)
chiValue = chi.OrgainChiSquard(table)
chiValues[key] = chiValue
return chiValues
def xiu_gai():
'''
修改学生条目
'''
xg = input('输入需要修改的学生的姓名：')
l = r_f.read_file()
count = 0
for i in l:
if i['姓名'] == xg:
count += 1
print('您要修改的是：', '姓名：%s，年龄：%s，成绩：%s' % (i['姓名'], i['年龄'], i['成绩']))
print(
'\n 要修改：\n 姓名 输入1 \n 年龄 输入2 \n 成绩 输入3 \n 可自由修改需要的信息，修改全部输入123')
qr = input()
if '1' in qr:
Nname = input('输入新的名字：')
i['姓名'] = Nname
if '2' in qr:
Nage = input('输入新的年龄：')
i['年龄'] = Nage
if '3' in qr:
Nscore = input('输入新的成绩：')
i['成绩'] = Nscore
break
if count == 0:
print('没有这个人哦')
print('重新输入吗？（y/n）')
s0 = input(' ')
if s0 == 'y':
xiu_gai()
return
else:
return
else:
# from imp import reload
# reload(get_path)
w_f.write_file(l, 'w')
return
def top_by_user():
#Create the directory to store the results
if not path.exists(directory):
makedirs(directory)
#If the file has alreadey been computed we remove it
#We can't use the "w" in the write function because write information
#in the loop and we have to use "a"
file_path = directory + "/" + "user_top50_" + date + ".txt"
if path.exists(file_path):
remove(file_path)
#range(30) is used to be sure to get all saved information by the log_reader script
#this is because we do not know the exact number if we execute the two scripts in a row
#to avoid useless round we make a breaking loop if the file number i does not exist
for i in range(30):
#Create the path of the file
users_path = data_directory + "/usersStream_" + str(
i) + "_" + date + ".npy"
#Get the extracted data from the saved dictionary
if path.exists(users_path):
user_tmp = np.load(users_path, allow_pickle=True)
#Transform the ridden data to dictionary
users_list = user_tmp.item()
else:
break
#Sort the id of the users to get a sorted result file
current = (s for s in sorted(users_list))
#result where the content to write will be stored
result = []
#For each user its result is precessed
for user in current:
try:
#users_list[user] contains all the listening of user
#Counter() will count all the repetition of each song id
#write_file function will return a string which will be stored in result
#result is the variable which will be written in the output file
result.append(write_file(Counter(users_list[user]), user))
except:
print("Error with user number", user)
#We make only few writes operations to reduce the execution time
open(file_path, "a").write("\n".join(result))
print("Top 50 songs by user has been written for the", log_name, "file")
def startprocessing(self, title, text):
"""Starts Processing the title and text to create tf-idf for the current document"""
docid = self.count
freq = processtext(docid, text)
titlefreq = processtitle(docid, title)
f = open('titles.txt', 'a+')
if self.first == 1:
f.write('\n')
if self.first == 0:
self.first = 1
tr = str(docid) + ' ' + title
f.write(tr.encode('utf-8'))
f.close()
for word in titlefreq:
if word in freq:
freq[word]['t'] += titlefreq[word]
else:
freq[word] = dict(d=docid,
t=titlefreq[word],
b=0,
i=0,
c=0,
l=0,
r=0)
for word in freq:
if len(word) < 3 or word.startswith('0'):
continue
freq[word]['d'] = str(docid)
if word not in self.invindex:
self.invindex[word] = list()
self.invindex[word].append(''.join(tag + str(freq[word][tag])
for tag in freq[word]
if freq[word][tag] != 0))
if self.count % 1000 == 0:
write_file(self.invindex, self.filecount, sys.argv[2])
self.invindex = dict()
self.filecount += 1
def ChiSquared_TypeandPepbondPoi(self,
subspects,
orginalpois,
computeflag='length'):
"""
compute the chi-squared statistic for Type and Pepbond‘s poisition
Args:
-subspects: a set of subspectrum
-orginalpois: a ndarray of subbins
-computeflag: choose compute mode, include length and mass
Return:
-chiValues : a list of chisquared value
"""
choicepois = [
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1
] # by people need to change
# choicepois = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] # by people
orgpois = [
choicepois[i] and orginalpois[i] for i in range(len(orginalpois))
]
orgpois = filter((lambda x: x > 0), orgpois)
ionpbptables = self.generateIonPepbondpoiTable(subspects, orgpois,
computeflag)
keyChiV = {}
chiValues = []
chi = ChiSquared()
for key in ionpbptables:
# print key
table = np.array(ionpbptables[key].T)
chiValue = chi.OrgainChiSquard(table)
chiValues.append(chiValue)
keyChiV[key] = chiValue
fw = write_file()
