Saravanan666/pypi-org huggingface dataset:
Package: nessus-file-analyzer
nessus file analyzernessus file analyzerby LimberDuck (pronouncedˈlɪm.bɚ dʌk) is a GUI
tool which enables you to parse multiple nessus files containing the results
of scans performed by using Nessus by (C) Tenable, Inc. and exports parsed
data to a Microsoft Excel Workbook for effortless analysis.Operational memory usage will be kept low while parsing even the largest of
files. You can run it on your favourite operating system, whether it is Windows,
macOS or GNU Linux. As a parsing result, you will receive spreadsheets with a
summary view of the whole scan and/or all reported hosts. You will also be
able to generate spreadsheets with a detailed view of all reported
vulnerabilities and/or noncompliance.
It's free and open source tool, which has been created to automate our work,
decrease our workload and focus on data analysis.Main featuresselect one or more nessus files at once or select directory to get all nessus files from it and from all its subdirectoriesselect one or more of available report types like:scan,host,vulnerabilities,noncompliancechange target directory for output file to desired one, leave it default (current working directory) or set to be the same as source filesadd suffix for output file with_YYYYMMDD_HHMMSSand/or custom textDocumentationVisithttps://nessus-file-analyzer.readthedocs.ioto find out more.InstallationNote:It's advisable to use python virtual environment for below instructions. Read more about python virtual environment inThe Hitchhiker’s Guide to Python!Read aboutvirtualenvwrapper in The Hitchhiker’s Guide to Python!:virtualenvwrapperprovides a set of commands which makes working with virtual environments much more pleasant.Installnessus file analyzerpip install nessus-file-analyzerTo upgrade to newer version run:pip install -U nessus-file-analyzerRunnessus file analyzernessus-file-analyzerOptionally for Linux and macOS:nessus-file-analyzer&Run with&at the end to start the process in the background.Make a shortcut fornessus file analyzerWindows:Run in cmdwhere nessus-file-analyzer.exeCopy returned path.Go to e.g. to Desktop.Right click on Desktop and chooseNew > Shortcut.Paste returned path.ClickNext,Finish.Linux (Ubuntu) / macOSRun in Terminalwhich nessus-file-analyzerRun in Terminalln -s path_returned_in_previous_command ~/Desktop/macOSRun in Terminalwhich nessus-file-analyzerOpenbinfolder wherenessus-file-analyzeris located.Right click onnessus-file-analyzerand chooseMake alias.Move your alias e.g. to Desktop.MetaChange logSeeCHANGELOG.LicenceGNU GPLv3:LICENSE.AuthorsDamian Krawczykcreatednessus file analyzerbyLimberDuck.
Package: nessusha
Nessus Host Assessment CrawlerExtract Nessus HA data to xlsxGetting StartedThis package is for those who are suffering from Nesses HA work. Good luck :)InstallingThe package can be installed by pip install. Make sure you have installed it properly.Run the following commands to install pip.curl https://bootstrap.pypa.io/get-pip.py -o get-pip.pypython get-pip.pyThe following command is to install nessusha.pip install nessushaFor Mac User, you might have both python2 and python3. You may run the following command instead.pip3 install nessushaNow we are good to go. :)DeploymentCurrent package allows user to call the api to extract all the checks and corresponding values from HA html file.htmlparser():This function directly extract checks and values from Nessus HA html data file and store the values into a excel workbook.openhtml():Return