Saravanan666/pypi-org huggingface dataset:
Package: ent
entis a basic framework for generating attribute-based data structures from
dictionary-like data sources:> from ent import Ent

> obj = Ent(foo='foo', bar=None)

> obj.foo
u'foo'

> obj.bar
Noneentcan contain arbitrarily-nested hierarchies, as long as every node/leaf
is either a primitive type, or an ent:> obj = Ent({
    'foo': 'bar',
    'list': [1, 2, 3, 4],
    'inner': {
        'foo': 'baz',
        'bar': 'bang',
        },
    'ent': Ent(foo='bar'),
    })

> obj.foo
u'bar'

> obj.list[2]
3

> obj.inner.foo
u'baz'entcan be merged and diffed, and it will even enforce types when merging
keys that are shared:> ent1 = Ent(foo=1, bar=True)

> ent2 = Ent(foo='hi', bar=False, goo='win')

> Ent.merge(ent1, ent2)
<Ent {'foo': 1, 'bar': False}>

> Ent.diff(ent1, ent2)
<Ent {'bar': False}>enteven provides a stand-in for thejsonandyamlmodules to
automatically convert to and from Ent objects and raw JSON/YAML:> from ent import json, yaml

> json.loads('{"foo": true, "bar": null}')
<Ent {'foo': True, 'bar': None}>

> json.dumps(Ent(foo=True, bar=None))
u'{"foo": true, "bar": null}'

> yaml.safe_load('bar: null\nfoo: true\n')
<Ent {'foo': True, 'bar': None}>

> yaml.dump(Ent(foo=True, bar=None))
u'baz: 1\nfoo: bar\n'whyWhen working with configs, or other content pulled from sources like JSON, it
can be really annoying to need to constantly use brackets to access nested
data structures.Let’s say we have a small JSON file containing my personal profile:{
    "name": "John Reese",
    "urls": {
        "blog": "https://noswap.com",
        "github": "https://github.com/jreese",
        "facebook": "https://www.facebook.com/nucleareclipse"
    }
}Now let’s read in that data and print some of it out using the standardjsonmodule:import json

with open(...) as f:
    data = json.load(f)

name = data['name']
url = data['urls']['github']We can do better withent:from ent import json

with open(...) as f:
    data = json.load(f)

