Saravanan666/pypi-org huggingface dataset:
Package: nicedjango
nicedjangoNice django toolsModelGraphSelective dumping and loading of only the needed model data for all objects and their related
objects of one or more querysets.This is done bygetting a graph of all relations between models,getting all pks first in chunks,dump them in an order that enables correct loading.Examples# show model graph parts that would be dumped and those which not:
# example for query model a1.A with relation to child a1.B(A)
./manage.py dump_graph -p -q a -r a.b
    a1-a:
               a1-a.b          to child            a1-b.pk
       excludes:
               a1-a.f          to foreign          a1-f.a
     a1-b:
               a1-b.pk         to parent           a1-a.pk
       excludes:
               a1-b.c          to child            a1-c.pk
               a1-b.e          to child            a1-e.pk

# dump all objects from a1.models.A.objects.filter() with relation a.b as compact yaml:
./manage.py dump_graph -f dump.yaml -s compact_yaml -q a.filter(pk__in=(1,2)) -r a.b
    - a1-a: [pk]
     - [1]
     - [2]
     - a1-b: [pk]
     - [2]

# load back the dumped dump.yaml
./manage.py load_graph -f dump.yaml -s compact_yaml

# by default serializing into compact csv files is enabled:
mkdir dump_folder
./manage.py dump_graph -f dump_folder -q a.filter(pk__in=(1,2)) -r a.b
#results in two files under dump_folder:
# a1-a.csv:
 a1-a:pk
 1
 2
# and a1-b.csv:
 a1-b:pk
 2
Package: niced-url-request
niced_url_requestA simple wrapper class to "nice" url requests in python by setting a maximum frequency to url requests, and using a disk cache.MotivationI have needed several times lately to get some relatively large dataset from the net as a series of smaller http requests. This class is a convenient small wrapper for doing so without annoying the server.Functionalitieslimit request ratecache the answers on diskperform up to 10 re-tries waiting 10 seconds in between if request fails to make querying large quantities of requests robust to minor network / service interruptionsInstallationpip install niced_url_requestTests and exampleTo run tests:pytest -v .from the present folder.For a detailed example: seeexamplesfolder.Small start up example:from niced_url_request import NicedUrlRequest

niced_requester = NicedUrlRequest()

request_result = niced_requester.perform_request("http://httpbin.org/get?bla1=blabla1")