values: (String) rawhtml, (String) filenameThis function allows you to open the html file and return the rawhtml data into a string. It also returns the filename.allhosts(rawhtml):Return values: myhsotsThis function returns a list of all the hosts appeared in the html file.VersioningCurrent Version: 0.3.0Now we can extract policy values which is your desired value into the output file.Only html files are accepted as input file.History Version:0.2.3Bug fixed for linux not open the output file properly.nessusha 0.2.2Bug fixed for the output file is not opened properly. Pre requests are added to setup file.nessusha 0.2.1Now we support MacOS and Linux! It will open the output file once it finishes.nessusha 0.2.0Documentation and other functions are added.nessusha 0.1.2nessusha 0.1.1nessusha 0.1.0AuthorsLi Xingxuan:lxx895466249@gmail.comSakthi Ram:sakthi_ram@hotmail.comLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: nessus-nessus-parser
Nessus Report Parser transforms a nessus xml report file into a Plain Python objectThe object contains sub objects, mirroring the XML node hierarchy, parsing date string and integer values and attributes to their appropriate Python Datastructures.Usagefrom nessus import parse_nessus_file, parse_nessus_xml
parse_nessus_file('path/to/nessus/file.nessus')
Package: nessus-report-parser
[![Build Status](https://travis-ci.org/siteblindado/nessus-report-parser.svg?branch=master)](https://travis-ci.org/siteblindado/nessus-report-parser#)Nessus Report Parser transforms a nessus xml report file into a Plain Python objectThe object contains sub objects, mirroring the XML node hierarchy, parsing date string and integer values and attributes to their appropriate Python Datastructures.Usagefrom nessus_parser import parse_nessus_file, parse_nessus_xml
parse_nessus_file('path/to/nessus/file.nessus')
Package: nessvec
nessvecInstall from Source (recommended)Clone the repository with all the source code and data:$gitclonegit@gitlab.com:tangibleai/nessvec$cdnessvecCreate a conda environment and install the dependencies:$condacreate-nnessvec'python==3.9.7'$condaenvupdate-nnessvec-fscripts/environment.yml$pipinstall-e.Install from PyPi (only tested on Linux)$pipinstallnessvecGet Started>>>fromnessvec.utilimportload_glove>>>w2v=load_glove()>>>seattle=w2v['seattle']>>>seattlearray([-2.7303e-01,8.5872e-01,1.3546e-01,8.3849e-01,...>>>portland=w2v['portland']>>>portlandarray([-0.78611,1.2758,-0.0036066,0.54873,-0.31474,...>>>len(portland)50>>>fromnumpy.linalgimportnorm>>>norm(portland)4.417...>>>portland.std()0.615...>>> cosine_similarity(seattle, portland)
0.84...
>>> cosine_similarity(portland, seattle)
0.84...>>>fromnessvec.utilimportcosine_similarity>>>cosine_similarity(w2v['los_angeles'],w2v['mumbai']).5
Package: nessvector
ALIAS fornessvecInstall from Source (recommended)Clone the repository with all the source code and data:$gitclonegit@gitlab.com:tangibleai/nessvec$cdnessvecCreate a conda environment and install the dependencies:$condacreate-nnessvec'python==3.9.7'$condaenvupdate-nnessvec-fscripts/environment.yml$pipinstall-e.Install from PyPi (only tested on Linux)$pipinstallnessvecGet Started>>>fromnessvec.utilimportload_glove>>>w2v=load_glove()>>>seattle=w2v['seattle']>>>seattlearray([-2.7303e-01,8.5872e-01,1.3546e-01,8.3849e-01,...>>>portland=w2v['portland']>>>portlandarray([-0.78611,1.2758,-0.0036066,0.54873,-0.31474,...>>>len(portland)50>>>fromnumpy.linalgimportnorm>>>norm(portland)4.417...>>>portland.std()0.615...>>> cosine_similarity(seattle, portland)
0.84...