name = data.name
url = data.urls.githubinstallent is compatible with Python 2.7+ and Python 3.3+.
You can install it from PyPI with the following command:$ pip install entlicenseent is copyright 2015 John Reese, and is licensed under the MIT license.
Package: entab
EntabParse record-based file formats into a stream of records.UsagefromentabimportReaderreader=Reader(filename='test.fa')forrecordinreader:print(record.id)DevelopmentBuild withmaturin build --cargo-extra-args=--features=maturinor build
a working copy withmaturin develop --cargo-extra-args=--features=maturin.Test withcargo test.ReleasesBinary wheels can be built by running the following from the workspace root directory (one up):docker run --rm -v $(pwd):/io konstin2/maturin:v0.12.6 build -m entab-py/Cargo.toml --cargo-extra-args=--features=maturin --no-sdist
Package: entangld
python-entangldThis is a python port of the node.js libraryentangld, which you can checkouthereBasic Usage// TODOBe sure to checkout the full apihereTODOGIVEentangld.EntangldA__repr__for goodness sake!Add more some examples to the readmeAdd more documentationAdd more testsAdd limit to set/push request for recursion depthStandardize how methods are treated as sync or asyncDevelopmentSetup the a virtual environment$python3-mvenvvenv$sourcevenv/bin/activate(venv)$pipinstall-rrequirements.txt# install dependencies(venv)$pythonsetup.pyinstall# build an installationRun tests like:$./scripts/run_tests.shBuild documentation like:$./scripts/gen_docs.shPip stuffTo upload to pip, first remove old source packages and build new ones:(venv)$rm-rbuilddistentangld.egg-info(venv)$pythonsetup.pysdistThen push it to pip with (with appropriate~/.pypircfile):$./venv/bin/twineuploaddist/*
Package: entangle
shaCheck and get file hashes (using any algorithm)Installation$ pip install shaAPIcheck(fileName, expected, [options,] cb) / checkSync(filename, expected, [options])Asynchronously check thatfileNamehas a "hash" ofexpected.  The callback will be called with eithernullor an error (indicating that they did not match).Options:algorithm: defaults tosha1and can be any of the algorithms supported bycrypto.createHashget(fileName, [options,] cb) / getSync(filename, [options])Asynchronously get the "hash" offileName.  The callback will be called with an optionalerrorobject and the (lower cased) hex digest of the hash.Options:algorithm: defaults tosha1and can be any of the algorithms supported bycrypto.createHashstream(expected, [options])Check the hash of a stream without ever buffering it.  This is a pass through stream so you can do things like:
Package: entangled-filters
Readme -- Entangled, Pandoc filtersThis contains several Pandoc filters and scripts for literate programming in Markdown. These filters are enough to get you going with literate programming using Pandoc.FilterFunctionpandoc-annotate-codeblocksAdds annotation to code blocks in the woven output.pandoc-doctestRuns doc-tests by passing the content of code blocks through Jupyter.pandoc-tangleGenerate source files from the content of code blocks.pandoc-bootstrapExpand some elements specifically targeting a Bootstrap page.InstallEntangled filters has the following prerequisites:Python >=3.7: All of these filters are written in Python. This is mainly to encourage as many users (I mean YOU) to start developing Pandoc filters.Dhall: thepandoc-bootstrapfilter requiresdhall-to-jsonto be installed: seeDhall language.
TLDR: downloaddhall-json-*-[windows|macos|linux].[zip|tar.bz2]from theDhall release page, and extract it to a location in your$PATH. Dhall is awesome, itwillmake your life better.Installation is easiest usingpip,pipinstallentangled-filtersFor developmentTo run tests, after doing a normal install (to get the executables installed), runpipinstall--upgrade-e.[test]pytestThe executables are auto-generated by thesetup.pyscript and call somepython -mcommand.Supported syntaxSee theproject homepagefor more info.Named code blocks``` {.python#hello}
print("Hello, World!")
```Reference code blocks``` {.python#main}
def main():
    <<hello>>
```Define files``` {.python file=hello.py}
<<main>>