print(request_result.decode("ASCII"))
Package: nicefid
nicefidMinimalistic FID and KID implementation. Reference checked againstcleanfid.
Code is a mix betweencrowsonkb's implementationandcleanfid.On Aliased Resizing and Surprising Subtleties in GAN EvaluationInstallpoetryaddnicefidOr, for the old timers:pipinstallnicefidAPInicefid.Features.from_directory(path:Union[str,Path])nicefid.Features.from_iterator(iterator:Iterator[torch.Tensor])# NCHWnicefid.Features.from_path(path:Union[str,Path])features.save(path:Union[str,Path])nicefid.compute_fid(features_a,features_b)nicefid.compute_kid(features_a,features_b)UsageComparing directory with generated images.importnicefidfeatures_generated=nicefid.Features.from_iterator(...)features_real=nicefid.Features.from_directory(...)fid=nicefid.compute_fid(features_generated,features_real)kid=nicefid.compute_kid(features_generated,features_real)
Package: nice-figures
Nice FiguresA collection of scripts, modules, and style files for making publication quality figures in Matplotlib.Styles are in accordance with American Physical Society (APS) and Nature publications.To load module:fromnice_figuresload*Installation instructionsThis package requires prior intallation of the following packages:numpymatplotlibI recommend installing for the PyPI server using pip:pipinstallnice-figuresAlternatively, the files can be downloaded directly from GitHub.To import, use:fromnice_figuresimport*FunctionsThe following functions are included:load_style()Load a set of predefined rcParams.add_numbering()Add numbering (e.g., a, b, c, 1, 2, 3, ...) to axes.load_cols()Load a dictionary of colors.add_border()Add a border around the figure. This is useful in, e.g., Jupyter notebooks, where it is unclear how large the figure.For more details about each function, please see the function docstrings.ExamplesExample scripts and figures are given inexamples.Useful resourceshttps://matplotlib.org/3.2.1/tutorials/introductory/customizing.htmlhttps://journals.aps.org/prl/authorshttps://www.nature.com/nature/for-authors/final-submissionWhere was this package used?https://arxiv.org/abs/1907.07030Any suggestions for improvements are very welcome!
Package: nicegpt
nicegptA Python package that simplifies the usage of the GPT API by providing a convenient class to manage context-driven instances of GPT models. With nicegpt, you can create instances, change models, and obtain context-driven responses from the GPT model.UsageFirst, make sure you have run theconfigfunction to set up your API key and optionally the organization. If you don't have environment variables set up for the key, you need to run this function.importnicegptnicegpt.config("your_api_key","your_organization")Next, create a GPT instance using the GPT class. You can choose the GPT model by accessing the Model enum:gpt_instance=nicegpt.GPT(model=nicegpt.Model.GPT4)Now you can obtain context-driven responses using the response function:response=gpt_instance.response("What is the capital of France?")Set thejust_replyparameter to obtain the raw JSON instead of the message. You can also pass additional kwargs to the response function, which will be forwarded to the GPT API:json_response=gpt_instance.response("What is the capital of France?",just_reply=False,max_tokens=50,temperature=0.8)To change the GPT model, use the change_model function:gpt_instance.change_model(nicegpt.Model.GPT3_5)To access the message history, use the get_messages function:message_history=gpt_instance.get_messages()print(message_history)LicenseThis project is released under the MIT License.
Package: nicegui-add
NiceGUI 补充 / Adds补充NiceGUI未实现的一些组件在使用NiceGUI的过程中，我查阅了组件库文档和quasarchs文档，但突然发现有些组件竟然没有实现，如BreadCrumbs，感觉还是挺重要的，就专门创了一个项目安装 / Installation# nicegui>=1.4.0pipinstallnicegui-add构建 / Build本库是使用Poetry构建，更多需查询官方文档poetrybuild
poetrypublish--build文档构建 / Documents Build文档是使用Sphinx构建cddocs