>>> cosine_similarity(portland, seattle)
0.84...>>>fromnessvec.utilimportcosine_similarity>>>cosine_similarity(w2v['los_angeles'],w2v['mumbai']).5
Package: nessy-cli
Simple command line interface to the Nessy locking system.InstallationIt is simplest to install via PyPI using:pip install nessy-cliUsageFor convenience, you should set the nessy server claims URL in your environment:export NESSY_CLAIMS_URL=http://nessy.example.com/v1/claimsIfNESSY_CLAIMS_URLorGENOME_NESSY_SERVERare set in your environment,
then those will be used; otherwise, you will need to specify the URL on the
command line with the–urloption.To list active claims:nessy list --status activeTo revoke claims matching a filter:nessy revoke --status active --min-active-duration 100000To avoid a race condition while trying to revoke a specific claim on a
resource, you should add a filter on the active duration:nessy revoke --status active --min-active-duration 100000 --resource <RESOURCE>
Package: nest
NeST: Network Stack TesterNeSTis a python3 package that handles testbed setup, testbed configuration,
collecting and visualizing data by providing a user friendly API, addressing
common issues involved in conducting networking experiments.Thepaperintroducing
NeST was accepted atANRW'20.NeSTsource code repository is maintained atGitLab.NOTE: NeST is currently in beta stage.InstallationInstructions to installNeSTcan be found ininstall.rst.ExamplesSeveral examples, that explain the basic APIs and features of NeST, are
available in theexamples directory.DocumentationThe documentation of NeST APIs can be read online at:https://nest.nitk.ac.in/docs/Instructions for generating/building the API documentation can be found indocs/README.mdfolder.Unit testsTests can be found innest/testsfolder.
Run the below command in the repo's root folder to run the tests.sudopython3-munittest-vNOTE: NeST requiresrootaccess currently to create and manage network namespaces.ContributingTo contribute, readCONTRIBUTING.md
Package: nest2D
IntroductionNest2D is a 2D bin packaging tool for python.Nest2D works with the C++ libnest2d backend for speed. For python C++
interoperability we use pybind11.The library is written in a way that it should be usable out of the box
with a very simple interface. The backend is reasonably fast and robust,
being built on top of boost geometry and thepolyclippinglibrary.ExampleA simple example may be the best way to demonstrate the usage of the
library.fromnest2DimportPoint,Box,Item,nest,SVGWriterdefadd_shape1(n,items):foriinrange(n):item=Item([Point(-5000000,8954050),Point(5000000,8954050),Point(5000000,-45949),Point(4972609,-568550),Point(3500000,-8954050),Point(-3500000,-8954050),Point(-4972609,-568550),Point(-5000000,-45949),Point(-5000000,8954050)])items.append(item)defadd_shape2(n,items):foriinrange(n):item=Item([Point(-11750000,13057900),Point(-9807860,15000000),Point(4392139,24000000),Point(11750000,24000000),Point(11750000,-24000000),Point(4392139,-24000000),Point(-9807860,-15000000),Point(-11750000,-13057900),Point(-11750000,13057900)])items.append(item)defmain():box=Box(150000000,150000000)input=[]add_shape1(23,input)add_shape2(15,input)pgrp=nest(input,box)sw=SVGWriter()sw.write_packgroup(pgrp)sw.save()It is worth to note that the type of the polygon carried by the Item
objects is the type defined as a polygon by the geometry backend. In the
example we use the clipper backend and clipper works with integer
coordinates.Example call$pipinstallnest2D$pythonexamples/simple_sample.pyExample outputAlt textLicenseUnfortunately libnest2d is provided with a contractible GPL type license
so we can not release this with better license terms. Details can be
found in the LICENSE file. By using, distributing, or contributing to
this project, you agree to the terms and conditions of this license.Referencespybind11libnest2dSVGNestAn effective heuristic for the two-dimensional irregular bin packing
problemComplete and robust no-fit polygon generation for the irregular
stock cutting
problem
Package: nestap-excel-create
Create ExcelThis Package will help you to create Excel file more quickly and efficiently.How to Install it?On Windows:pip install nestap_excel_createOn Linux:sudo pip3 install nestap_excel_createHow to Use it?Initializing The Packagefrom nestap_excel_create.create_excel_helper import create_excel
     
     data1 = Employee.objects.values('eno', 'ename', 'esal', 'eaddr').all()
     
     data2 = Student.objects.values('roll_no', 'name', 'school', 'addr').all()
     
     columns1 = [
                  ('eno', 'Employee Number'),
                  ('ename', 'Employee Name'),
                  ('esal', 'Employee Salary'),
                  ('eaddr', 'Employee Address')

              ]
              
     columns2 = [
                  ('roll_no', 'Roll Number'),
                  ('name', 'Student Name'),
                  ('school', 'School'),
                  ('addr', 'Address')

              ]         
        
     create_excel([
        {
            'sheet_name': Your sheet name 1,
            'columns_keys': columns1 
            'data_list': list(data1)
        },
        {
            'sheet_name': Your sheet name 2,
            'columns_keys': columns2 
            'data_list': list(data2)
        }
    ]) # Provide the list of the dictionary while InitializingThis Nestap Excel package create 1 and more than 1 excel sheet in same excel fileThis Nestap Excel Package create excel file more quickly and efficiently.