if__name__== "__main__":
    main()
```Documentation tests``` {.python .doctest#the-question}6*7---42
```pandoc-tangleExtracts code blocks and writes them to files.pandoc-tplain--filterpandoc-tanglehello.mdpandoc-annotate-codeblocksAnnotates code blocks in generated HTML or PDF output with name tags.pandoc-thtml5-s--filterpandoc-anotate-codeblockshello.mdpandoc-doctestRuns doctests, and include results into output. Also annotates the code blocks (so no need
to runpandoc-annotate-codeblocks).pandoc-thtml5-s--filterpandoc-doctesthello.mdpandoc-bootstrapAlso annotates code blocks, and has two features:Expand a Dhall specification into a card deck for Bootstrap, that is a flex-box with a single row and several columns of cards. This is nice to have at the top of a page to draw attention to some key points.Collapsible/foldable code blocks. Add a.bootstrap-foldclass to a code block to have the code block hidden behind a button. This is nice for some larger uninteresting code.This filter should be used together with a Bootstrap template for Pandoc. An example of its use can be seen here:Chaotic Pendulum, with the source code atgh:jhidding/chaotic-pendulum.DockerThe Entangled pandoc filters is available as aDocker image.RunIn your current working directory with a README.md file rundocker run --rm -ti --user $UID -v $PWD:/data nlesc/pandoc-tangle README.mdThis will extracts code blocks and writes them to files.Builddockerbuild-tnlesc/pandoc-tangle.
Package: entDevType
EntropyDeviationType is an extension that is intended for finding
data hidden within other data with no knowledge of the data itself.
Specifically, the intended use case is to identify executable files
(Portable Executables specifically) embedded in non-executable files.
For example, malware hidden within a Microsoft Word or PDF document.
This is a common occurrence within Advanced Persistent Threat (APT)
style attacks which leverage client-side attacks in common business
office file formats and often follow the generic pattern that within
the exploit is a XOR encrypted executable that is dropped to the
compromised system and then the host document is cleaned to remove the
exploit.The module contains two classes,entropyDeviationTypeandxorTableSearchType. Both classes are intended as proof of concepts
and not immediately exportable to production. This package also
contains an example utility,edfind.py, which serves as both an
immediately usable utility and as a rough primer on how to use the
extension to quickly analyze and locate rogue data hidden within
benign information streams.DISCLAIMERYOUR MILEAGE MAY VARY. AS WITH EVERYTHING TEST THOROUGHLY YOURSELF
BEFORE UTILIZING IN PRODUCTION CODE. THIS MODULE HAS NOT RECEIVED
EXTENSIVE TESTING AND MAY CONTAIN BUGS NO WARRANTY, EXPLICIT OR
IMPLICIT IS PROVIDED. ITS THE INTERNET. TRUST BUT VERIFYBUILDINGRequires:C++ compiler that supports C++11Python >2.3 & <3.0 (tested only on 2.7)The boost::python library$ ./setup.py build
# ./setup.py installThe C++ classes can be extracted and utilized with only a C++ compiler
that supports C++11.MORE INFORMATIONIncluded with this distribution is a PDF file in the ./doc/ directory
that contains fairly verbose documentation that outlined both the
Python and C++ API, structure and intended usage. It further outlines
usage of the included example utility, edfind.py, and does so by
explaining its usage on example document files.In short, I really tried to type this all up in reST format, but that
is just nuts. I instead elected to have a text file that provides a
very basic description, that will play friendly with 80x60 terminals
and a PDF document that describes everything in detail that doesn’t
have to overly worry a whole lot about your particular environment for
viewing the data. Cheers.
Package: entegywrapper
Entegy APIv2 WrapperInstallationpipinstallentegywrapperUsagefromentegywrapperimportEntegyAPIPorted ModulesAPI ObjectsContentContent ObjectsContent ManagementCategoriesDocumentsMultiLinksAttendance TrackingAttendance Tracking ObjectsTrack ManagementNotificationNotification - Send BulkPluginsExternal AuthenticationPoints & AchievementPoints & Achievement ObjectsPoint ManagementProfileProfile ObjectsProfile ManagementProfile TypesProfile Custom FieldsProfile LinksProfile PaymentsModules to PortLead CaptureLead Capture ObjectsLead Capture ManagementPoints & AchievementAchievement ManagementProjectProject ObjectsProject ManagementProject API KeysSubmission FormsSubmission Form - Get
Package: enteletaor