pythonmake.pymake.pyimportosmake="make.bat "os.system(make+"clear")os.system(make+"html")组件 / ElementsBreadcrumbs 面包屑类似于文件管理器上的导航栏用法 / Usageaui.breadcrumbs(separator:str=...)separator为每个元件BreadcrumbsEl之间的隔开符，默认是/aui.breadcrumbs_el(text:str=...,icon:str=...,on_click=...)text为显示文本icon为显示图标on_click为被点击时的事件例子 / Examplefromniceguiimportuifromnicegui_addimportauiwithaui.breadcrumbs():aui.breadcrumbs_el("Home",icon="home",on_click=lambda:print("'Home' Clicked"))aui.breadcrumbs_el("System",icon="windows",on_click=lambda:print("'Windows' Clicked"))aui.breadcrumbs_el("Apps",icon="apps",on_click=lambda:print("'Apps' Clicked"))ui.run()开发笔记 / Development Notes选择存储库平台时，我本来是打算用Github的，但是Github的登录特别繁琐，干脆改用Gitlab算了，而且Gitlab在中国地区可以正常访问，而不像Github一样，老是登不上去版本记录 / Version Records0.0.10011第一次发布，包含bar,breadcrumbs,breadcrumbs_el,space,toolbar,toolbar_title组件0.0.20021补充项目资料0022添加组件Intersection / 交叉，可以为ui.Card组件做显示效果0.0.30031补充项目资料0.0.40041添加组件ButtonGroup / 按钮组
Package: nicegui-highcharts
NiceGUI HighchartsThis package is an extension forNiceGUI, an easy-to-use, Python-based UI framework.
It provides ahighchartelement based onHighcharts, the popular JavaScript charting library.
Due to Highcharts' restrictive license, this element is not part of the NiceGUI package anymore, but can be install separately.Installationpython3-mpipinstallnicegui-highchartsUsageWrite your nice GUI in a filemain.py:fromniceguiimportuiui.highchart({'title':False,'chart':{'type':'bar'},'xAxis':{'categories':['A','B']},'series':[{'name':'Alpha','data':[0.1,0.2]},{'name':'Beta','data':[0.3,0.4]},],})ui.run()NiceGUI recognizes the installation of thenicegui-highchartspackage and adds it to theuinamespace.
For more information, seehttps://nicegui.io/documentation/highchart.Launch it with:python3main.py
Package: nicehash-python
nicehash-pythonfork of nicehash/rest-clients-demo with updated python clientrest-clients-demoSourcepythonTEST environmentGenerate Api key and Secret for test platform on:https://test.nicehash.com(User / Settings / API Keys)
Organization ID is displayed just above "+ Create new API key" button.Usehttps://api-test.nicehash.comfor API domain.The entire platform runs on testnet. This means that you don’t have to use your own funds to test the platform. This is the opportunity to familiarize yourself with the new system, place hash-power orders and try trading without spending any real money.PRODUCTION environmentTo use production just generate key the same way onhttps://www.nicehash.comand usehttps://api2.nicehash.comfor API domain.API docsCan be found here:https://docs.nicehash.com/DEV environmentTested using python3.8. More notes on testing in changelog. Package is still under development as some API functionality is incomplete.Install test dependencies:python3.8 setup.py install -e [.dev]Run some tests:python3.8 -m unittest tests.test_nicehash_publicRun all tests:python3.8 -m unittest discover -s ./tests
Package: nice-list
Pretty prints Python lists.Usagefromnice_listimportnice_print,nice_formatnice_print([1,2,3,4])# -> 1, 2, 3 and 4nice_print(["Alice","Bob","Charles"])# -> Alice, Bob and Charlesnice_print(["Alice","Bob","Charles"],use_and=False)# -> Alice, Bob, Charlesnice_print(["Alice","Bob","Charles",20],string_quotes='"')# -> "Alice", "Bob", "Charles" and 20nice_print([1,2,3,4],oxford_comma=True)# -> 1, 2, 3, and 4print(nice_format(["Alice","Bob","Charles"]))# -> Alice, Bob and CharlesInstallationpipinstallnice_listTestingpythonnice_list_test.pyLicenseMIT.
Package: nicellm
NiceLLM: A Python LLM toolkitAboutNiceLLM is a Python package that homologates mainstream library functions of Large Language Models (LLMs), such as OpenAI, MistralAI, and Google Generative AI, into a unique and simple interface for the user.Main FeaturesSimplified handling of API connectionsSimplified interface to use images in the promptInstallationThe source code can be found athttps://github.com/idealab-udec/nicellm.pip install nicellmInitial configTo initialize the functions ofnicellm, you will need to set some environment variables, which will contain the API keys for OpenAI and Google Generative AI. We suggest to create a file called.envin your root folder with the following information:APIKEY_GOOGLE="YOUR-API-KEY-FROM-GENERATIVE-AI"
APIKEY_OPENAI="YOUR-API-KEY-FROM-OPEN-AI"
APIKEY_MISTRAL="YOUR-API-KEY-FROM-MISTRAL"It is not mandatory to set up all env variables. However, you must set up at least the ones corresponding to the models you want to use.To obtain your API keys, please visit:OpenAI:https://platform.openai.com/account/api-keysGoogle Generative AI:https://makersuite.google.com/app/apikeyMistral AI:https://console.mistral.ai/user/api-keys/UsageInteract withgpt-3.5-turbo:import nicellm as llm