Package: nestargs
nestargsnestargsis a Python library that treats command line arguments as a hierarchical structure. The functionality for interpreting command line arguments is the same as argparse.Installationpip install nestargsBasic usageWhen defining command line arguments, use "." as the delimiter. to represent a variable hierarchy. The following code example defines annandpricevariable in theapplehierarchy and another separatenandpricevariable in thebananahierarchy.importnestargsparser=nestargs.NestedArgumentParser()parser.add_argument("--apple.n",type=int)parser.add_argument("--apple.price",type=float)parser.add_argument("--banana.n",type=int)parser.add_argument("--banana.price",type=float)args=parser.parse_args(["--apple.n=2","--apple.price=1.5","--banana.n=3","--banana.price=3.5"])# => _NestedNamespace(apple=_NestedNamespace(n=2, price=1.5), banana=_NestedNamespace(n=3, price=3.5))Variables obtained by parsing command line arguments can be referenced by hierarchy.args.apple# => _NestedNamespace(n=2, price=1.5)Of course, you can also refer directly to variables lower down in the hierarchy.args.apple.price# => 1.5When referring to each level of hierarchy, you can usevarsto create a dictionary format.vars(args.apple)# => {'n': 2, 'price': 1.5}Use a different delimiter for namespaceThe default namespace delimiter is "." but can be any other character. In that case, specify the delimiter in theNestedArgumentParserconstructor argument.importnestargsparser=nestargs.NestedArgumentParser(delimiter="/")parser.add_argument("--apple/n",type=int)args=parser.parse_args(["--apple/n=1"])# => _NestedNamespace(apple=_NestedNamespace(n=1))However, references to variables must be separated by "." delimiter when referring to variables.args.apple.n# => 1
Package: nest-asyncio
IntroductionBy design asynciodoes not allowits event loop to be nested. This presents a practical problem:
When in an environment where the event loop is
already running it’s impossible to run tasks and wait
for the result. Trying to do so will give the error
“RuntimeError: This event loop is already running”.The issue pops up in various environments, such as web servers,
GUI applications and in Jupyter notebooks.This module patches asyncio to allow nested use ofasyncio.runandloop.run_until_complete.Installationpip3 install nest_asyncioPython 3.5 or higher is required.Usageimportnest_asyncionest_asyncio.apply()Optionally the specific loop that needs patching can be given
as argument toapply, otherwise the current event loop is used.
An event loop can be patched whether it is already running
or not. Only event loops from asyncio can be patched;
Loops from other projects, such as uvloop or quamash,
generally can’t be patched.