Enteletaor: Message Queue & Broker Injection toolVersion:1.0Code:https://github.com/cr0hn/enteletaorIssues:https://github.com/cr0hn/enteletaor/issues/Documentation:http://enteletaor.readthedocs.orgPython version:Python 2.7.x & 3Author:Daniel Garcia (cr0hn) - @ggdanielWhat’s EnteletaorMessage Queue & Broker Injection tool that implements attacks to Redis, RabbitMQ and ZeroMQ.Some of the actions you can do:Listing remote tasks.Read remote task content.Disconnect remote clients from Redis server (even the admin!)Inject tasks into remote processes.Make a scan to discover open brokers.Try to discover user/passwords in auth protected brokersCurrently supported brokers are:RabbitMQ (or AMQP compatible).ZeroMQ.Redis.See documentation for more information.What’s new?This Enteletaor version, add a lot of new features and fixes, like:NoteYou can read entire list in CHANGELOG file.Version 1.1.xMany improvements and fixesAdded new module:password bruteforcer.Version 1.0.0First version released
Package: ent-embedding
Package transforms your categorical variables into embedded vectors. You should have tensorflow, pandas, numpy, keras and sklearn installed.Attributes:
model = EntityEmbedding(dataframe, features from the copy of the df, target column, column you want a vector for)Hyperparameters you can optimize:
model.train_fit(activation1='relu', activation2='relu', activation3='relu', loss='mean_squared_error', metrics='mape', dense_size_num=128, dense_size_conc_1=300, dense_size_conc_2=300, alpha=1e-3, epochs=1000, batch_size=512, verbose=1, patience=5)Inside model.transform(), always provide embedded vector you want to use: model.transform(model.ent_emb)model.visualize() returns 2 d visualization of your column categories.
Package: enterble
Enter BLE SDK For PC简介Enter BLE SDK For PC 是回车科技提供的，适配回车科技蓝牙芯片的 PC 端 SDK。本 SDK 使用 Python 语言开发，可以在 macOS、Linux、Win 下运行。安装pip install enterble功能搜索蓝牙设备连接蓝牙设备与蓝牙设备通信回车科技 Flowtime 系列芯片数据交互适配使用查看examples指引打开扫描设备loop.run_until_complete(device_discover())运行 simple.py观察设备扫描情况修改设备唯一 ID 为你扫到的自己的设备（macOS 系统用 UUID、Win 系统用 MAC 地址）device_identify=("d2:ab:3f:c9:37:ad"ifplatform.system()!="Darwin"else"D5D4362A-1690-4204-B797-3015EEDB510E")打开采集数据的代码loop.run_until_complete(data_collector())运行 simple.py应该就可以了Simple:importasyncioimportsysimportloggingimportplatformfromtypingimportListfrombleak.backends.clientimportBaseBleakClientfromenterbleimportDeviceScanner,FlowtimeCollectorifsys.version_info<(3,7):asyncio.get_running_loop=asyncio._get_running_looplogging.basicConfig(level=logging.INFO,format='%(levelname)s-%(asctime)s-%(name)s-%(message)s')logger=logging.getLogger(__name__)defbleak_log(level=logging.INFO):importbleaklogger.info(f'Bleak version:{bleak.__version__}')logging.getLogger('bleak').setLevel(level=level)asyncdefdevice_discover():"""设备扫描器：扫描设备并获取设备 MAC 信息等Raises:Exception: 设备未发现"""devices=awaitDeviceScanner.discover(name=None,model_nbr_uuid='0000ff10-1212-abcd-1523-785feabcd123',)ifnotdevices:raiseException('No device found, please try again later.')fordeviceindevices:try:services=awaitdevice.get_services()for_id,serviceinservices.characteristics.items():logger.info(f'{device}-{_id}-{service}')MAC=awaitdevice.get_mac_address()logger.info(f'{device}-{MAC}')exceptExceptionase:logger.error(f'{device}-{device.identify}-{e}')asyncdefdata_collector():"""数据采集器：采集数据并输出到日志"""asyncdefdevice_disconnected(device:BaseBleakClient)->None:"""设备断开回调函数Args:device (BaseBleakClient): 设备实例"""logger.info(f'Device disconnected:{device}')asyncdefsoc_callback(soc:float)->None:"""电池电量回调函数Args:soc (float): 电池电量"""logger.info(f'Battery SOC:{soc}')logger.info(f'SOC:{soc}')passasyncdefwear_status_callback(wear_status:bool)->None:"""佩戴状态回调函数Args:wear_status (bool): 佩戴状态"""logger.info(f'Wear status:{wear_status}')passasyncdefeeg_data_collector(data:List[int])->None:"""EEG 数据采集回调函数Args:data (List[int]): EEG 数据"""logger.info(f'EEG:{data}')passasyncdefhr_data_collector(data:int):"""HR 数据采集回调函数Args:data (int): HR 数据"""logger.info(f'HR:{data}')pass# 设备广播 UUIDmodel_nbr_uuid='0000ff10-1212-abcd-1523-785feabcd123'# 设备唯一 ID（可以通过扫描设备确认）device_identify=("d2:ab:3f:c9:37:ad"ifplatform.system()!="Darwin"else"D5D4362A-1690-4204-B797-3015EEDB510E")# 初始化采集器collector=FlowtimeCollector(name='Flowtime',model_nbr_uuid=model_nbr_uuid,device_identify=device_identify,device_disconnected_callback=device_disconnected,soc_data_callback=soc_callback,wear_status_callback=wear_status_callback,eeg_data_callback=eeg_data_collector,hr_data_callback=hr_data_collector,)# 启动采集器awaitcollector.start()# 等待结束awaitcollector.wait_for_stop()if__name__=='__main__':bleak_log(logging.INFO)loop=asyncio.get_event_loop()# 扫描设备# loop.run_until_complete(device_discover())# 采集数据loop.run_until_complete(data_collector())
