Saravanan666/pypi-org huggingface dataset:
Package: evidence
DescriptionDifferential analysis of DFXML idifference2.py output.Installationpip install evidenceUsageFrom command line:python -m evidence --path PATH [--output OUTPUT] [--occurence OCCURENCE]OptionShortTypeDefaultDescription--path-pString-Path to idifference output dir--output-oStringoutputPath to result dir--occurence-cInt2The number of occurences in *.me to be used in ce processing--noise-nStringnoisename of noise fileExampleGiven the following files, where the first placeholer describes
the action and the secon placeholder describes the number of execution.IMPORTANT: noise.1.idiff needs to exsist. It contains only information
with no action applied. It is possible to provide a custom name for the
noise file.ge/
    *.*.idiff
    noise.1.idiffImportant: the first placeholder is used as an identifier and must be
the same for each following execution. Example:ge/
    a.1.idiff
    a.2.idiff
    a.3.idiff
    noise.1.idiffpython -m evidence -p path/to/idifference2result################################################################################

evidence by 5f0
Differential analysis of DFXML idifference2.py output

Current working directory: path/to/evidence

Datetime: 01/01/1970 10:20:30

################################################################################

--> Creating folder structure
    --> path/to/evidence/output

--> Prepare Evidence
    --> Write File: path/to/evidence/output/pe/a.1.pe
    --> Write File: path/to/evidence/output/pe/a.2.pe
    --> Write File: path/to/evidence/output/pe/a.3.pe
    --> Write File: path/to/evidence/output/pe/b.1.pe
    --> Write File: path/to/evidence/output/pe/b.2.pe
    --> Write File: path/to/evidence/output/pe/b.3.pe
    --> Write File: path/to/evidence/output/pe/c.1.pe
    --> Write File: path/to/evidence/output/pe/c.2.pe
    --> Write File: path/to/evidence/output/pe/c.3.pe
    --> Write File: path/to/evidence/output/pe/noise.1.pe

--> Merge Evidence
    --> Write File: path/to/evidence/output/me/a.me
    --> Write File: path/to/evidence/output/me/b.me
    --> Write File: path/to/evidence/output/me/c.me
    --> Write File: path/to/evidence/output/me/noise.me

--> Characterize Evidence
    --> Calculate Characteristic Evidence for a
        --> Action for Evidence Sum: b
        --> Action for Evidence Sum: c
        --> Action for Evidence Sum: noise
        --> Write File: path/to/evidence/output/ce/a.ce
    --> Calculate Characteristic Evidence for b
        --> Action for Evidence Sum: a
        --> Action for Evidence Sum: c
        --> Action for Evidence Sum: noise
        --> Write File: path/to/evidence/output/ce/b.ce
    --> Calculate Characteristic Evidence for c
        --> Action for Evidence Sum: a
        --> Action for Evidence Sum: b
        --> Action for Evidence Sum: noise
        --> Write File: path/to/evidence/output/ce/c.ce