Package: nestbackup
NestBackupBackup a server to S3 using a short and simple config file.The config file describes a list of steps to be ran. It can backup databases (PostgreSQL and MySQL), folders (e.g. application media), and send an email with the result of the backup.All the backup commands are ran as shell calls (e.g.aws s3 ...,pg_dump ...), and each of these calls are logged, so what NestBackup does under the hood is transparent and the steps can be easily reproduced in a shell.ConfigurationNestBackup expects the config file to be in~/backup.ini. It can be overriden using theNESTBACKUP_CONFIGenvironment variable.SectionsEach section describes a job to be ran, and the backup is uploaded tohostname/section_name/[remote_path].Available jobs:sync: Sinchronizes a path with the remote one. Usess3 sync.database: Dumps the data of given database, compresses it and uploads it to the bucket. It supports mysql and postgresql.smtp: Sends an email with a report of the jobs that were ran.Thejobfield is a mandatory attribute for all the section, as is the one that determines which job will be run.Example config file[DEFAULT]
# The DEFAULT section contains the default values set to all the jobs
aws_access_key_id=app
aws_secret_access_key=12345678
bucket=backup

[media]
job=sync
local_path=/var/www

[db]
job=database
db_uri=postgresql://app:app@postgres/app
# db_uri=mysql://app:app@mysql/app
# keep 7 files, delete older ones
retention=7

[notify]
job=smtp
server=smtp.mailgun.org
ssl=yes
port=465
username=postmaster@mg.example.com
password=secret123
sender=Example Systems <postmaster@mg.example.com>
recipients=admin@example.comUsageInitialize a base config file in~/backup.ini(overridable byNESTBACKUP_CONFIGenvironment variable):nestbackup initBackup:nestbackup backupRestore:nestbackup restoreCron example0 1     *  *  * bash -lc "nestbackup backup"Installationpip3 install nestbackupOr you can checkout the repository and install it usingsetup.py:git checkout git@github.com:jorgebg/nestbackup.git
cd nestbackup
python3 ./setup.py installOr download it right to your/usr/bin/folder:wget https://raw.githubusercontent.com/jorgebg/nestbackup/main/nestbackup.py -o /usr/bin/nestbackup
chmod +x /usr/bin/nestbackupRequirementsPython 3.6+, but I'm successfully using it on machines with Python 3.4AWS CLIpip install awscli
Package: nestd
nestedExtract your nested functions!InstallationpipinstallnestdUsagefromnestdimportnested,get_all_nesteddefdummy_function():test_variable="hello, world"definner_function():nonlocaltest_variablereturntest_variabledefdummy_function_with_two_inner_functions():test_variable="hello, world"test_array=[1,2,3]definner_function():nonlocaltest_variablereturntest_variabledefinner_function_2():nonlocaltest_arrayreturntest_array[1:]deftest_nested_function():inner_function=nested(dummy_function,"inner_function",test_variable="hello")assert"hello"==inner_function()deftest_2_nested_functions():all_inner_functions=get_all_nested(dummy_function_with_two_inner_functions,"hello_world",[1,2])inner_function,inner_function_2=all_inner_functionsassertinner_function[0]=="inner_function"assertinner_function[1]()=="hello_world"assertinner_function_2[0]=="inner_function_2"assertinner_function_2[1]()==[2]To perform a very deep nested searchdefdummy_function_with_nested_inner_functions():test_array=[1,2,3]defmath():nonlocaltest_arraydefsum():nonlocaltest_arraydefsum_of_array():nonlocaltest_arrayinside_arr=[random.randint(1,10)]*len(test_array)returntest_array+inside_arrdefmulti_of_array():nonlocaltest_arrayinside_arr=[random.randint(1,10)]*len(test_array)foriinrange(len(test_array)):inside_arr[i]=inside_arr[i]*test_array[i]returninside_arrans=0foriintest_array:ans+=ireturnansdefmultiply():nonlocaltest_arrayans=1foriintest_array:ans=ans*ireturnansreturntest_arraydefstats():nonlocaltest_arraydefmean():nonlocaltest_arrayreturnsum(test_array)/len(test_array)returntest_arraydeftest_3_nested_functions():inner_functions=get_all_deep_nested(dummy_function_with_nested_inner_functions,test_array=[1,2,3],)assertinner_functions["math"]()==[1,2,3]assertinner_functions["sum"]()==6assertinner_functions["mean"]()==2.0Contributor GuidelinesFeel free to open an issue for any clarification or for any suggestions.To Develop Locallypoetry installto install the dependenciespytest teststo run the tests
Package: nest-desktop
NEST DesktopA web-based application for spiking neuronal simulation.GeneralGitHubDockerPythonCondaAppImageSnapSynopsisNEST Desktop is a web-based application which provides a graphical user interface forNEST Simulator. With this easy-to-use tool, users can interactively construct neuronal networks and explore network dynamics.Advanced users often choose NEST Simulator: a prominent tool for spiking neuronal networks to measure network dynamics.