Package: enterprise
django-enterprise-coreA base core libraries and apps to build enterprise system using djangoInstallationjustpip install -e git+https://github.com/ardzix/django-enterprise-core.git#egg=enterprisein your python environmentFeaturesThis repo contains work to build base structures of an enterprise system that includes:Notable libraries like:ProtectedMixin
A library that handle your view permission based on django group of permissionTrackerMixin
A library that handle your visitor tracking in each viewRest Module base library
bunch of libraries that will useful if you want to build a rest apiBase Apps that u will use in every development:Authentication
Don't worry about register, login, email verification etc. We handle itTracker
App to track your visitor, a TrackerMixin viewerSuperUser
You want to manage another user inside your system? groups, permission, you name it. the super user app can handle itThe StructuresStructure holds models and admins of djangoAuthenticationCommonIntegrationTrackerUsageInsert desired app(s) to installed_apps in your settings.pyINSTALLED_APPS=[# ........'enterprise.apps.account','enterprise.apps.superuser',# ........]Include the app's urlfromdjango.contribimportadminfromdjango.conf.urlsimporturl,includefromenterprise.apps.accountimporturlsasaccountfromenterprise.apps.superuserimporturlsassuperuserurlpatterns=[# ...url(r'^account/',include((account,'account'),namespace='account')),url(r'^superuser/',include((superuser,'superuser'),namespace='superuser')),# ...]LibrariesModel libFirst thing firstInsert path bellow to your settings.pyINSTALLED_APPS=[# ........'enterprise.structures.common',# ........]You can follow this example to create a modelfromdjango.dbimportmodelsfromenterprise.libs.modelimportBaseModelGeneric# Create your models here.classTestField(BaseModelGeneric):display_name=models.CharField(max_length=100)short_name=models.SlugField(max_length=100)Protected MixinProtected mixin is a mixin for to protect view, it applies django permission and group to the implemented viewJust specify the namespace and model of the viewExample:fromenterprise.libs.viewimportProtectedMixinfromdjango.views.genericimportTemplateViewclassUserView(ProtectedMixin,TemplateView):template_name="account/user.html"namespace="account"model="user"defget(self,request):returnself.render_to_response({})
Package: enterprise-extensions
enterprise_extensionsA set of extension codes, utilities, and scripts for the enterprise PTA analysis framework.Free software: MIT licenseDocumentation:https://enterprise-extensions.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History2.4.2 (2022-28-12)
Fix HyperModel to support newer versions of numpy.2.4.1 (2022-02-10)
Add support for new noise definitions in WidebandTimingModel.2.4.0 (2022-02-10)
Use Timing Package (Tempo,Tempo2,Pint) definition of EQUAD. Enterprise has
broken backwards compatibility, and here we use thetnequadflag to switch on
the old definition.2.3.4 (2021-11-02)
Fix phase shift seed caching issue.2.3.3 (2021-10-04)
Fix bug in release build by adding ACE text file to MANIFEST.in.2.3.2 (2021-10-04)
Fix bug in HyperModel when using save_runtime_info.2.3.1 (2021-09-30)
Fix bugs associated with recent function additions. Added linting and mild PEP8
rules. Also removed older Python functionality which is no longer supported.2.3.0 (2021-09-15)
Functionality added for NANOGrav 15yr dataset analyses.
Outlier analysis software moved into separate package.2.2.0 (2021-08-10)
Version with outlier analysis.0.9.1 (2021-05-06)
0.9.0 (2019-09-20)
——————First release on PyPI.
Package: enterprise-pulsar
enterpriseENTERPRISE (Enhanced Numerical Toolbox Enabling a Robust PulsaR
Inference SuitE) is a pulsar timing analysis code, aimed at noise
analysis, gravitational-wave searches, and timing model analysis.Note:enterprise>=3.0does not support Python2.7. You must use
Python >= 3.7.Free software: MIT licenseDocumentation:https://enterprise.readthedocs.io.InstallationTo install viapip, some non-python dependencies are required. See thelibstempoandscikit-sparsedocumentation for more info on how to install these dependencies. Once
these are installed, you can dopipinstallenterprise-pulsarTo install viaconda, simply docondainstall-cconda-forgeenterprise-pulsarAttributionIf you make use of this software, please cite it:Ellis, J. A., Vallisneri, M., Taylor, S. R., & Baker, P. T. (2020, September 29). ENTERPRISE: Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE (v3.0.0). Zenodo. http://doi.org/10.5281/zenodo.4059815