################################################################################

Execution Time: 2.519259 secLicenseMIT
Package: evidence-ext
evidence-extevidence-extis A Meltano utility extension forEvidence.dev📊Testing with MeltanoThis extension includes a sample Evidence project, along with ameltano.ymlproject file, allowing you to test Evidence with Meltano.# install Meltanopipxinstallmeltano# install the Meltano project locallymeltanoinstall# run evidence in dev modemeltanoinvokeevidencedev# build the example evidence projectmeltanoinvokeevidencebuildUpgrading Evidence VersionsFrom time to time you may need to upgrade the version of an evidence project.# install latest version of evidencemeltanoinvokeevidenceupgrade# run arbitrary npm commands for evidencemeltanoinvokeevidencenpm{args}Installing this extension for local developmentInstall the project dependencies withpoetry install:cdpath/to/your/project
poetryinstallVerify that you can invoke the extension:poetryrunevidence_extension--help
poetryrunevidence_extensiondescribe--format=yaml
poetryrunevidence_invoker--help# if you have are wrapping another toolTemplate updatesThis project was generated withcopierfrom theMeltano EDK template.
Answers to the questions asked during the generation process are stored in the.copier_answers.ymlfile.Removing this file can potentially cause unwanted changes to the project if the supplied answers differ from the original when usingcopier update.
Package: evidence-normalizer
Evidence NormalizationService for normalizing evidenceDeveloper instructionsThe following sections include instructions specifically for developers.InstallationPipenvFor a development install, we recommend using Pipenv. See thepipenv docsfor direction on installing pipenv in your compute environment.Once installed, from the project root dir, just run:pipenv shell
pipenv lock && pipenv syncPipIf you wish to install developer dependencies forevidence.dev:pip install evidence-normalizer[dev]If you do not need the extra dependencies:pip install evidence-normalizerBackend ServicesEvidence Normalization relies onVariation Normalizationfor normalizing Cancer Hotspots data. You will need to setup backend services and set the appropriate environment variables. See theREADMEfor more information.Starting the Evidence Normalization Service LocallyTo start the service, run the following:uvicorn evidence.main:app --reloadNext, view the OpenAPI docs on your local machine:http://127.0.0.1:8000/evidenceInit coding style testsCode style is managed byflake8and checked prior to commit.We usepre-committo run conformance tests.This ensures:Check code styleCheck for added large filesDetect AWS CredentialsDetect Private KeyBefore first commit run:pre-commit installRunning unit testsRunning unit tests is as easy as pytest.pipenv run pytest
Package: evidencer
evidencerWork In Progressimportevidencerimportose=evidencer.Evidencer()# Add directory with custom extractors pluginse.append_extractors_plugin_directory("test_extractors")# Add directory with custom extractors pre configurationse.append_extractors_pre_configurations_directory("test_extractor_pre_configurations")extractions=e.extract_by_file(os.path.join("test_user_configurations","with_pre_configuration.json"))forextraction_key,extractioninextractions.items():print("%s:%s"%(extraction.result.label,extraction.result.value))
Package: evidencetheory
A Python Library to attack the problem ofFusion Information.
Dempster-Shafer Theory implemention is our first goal.Install:________pip install evidencetheoryUsage:from evidence.theory import dst # load dempster-shafer functions
from evidenec.tests.assets import INPUT
print INPUT['bba1'] # path for the bba exampleKnowing the format of filebba1:bba = []
with open(INPUT['bba1'], 'r') as f:
    N = int(f.readline())
    for _  in range(N):
        bba += [ float(f.readline()) ]
print bba # verify a table with values between 0 and 1We want to get a Belief functionBelfrom a tablebba(basic
belief assignament function), so the following is how to do that:bel1 = dst.Bel(m=bba)
for i in range(1<<len(bba)):
    print bel1(A=i) # i is a set represents in binary form.For now, please review code inevidence.theory.dst.
Package: evidence-with-ease
Evidence With EaseA nifty Python tool to expedite evidence printingThis script is aimed at helping debaters who use Google Docs to write their cases and scatter links to evidence all throughout their documents. I know (from personal experience) that it's a nightmare to go through cases two days before a tournament, click on every link, and print each webpage and PDF individually. Even if you have a master evidence sheet, it still takes hours to click and print--hours which would be better spent preparing blocks and fine-tuning arguments.Installing evidence-with-ease will make theevidencecommand available to you: just runevidencein your terminal and the GUI should appear. Paste the shareable link to your Doc, select a target download folder, and hit "Go!" Don't close the terminal--the program will log its progress and alert you of any unexpected failures.The Program (in a nutshell)Evidence with Ease utilizes the PythonRequestsandBeautifulSoup4modules to first scrape your Google Doc for any hyperlinks, then to scrape each linked webpage and download its HTML source code. Next, each HTML file is converted to a PDF usingpdfkit. Finally, using PyPDF2'sFileMerger, all the PDFs are merged into one big PDF for maximum printing ease. Individual PDFs longer than 30 pages aren't included in the final product (but are still downloaded for you), and should an error occur in either grabbing a webpage's source code or converting it to a PDF, the program will let you know so you can download/print those files afterwards.Clean PrintingI created functions with BeautifulSoup that remove ~80-90% of unwanted tags (images/videos, forms, headers/footers etc.) from the HTML as well as all Javascript and CSS styling before writing to file. Some pages are cleaner than others, but it does the job, and I daresay it does it adequately well.Known IssuesRemoving Javascript from webpages breaks certain well-devloped sites which generate all page content dynamically, so some 'cleaned' files might not contain any article content at all. Furthermore, removing Javascript looks fishy to sites with good bot detection, so some pages may be blocked or Captcha-prompted. I haven't yet figured out a good way to stop the program from downloading these blocked/Captcha'd pages, but by supplying a realistic user agent and other legitimate browser markers in the request header, I've managed to get around most blocks. In short, don't treat this program as the be-all end-all, and review the final PDF closely for any broken or blocked pages as you'll have to reprint these from the original sources yourself.Happy printing!-Sage
Package: evidential-deep-learning
Evidential Deep Learning"All models are wrong, but some —that know when they can be trusted— are useful!"- George Box (Adapted)This repository contains the code to reproduceDeep Evidential Regression, as published inNeurIPS 2020, as well as more general code to leverage evidential learning to train neural networks to learn their own measures of uncertainty directly from data!SetupTo use this package, you must install the following dependencies first:python (>=3.7)tensorflow (>=2.0)pytorch (support coming soon)Now you can install to start adding evidential layers and losses to your models!pip install evidential-deep-learningNow you're ready to start using this package directly as part of your existingtf.kerasmodel pipelines (Sequential,Functional, ormodel-subclassing):>>> import evidential_deep_learning as edlExampleTo use evidential deep learning, you must edit the last layer of your model to beevidentialand use a supported loss function to train the system end-to-end. This repository supports evidential layers for both fully connected and convolutional (2D) layers. The evidential prior distribution presented in the paper follow a Normal Inverse-Gamma and can be added to your model:import evidential_deep_learning as edl
import tensorflow as tf