filename = "SubSpectrumData/" + "pepbondpoi_Chivalue"
fw.writeFile_cp(filename, keyChiV)
return chiValues
print orginalpois
# file_name = 'data/new_CHPP_LM3_RP3_2.mgf'
# parser = SpectrumParser()
# specs = list(parser.readSpectrum(file_name)) # orignal datas file
#
# spectMaxInt = iglearner.generateMaxIntentity(specs)
file_name3 = "SubSpectrumData/"+"SpectMaxInt"
spectMaxInt = iglearner.generateMaxIntentityFile(file_name3)
ionLists = iglearner.generateIonGroup_Int(atypesubspects, orginalpois, spectMaxInt)
file_name4 = "SubSpectrumData/"+"IonGroups_AtypeInt"
wfile = write_file()
# wfile.writeSpectMaxInt(spectMaxInt)
# iglearner.paintMaxInt(spectMaxInt)
# wfile.writeIonPoi(orginalpois)
wfile.writeIonGroups(ionLists, file_name4)
#
## ionLists = iglearner.generateIonGroup(subspects, orginalpois)
#
################################ Test 5#####################################
# names=['y10+ ']
# for name in names:
# filename1 = "SubSpectrumData/"+"new_CHPP_LM3_RP3_2_"+name+"_intensity_20160120"
# subparser = SubSpectrumGenerator()
# atypesubspects = list(subparser.generateSubSpecfile(filename1,'intensity'))
#
if word not in self.invindex:
self.invindex[word] = list()
self.invindex[word].append(''.join(tag + str(freq[word][tag])
for tag in freq[word]
if freq[word][tag] != 0))
if self.count % 1000 == 0:
write_file(self.invindex, self.filecount, sys.argv[2])
self.invindex = dict()
self.filecount += 1
if __name__ == "__main__":
try:
os.remove('titles.txt')
except OSError as e:
if e.errno != errno.ENOENT:
raise
if not os.path.exists('./tmp/'):
try:
os.makedirs('./tmp/')
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
parser = xml.sax.make_parser()
Handler = IndexGenerator()
parser.setContentHandler(Handler)
parser.parse(sys.argv[1])
if Handler.count % 1000 > 0:
write_file(Handler.invindex, Handler.filecount, sys.argv[2])
Handler.filecount += 1
mergefiles(Handler.filecount, 'tmp')
print orginalpois
# file_name = 'data/new_CHPP_LM3_RP3_2.mgf'
# parser = SpectrumParser()
# specs = list(parser.readSpectrum(file_name)) # orignal datas file
#
# spectMaxInt = iglearner.generateMaxIntentity(specs)
file_name3 = "SubSpectrumData/" + "SpectMaxInt"
spectMaxInt = iglearner.generateMaxIntentityFile(file_name3)
ionLists = iglearner.generateIonGroup_Int(atypesubspects, orginalpois,
spectMaxInt)
file_name4 = "SubSpectrumData/" + "IonGroups_AtypeInt"
wfile = write_file()
# wfile.writeSpectMaxInt(spectMaxInt)
# iglearner.paintMaxInt(spectMaxInt)
# wfile.writeIonPoi(orginalpois)
wfile.writeIonGroups(ionLists, file_name4)
#
## ionLists = iglearner.generateIonGroup(subspects, orginalpois)
#
################################ Test 5#####################################
# names=['y10+ ']
# for name in names:
# filename1 = "SubSpectrumData/"+"new_CHPP_LM3_RP3_2_"+name+"_intensity_20160120"
# subparser = SubSpectrumGenerator()
# atypesubspects = list(subparser.generateSubSpecfile(filename1,'intensity'))
#
def writetoFile(self, spects, filename):
wf = write_file()
wf.writeSampleFile(spects, filename)
import Tkinter as tk
import tkFileDialog
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from write_file import write_file
from SubSpectrumProcessor import SubSpectrumProcessor
from SubSpectrumGenerator import SubSpectrumGenerator
from SpectrumParser import SpectrumParser
from PeptideProcessor import PeptideProcessor
from IonGroupLearner import IonGroupLearner
from SpectrumProcessor import SpectrumProcessor
import os
fw = write_file()
class showproject(object):
def __init__(self):
self.root = tk.Tk()
self.root.title("Show Project")
# self.root.geometry('470x320')
# menu
self.menu = tk.Menu(self.root)
# menu initial
self.menuTest()
self.root.config(menu=self.menu)
# drawpiture
def write(self, option, account):
return write_file(option, account)
import read_file
import scoring
import write_file
books = []
libraries = []
nbDays = read_file.reading_file("SubmitInput/b_read_on.txt", books, libraries)
#print(books)
#print(libraries)
#librarie = libraries[:]
libToScan = scoring.scoreAllLibs(libraries, nbDays)
#print(libToScan)
write_file.write_file(libToScan)
from write_file import write_file
if __name__ == '__main__':
write_file('file')
print('***')
write_file('file.txt')
print('***')
write_file('file', 'Hello there')
print('***')
write_file('new_file.txt', 'Hello there')
print('***')