@misc{enterprise,
  author       = {Justin A. Ellis and Michele Vallisneri and Stephen R. Taylor and Paul T. Baker},
  title        = {ENTERPRISE: Enhanced Numerical Toolbox Enabling a Robust PulsaR Inference SuitE},
  month        = sep,
  year         = 2020,
  howpublished = {Zenodo},
  doi          = {10.5281/zenodo.4059815},
  url          = {https://doi.org/10.5281/zenodo.4059815}
}CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: enterprise-search
enterprise_searchEnterprise search CLIDocumentation:https://nsi88.github.io/enterprise_searchGitHub:https://github.com/nsi88/enterprise_searchPyPI:https://pypi.org/project/enterprise_search/Free software: GPL-3.0-onlyFeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: entest
EntestGod is testing youMost of the testing frameworks are pretty old and all of them (python or otherwise) treat unit tests as first class citizens.
The ideas of this lib are based on the Testing Trophy. This means that we consider integration tests to be more important.
This lib provides a convenient way to explicitly define dependency relationships between tests. Improves transparency of globally needed assets (think fixtures but better).For how that would look like practically seetests/example.py. This code is referred to in my PyCon talk.https://www.ganjing.com/video/1fic7807r1p1YVbLCkn4uytkJ1c11cDemoInstallingpip install entest[all]DocumentationSeetests/example.py.To have a test implicitly depend on all other tests userun_lastflag. This is the case for teardown of critical resources for example users. To skip these tests use--skip-teardownor setENTEST_SKIP_TEARDOWNenvironment variable.To have all tests implicitly depend on a given test place it closer to the root of the graph.
Usesetup_setupto take advantage ofdepends_ondefault behavior. (i.e. for the first decorated function in a moduleTEST_ROOTis taken do be the previous test)To have a test depend on another test NOT being run usewithoutflag. This is usefull for testing error flows.ContributingPlease do not maintain a fork! Make a pull request and if it is not obviously bad I will merge it in a timely manner.python3 -m venv .venv
. .venv/bin/activate
pip install poetry
poetry install
echo "
export PYTHONPATH=$PWD
alias entest="python3 ./entest/cli.py"
" >> .venv/bin/activate
export PYTHONPATH=$PWD
alias entest="python3 ./entest/cli.py"I would like to change a lot of things structure-wise, but API will stay the same. In particular:depends_ondecorator with kwargsprevious,run_lastandwithout.STATUSclassificator. I see how it can be misused easily, but I will still ship this footgun.Run testsentest --graph
entest
entest --skip-teardown
entest --env env_name tests/example.py --snoopRoadmapBetter error output withstackprinter.Verbose mode that automatically appliespySnooperorcyberbrain.Aninitcommand that sets up the tests folder with env subfolder.
Package: entest-cognito-userpool
author: haimtrantitle: create a cognito userpool for web development
date: 20/08/2023Cognito AuthorizerWhen developing an web or mobile application, we usually need a identity provider. So this Construct can be used to create a Cognito UserPoolnewCognitoAuthorizer(stack,'CognitoAuthorizer',{userPoolName:'chatbot',userPoolClientName:'chatbotapp',});Usage GuideThere are different use cases, for exampleUse in another CDK StackCreate only a UserPool for a web application