model = tf.keras.Sequential(
    [
        tf.keras.layers.Dense(64, activation="relu"),
        tf.keras.layers.Dense(64, activation="relu"),
        edl.layers.DenseNormalGamma(1), # Evidential distribution!
    ]
)
model.compile(
    optimizer=tf.keras.optimizers.Adam(1e-3), 
    loss=edl.losses.EvidentialRegression # Evidential loss!
)Checkouthello_world.pyfor an end-to-end toy example walking through this step-by-step. For more complex examples, scaling up to computer vision problems (where we learn to predict tens of thousands of evidential distributions simultaneously!), please refer to the NeurIPS 2020 paper, and the reproducibility section of this repo to run those examples.ReproducibilityAll of the results published as part of our NeurIPS paper can be reproduced as part of this repository. Please refer tothe reproducibility sectionfor details and instructions to obtain each result.CitationIf you use this code for evidential learning as part of your project or paper, please cite the following work:@article{amini2020deep,
  title={Deep evidential regression},
  author={Amini, Alexander and Schwarting, Wilko and Soleimany, Ava and Rus, Daniela},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}
Package: evidentialreasoning
Python implementations evidential reasoning (ER) approachInstallation:Using pip:pipinstallevidentialreasoning##Example:importnumpyasnp
fromevidentialreasoning.ERimport*input=np.array([[0,0,0.2,0.2,0.6,0],[0,0,0.2,0.4,0.4,0],[0,0,0.2,0.8,0,0],[0,0,0.4,0.2,0.4,0]])weights=np.array([0.25,0.25,0.25,0.25])a=ER_Aggregation(input,weights)print(a)b=ER_Normaliation(a)print(b)To be updated
Package: evileg-core
(ESNF-C) EVILEG Social Network Framework - Core ModuleSocial network EVILEG. Django app - core moduleThis application is core module of EVILEGDonatePlease, support development of evileg-core module. We open part of EVILEG source code after stabilization in this module.You can donate to us with following ways:PayPal.MeYandex.MoneyDocumentationYou can read documentation of this module onEVILEG
Package: evilHunter
evilHunterNOVEDADES:¡Fuerza Brute en C!
    ¡Ahora puedes atacar a mas de 1 cliente!Argumentos:[♦] evilHunter [-w /path/to/wordlists] [-b 12 (passwd length)] [-t 400 (Nº of threads)][♦] Attack Type:

        (-w / --wordlist)
          -w /path/to/wordlists

        (-b / --brute)
          -b passwd_length ("r" for rand)[♦] Especification: 

        (-t / --threads)
            -t Nº_of_threads

        (-h / --help)[♦] Example:
                                                   ┌── (Path/to/wordlist)
        Wordlist attack --> sudo evilHunter.py -w /usr/share/wordlists/rockyou.txt

    -------------------------------------------------------------------------------
                                                          ┌──(500 threads)                                                              
        Dictionari attack --> sudo evilHunter.py -b r -t 500
                                                    └─(random length)INSTALACIÓN:Git Hub: (v0.2.18)FOR USE THE BRUTE FORCE 'C' INSTALL THE SETUP.PY FILE, 
         ¡DONT RUN IT DIRECTLY FROM THE DIR CLONED! 
    
 [!] ONE LINER: 
 
       git clone https://github.com/an0mal1a/evilHunter && chmod -R 744 evilHunter && cd evilHunter && sudo python3 setup.py install


 [!] COMMANDS

        1. git clone https://github.com/an0mal1a/evilHunter
        2. chmod -R 744 evilHunter
        3. cd evilHunter 
        4. sudo pip install .

[!] ERROR:

    Si usas el comando sudo python3 setup.py install te da este error:

         pkg_resources.extern.packaging.version.InvalidVersion: Invalid version: '1.14.0-unknown'
    
    Ejecuta este comado:

          ┌──(supervisor<  ㉿kali-machine)-[~]
          └─$ sudo pip install .


[*] END:

    ┌──(supervisor㉿kali-machine)-[~]
    └─$ evilHunter.py -hPyPi: (https://pypi.org/project/evilHunter/)1.┌──(supervisor㉿kali-machine)-[~/Escritorio]
  └─$ pip install evilHunter
            
2.┌──(supervisor㉿kali-machine)-[~/Escritorio]
  └─$ evilHunter.pyREQUERIMENTS:Esta herramienta requiere de python3 y de el pack
de herramientas de 'aircrack-ng' y 'macchanger'

    -   macchanger
    -   aircrack-ng
    -   airodump-ng
    -   aireplay-ng
    -   airmon-ngInstall all dependencies┌──(supervisor㉿kali-machine)-[~/ALL_MINE/CRACK_WIFI]
└─$ sudo apt-get update -y && sudo apt-get install aircrack-ng -y && sudo apt-get install macchanger -y && sudo apt-get install wpasupplicantDICCIONARIO:RockYou install -> (https://github.com/an0mal1a/evilHunter/releases/tag/RockYou)Procedimiento:1. Seleccionamos interfaz de red 'compatible' (wlan0) 
    y la establecemos en modo monitor.


2. Escaneamos redes cercanas para guardar información sobre:

            · ESSID/NOMBRE DE RED
            · Cifrado
            · BSSID
            · Canal/Channel
            · Clientes
            · Power ( Señal )


3. A la vez que estamos escaneando esa red fijando la tarjeta de
    red en el mismo canal, enviamos paquetes de deautenticación 
    