# check with a non-empty file
write_file('example.txt', 'A new message')
print('***')
def choose_option():
myobj = dbclassPY.DB_communication()
print("Hey Hoh, what do you want to do?:\n")
if myobj.runningval == 1:
print("db accessible")
inputvar = "init"
while inputvar != "exit":
inputvar = input(
"<make> entry, <read> db, <export> , <barplot> or <exit>\n")
if inputvar == "make":
title = input("Titel:\n")
description = input("Description:\n")
person = input("Person:\n")
datestart = input("(optional)Start d m Y:\n")
try:
start = datetime.datetime.strptime(datestart,
"%d %m %Y").date()
except:
start = datetime.date.today()
print(start)
myobj.insert_entry(title, person, description, start)
elif inputvar == "read":
entrylist = myobj.read_all()
print(entrylist)
elif inputvar == "export":
data = myobj.file_export()
try:
write_file.write_file(data)
rsubcall.rsub_call()
except:
print("fail... export")
elif inputvar == "barplot":
show_bar.show_barplot()
#print(os.getcwd())
#myobj.export_csv()
#expobj = class_exporter.Exporter()
#expobj.export_query_txt(content)
elif inputvar.startswith("read "):
try:
words = inputvar.split()
if len(words) > 1:
if words[1] == "opentasks":
datalist = myobj.filter_open()
elif words[1] == "all":
datalist = myobj.read_all()
else:
datalist = myobj.filter_person(words[1])
if len(words) > 2:
if words[2] == "txt":
name = words[1]
write_file.write_file(datalist, name)
else:
write_file.write_cmd(datalist)
except:
print("invalid input...\n")
elif inputvar == "cls":
cmd_commands.cls()
else:
print("local mode:")
inputvar = "init"
while inputvar != "exit":
inputvar = input("<export>, <barplot> or <exit>\n")
if inputvar == "export":
try:
rsubcall.rsub_call()
except:
print("writing failure\n")
elif inputvar == "barplot":
show_bar.show_barplot()
## main.py
## Gets words from corncob list and applies "proper" ending
from read_words import read_words
from find_by_endings import find_by_endings
from make_correct_endings import make_correct_endings
from show_off import show_off
from write_file import write_file
words = read_words("corncob_lowercase.txt")
latin_us = find_by_endings(words, "us")
latin_us_plurals = make_correct_endings(latin_us, "us", "i")
show_off_with_latin_plurals = show_off(latin_us, latin_us_plurals, "plural")
write_file("latin_plurals.txt", show_off_with_latin_plurals)
def writetoFile(self, spects, filename):
wf = write_file()
wf.writeSampleFile(spects,filename)
from parser import parse
from write_file import write_file
from bootstrap import createBootstrap
# converts relative jump instructions to absolute jump instructions
from relative_instruction import map_relative_instructions
fileOrPath = sys.argv[1]
# initialize string to have bootstrap code at beginning
concatenatedFiles = createBootstrap()
# if argument is a directory, convert all .vm files to .asm
try:
folderName = os.path.basename(os.path.normpath(fileOrPath))
# if listdir throws an exception, then the argument was a file not a directory
fileList = os.listdir(fileOrPath)
for fileName in fileList:
if (fileName.endswith('.vm')):
concatenatedFiles = concatenatedFiles + parse(open(fileOrPath + fileName, 'r').read(), fileName)
# maps the relative instructions to absolute jump instruction lines in assembly
concatenatedFiles = map_relative_instructions(concatenatedFiles)
write_file(concatenatedFiles, fileOrPath + folderName + '.vm')
# if argument is a single file, convert it to .as
except:
translatedCode = parse(open(fileOrPath, 'r').read(), os.path.basename(fileOrPath))
write_file(translatedCode, fileOrPath)
import Tkinter as tk
import tkFileDialog
import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from write_file import write_file
from SubSpectrumProcessor import SubSpectrumProcessor
from SubSpectrumGenerator import SubSpectrumGenerator
from SpectrumParser import SpectrumParser
from PeptideProcessor import PeptideProcessor
from IonGroupLearner import IonGroupLearner
from SpectrumProcessor import SpectrumProcessor
import os
fw = write_file()
class showproject(object):
def __init__(self):
self.root = tk.Tk()
self.root.title("Show Project")
# self.root.geometry('470x320')
# menu
self.menu = tk.Menu(self.root)
# menu initial
self.menuTest()
self.root.config(menu=self.menu)
# drawpiture
def edit_users(self, number, name, pin, type, cc, balance, file):
write_file()