However, programming knowledge is required to write code for this tool.
NEST Desktop bypasses this requirement, but still teaches users how to construct and explore neuronal networks.
A textual script is generated from the constructed networks and sent to NEST Simulator;
the network activity is then visualized in a graph or table.
It is a useful teaching tool, since the network graphs and network activity visualizations can be exported to files that users can implement for their course protocol.NEST Desktop is available onEBRAINS(free EBRAINS account required).Quick startTo get started with NEST Desktop and NEST Simulator, use Docker compose with the configuration file:wget https://raw.githubusercontent.com/nest-desktop/nest-desktop/main/docker-compose.yml
docker-compose up --buildFor more information, please see theUser Documentation Page.Cite NEST DesktopIn order to cite NEST Desktop in general, please use the DOI10.5281/zenodo.5037050for all versions (always redirecting to the latest version).
If you like to refer to a single version, you can find these also on Zenodo, e.g.10.5281/zenodo.5037051for Version 3.0.
You can use the reference to the paper for NEST Desktop (DOI:10.1523/ENEURO.0274-21.2021) mentioned above as well, if that is more appropriate in the context of your reference.You will also find the exports for the citation managers on Zenodo and eNeuro.FundingThis project has received funding from the European Union’s Horizon 2020 Framework
Programme for Research and Innovation under Specific Grant Agreement No. 785907
(Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3).
This project was funded by the Helmholtz Association Initiative and Networking Fund
under project number SO-092 (Advanced Computing Architectures, ACA).
This work was supported by the DFG Excellence Cluster BrainLinks-BrainTools (grant EXC 1086).LicenseNEST Desktop is published under theMITlicense.
Package: nest-dfa
InstallingInstall withpippipinstallnest-dfa==1.1.2UsageDFAFilter用法:explain: 敏感词过滤:syntax: DFAFilter(sensitive_words: str) -> None:syntax: filter(self, message: str, repl: str="*") -> str:param: sensitive_words， 敏感词文本文件地址:param: message  需要检测敏感词的字符串:param: repl  敏感词需要替换的标识，默认为 *:return: 过滤后的字符串>>>frompydfa.dfaimportDFAFilter>>>dfa=DFAFilter(sensitive_words="/mnt/d/sensitive_words.txt")>>>message="大家足球投注哈麻醉">>>print(dfa.filter(message))>>>"大家****哈**"
Package: nestdict
NestdictMakes Nested Dictionaries Easy To UseNestdict is used to simplify various operations on nested and plain dictionaries.
It offers various functions to simplify your work.Pass the dictionary object in the function of your need.Get the result you want.Functionsfind_in_map(dict_obj, key1, key2, key3, ...)finds the value for nested keys if exist.obj={"cars_owned":{"sedan":1,"suv":3,"hatchback":2},}suv_cars_count=find_in_map(obj,"cars_owned","suv")print(suv_cars_count)>>3all_keys(dict_obj)returns all the keys present in the objectobj={"cars_owned":{"sedan":1,"suv":3,"hatchback":2},}car_types=all_keys(obj["cars_owned"])all_keys_in_obj=all_keys(obj)print(car_types)>>["sedan","suv","hachback"]print(all_keys_in_obj)>>["cars_owned","sedan","suv","hachback"]check_keys(dict_obj, [key1, key2, ...])check if all the keys exist in objectobj={"cars_owned":{"sedan":1,"suv":3,"hatchback":2},}is_sedan_and_suv_exist=check_keys(obj,["sedan","suv"])print(is_sedan_and_suv_exist)>>TrueInstallationNestdict requires Python3.6 +Install using pippipinstallnestdictImport functions from nestdictfromnestdictimportfind_in_map,check_keysDevelopmentWant to contribute? Great!Fork the repository.Clone the Project and create a seprate branch with function name(/feature/<featureName>) or bug name(/bugfix/<brief>).Make sure to clearly describe the functionality and use case.Make a pull request.