4. Una véz capturado el handshake, abrimos el archivo .cap y
    con diccionary attack o generando contraseñas lo crackeamos.For suggeriments or problems to fix -->https://github.com/an0mal1a/evilHunter/issues
Package: evilmc
A python version of the EVIL-MC codeInstallationpipYou can installevilmcwith pip (recommended):$ pip install evilmcFrom sourceYou can also install directly from source. The most currentstable releaseis up on PyPI.Unpack the distribution withtar-xvfand navigate to the source root directory.  To install, run the setup script:$ sudo python setup.py installNote that you’ll need tocdout of the source directorybeforeyou can importevilmc.Requirementsnumpy>=1.14.3PyAstronomy>=0.12.0six>=1.11.0astropy>=2.0Authorsevilmcwas written byBrian Jackson.
Package: evilpy
# evil.py
python必不备库 借鉴Evil.js的python版https://github.com/chao325/Evil.js配置出错概率, 随机概率出错 让出错更可控
配置出错时间, 规避目标前期检查, 你看我, 我正常, 你不看我就出错
修改len 大于配置(20)的始终少1
修改print 概率不打印
交换floor ceil 上下取整颠倒
替换random randint(argv, argv_1) 返回 randint(max(argv_1 // 2, argv),argv_1)# 声明
本项目仅用于作者个人项目代码，用于增加游戏随机性
请不要转载和引用，造成的损失与本作者无关
Package: evilunit
featuresshortcutsparameterizednestedshortcutsthis is tiresome.importunittestclassTests(unittest.TestCase):def_getTarget(self):fromfoo.barimportbooreturnboodef_callFUT(self,*args,**kwargs):self._getTarget()(*args,**kwargs)deftest_it(self):params=object()result=self._callFUT(params)self.assertEqual(result,params)shortcuts decoratortest_target – adding _getTarget(), _makeOne() methodstest_function  – adding _getTarget(), _callFUT() methodsimportunittestfromevilunitimporttest_function@test_function("foo.bar:boo")classTests(unittest.TestCase):deftest_it(self):params=object()result=self._callFUT(params)self.assertEqual(result,params)parameterizedimportunittestfromevilunitimportNestedTest,nested_test_compileclassTests(unittest.TestCase):def_callFUT(self,x,y):returnx+y@paramaterized([(1,2,3),(1,1,2),(1,0,1),(2,3,5),(4,4,8)])def_test_add(self,x,y,expected):"""adding number"""result=self._callFUT(x,y)self.assertEqual(result,expected)@classmethoddeftearDownClass(cls):count_of_test_method=sum(1fork,vincls.__dict__.items()ifk.startswith("test_")andcallable(v))assertcount_of_test_method==5nestedimportunittestfromevilunitimportNestedTest,nested_test_compileL=[]@nested_test_compileclassToplevelTest(NestedTest):@classmethoddefsetUpClass(cls):assertissubclass(cls,unittest.TestCase)L.append("t1")defsetUp(self):assertisinstance(self,unittest.TestCase)L.append("t2")deftest_it(self):assertisinstance(self,unittest.TestCase)L.append("t3")self.assertEqual(L,["t1","t2","t3"])deftearDown(self):assertisinstance(self,unittest.TestCase)globalLL=[]classChild(NestedTest):@classmethoddefsetUpClass(cls):assertissubclass(cls,unittest.TestCase)L.append("m1")defsetUp(self):assertisinstance(self,unittest.TestCase)L.append("m2")deftest_it(self):assertisinstance(self,unittest.TestCase)L.append("m3")self.assertEqual(L,["t1","m1","t2","m2","m3"])classGrandChild(NestedTest):@classmethoddefsetUpClass(cls):assertissubclass(cls,unittest.TestCase)L.append("n1")defsetUp(self):assertisinstance(self,unittest.TestCase)L.append("n2")deftest_it(self):assertisinstance(self,unittest.TestCase)L.append("n3")self.assertEqual(L,["t1","m1","n1","t2","m2","n2","n3"])classChild2(NestedTest):@classmethoddefsetUpClass(cls):assertissubclass(cls,unittest.TestCase)L.append("o1")defsetUp(self):assertisinstance(self,unittest.TestCase)L.append("o2")deftest_it(self):assertisinstance(self,unittest.TestCase)L.append("o3")self.assertEqual(L,["t1","o1","t2","o2","o3"])0.1.1:
- fix if fixture is undefined in child class parent class’s method is not called.
Package: evilvars
Changes immutable data types (in-place)pip install evilvarsImportant: As the module's name suggests, it should not be used in a real project.go_to_purgatory(v,new):"""Copies the bytes of a new value to a buffer of an existing value.Args:v (Any): The existing value to copy the bytes to.new (Any): The new value to copy the bytes from.Returns:bool: True if the bytes were successfully copied, False otherwise."""fromevilvarsimportgo_to_purgatoryt=("275",54000,"0.0","5000.0","0.0")newvar="200"# the new string needs to be  <=  the old stringindex=0print(id(t),t)go_to_purgatory(v=t[index],new=newvar)print(id(t),t)index=1newvar=20.1go_to_purgatory(v=t[index],new=newvar)print(id(t),t)mystring="hallo"newvar="XallY"print(mystring)print(id(mystring))go_to_purgatory(v=mystring,new=newvar)print(mystring)print(id(mystring))mystringb=b"hallo"newvarb=b"XallY"print(mystringb)print(id(mystringb))go_to_purgatory(v=mystringb,new=newvarb)print(mystringb)print(id(mystringb))2218474368960('275',54000,'0.0','5000.0','0.0')2218474368960('200',54000,'0.0','5000.0','0.0')2218474368960('200',20.1,'0.0','5000.0','0.0')hallo2218646391856XallY2218646391856b'hallo'2218646405792b'XallY'2218646405792
Package: evinai-health-indices
evinai-health-indicesA unified collection of health indices and indicators e.g bmi, bai, corps index etc.health-indicesA unified collection of health indices and health indicators eg bmi,bai,corp index,etcPurpose of the PackageThe purpose of the package is to provide a collection of all health indices in one unified libaryFeaturesCollection of Health IndicesBMIBAICorpulence IndexPiglet IndicesetcCollection of Health IndicatorsMortality rateBirth ratePrevalence RateFertility rateGetting StartedThe package can be found on pypi hence you can install it using pipInstallationpipinstallevin-health-indicesUsageUsing the short forms or abbreviated forms of indices>>>fromhealth_indicesimportbmi,bai,>>>bmi(54,1.70)Using the long form of indices>>>fromhealth_indicesimportbodymassindex>>>bodymassindex(54,1.70)Examples>>>fromhealth_indicesimportbmi>>>bmi(54,1.70)BodyMassIndexis=>18.0BMICategory=>UnderweightBodyMassIndexis=>18.018.0>>>a=bmi(54,1.70)BodyMassIndexis=>18.0BMICategory=>UnderweightBodyMassIndexis=>18.0>>>a18.0>>>ContributionContributions are welcomeAuthorMain Mainteainer: Togay TuncaSource: Jesse E.Agbe(jCharis youtube tutorial)
Package: evince
evinceA visualization framework for the Hylleraas Software PlatformInstallationTo install use pip:$ pip install evinceFor a development installation (requiresNode.jsandYarn version 1),$ git clone https://github.com//evince.git
$ cd evince
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix evince
$ jupyter nbextension enable --py --sys-prefix evinceWhen actively developing your extension for JupyterLab, run the command:$ jupyter labextension develop --overwrite evinceThen you need to rebuild the JS when you make a code change:$ cd js
$ yarn run buildYou then need to refresh the JupyterLab page when your javascript changes.
Package: evision-lib
eVision Common Python LibraryInstallationpip install evision-libYou can also install the in-development version with:pip install https://github.com/evision-ai/evision-lib/archive/master.zipDocumentationhttps://evision-lib.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.2 (2019-10-29)No updates but to avoid namespace reusing0.1.1 (2019-10-29)Image source connection and distributionParallel wrapperHardware resource managementUtilities0.1.0 (2019-10-11)First release on PyPI.