llm.get_response(
    model_id="gpt-3.5-turbo",
    prompt="Who is the best LLM in the world?"
)The same question can be easily replicated usinggemini-pro:llm.get_response(
    model_id="gemini-pro",
    prompt="Who is the best LLM in the world?"
)By usingmistral-tiny:llm.get_response(
    model_id="mistral-tiny",
    prompt="Who is the best LLM in the world?"
)ContributionsThis project is maintained by the Interdisciplinary Education & Artificial Intelligence Lab, IDEALAB, from the University of ConcepciÃ³n, Chile.All contributions and ideas are welcome and well received. Please send us your PRs, and we will revise them as soon as possible.LicenseThis project is under an MIT-License.
Package: nicelog
Provide formatters to nicely display colorful logging output on the console.Fork this project on GitHubRight now, it contains only one formatter, coloring log lines
depending on the log level and adding nice line prefixes containing
logger name, but future plans are to add more formatters and allow
better ways to customize them.Installationpip install nicelogQuick usageSince version0.3, nicelog comes with a helper function to quickly
set up logging for basic needs.fromnicelogimportsetup_loggingsetup_logging()Or, if you want to include debug messages too:setup_logging(debug=True)Advanced usageimportloggingimportsysfromnicelog.formattersimportColorful# Setup a loggerlogger=logging.getLogger('foo')logger.setLevel(logging.DEBUG)# Setup a handler, writing colorful output# to the consolehandler=logging.StreamHandler(sys.stderr)handler.setFormatter(Colorful())handler.setLevel(logging.DEBUG)logger.addHandler(handler)# Now log some messages..logger.debug('Debug message')logger.info('Info message')logger.warning('Warning message')logger.error('Error message')logger.critical('Critical message')try:raiseValueError('This is an exception')except:logger.exception("An error occurred")Example outputHere it is, in all its glory:The output format can be further customized, eg. if you want to reduce
colorfulness or verbosity.IntegrationsDjangoI usually put something like this in my (local) settings:LOGGING['formatters']['standard']={'()':'nicelog.formatters.Colorful','show_date':True,'show_function':True,'show_filename':True,'message_inline':False,}Changelogv0.2More decoupling between “colorer” and “style”Support for pretty tracebacks (colorful + code context + locals)Added some testsPython3 support via sixv0.1.9Replacedstrftime(3)conversion specifiers%Fand%Taren’t available on all platforms: replaced with long versions%Y-%m-%dand%H:%M:%S.v0.1.8Prevent failure in case theTERMenvironment variable is not set (PR #1)v0.1.7Added support formessage_inlineargument. If set toFalse,
messages will be displayed on their own line (useful when enabling a lot of
information)v0.1.6Added support for showing more information:record datefile name / line numbermodule / functionv0.1.5Added support for nicer colors in 256-color modeRemoved dependency from termcolor (now shipping better implementation)
Package: nice-logging
nice_loggingSimple logging library that has colored and timestamped output by default. Can be used as a drop-in replacement.Installationpip install nice_logging(Or just grab the .py file)Usageimportnice_loggingnice_logging.basicConfig(level=nice_logging.DEBUG)logger=nice_logging.getLogger(__name__)logger.info("Hello")2023-07-23 11:27:33,571 [INFO ] [__main__] HelloOr:importnice_loggingasloggingStructured Loggingnice_logging has support for rudimentary structured logging. If you usegetStructuredLoggerinstead ofgetLogger(or instantiate aStructuredLogger), you will get a logger whose log functions take a single message, and any number of keyword arguments, which are aggregated in a single dict. The resultingLogRecordwill have the attributestructuredset toTrue, andstructured_datato the dict.
Package: nicely
Python Fancy PrinterAbstractThenicelypackage is yet another ‘pretty printer’.It is meant to:have areadable and consistentoutput.Nested levels are indented. (the indentation string is configurable (4 spaces by default))The sequences and the mapping keys are sorted. (can be disabled)Mapping keys are padded. (so the values are aligned)The data types may be shown. (disabled by default)besimpleto use.Only one class is required.A convinience function is also provided.A CLI allow todumpfiles.beextensibleIf the provided support for standard types doesn’t suit your need, it’s easy to modify it.Any object can easaly be supported.be able to handlelarge data structure.Sequences, mappings, strings and multi-lines can be truncated as needed.Mapping items can be omitted based on regex.UsageThere are 3 main ways to use nicely: ThePrinterclass, thedumpfunction and theCLICommand.The Printer class:fromnicelyimportPrinterp=Printer()p.print(some_data_structure)ThePrinter.printmethod sends a formatted representation of its argument to a file-like destination (stdout by default).The dump function:fromnicelyimportdumpdump(some_data_structure)Thedumpfunction uses thePrinterclass to ‘dump’ its argument on stdout as well as to a file (‘nicely.dump’ by default).Data Types supportThere are 2 ways for an object to be “printable” / “dumpable”:The Formatter classThePrinterclass needs oneFormatterfor each supported data type (exept for string).A set of defaultFormatteris provided by the formatters module for Python’s builtin types and should be good enough for most purposes.These formatters can be modified or extended at will to support new types or modify the output for existing ones.The __print__ methodAny object can become ‘printable’ by implementing the__print__method which will call the Printer’s methods in appropriate ways.Installation$pipinstallnicely
Package: niceman
NICEMANNICEMAN aims to simplify creation and management of computing
environments in Neuroimaging. While concentrating on Neuroimaging
use-cases, it is by no means is limited to this field of science and
tools will find utility in other fields as well.StatusNICEMAN is under initial rapid development to establish core
functionality. While the code base is still growing the focus is
increasingly shifting towards robust and safe operation with a sensible
API. There has been no major public release yet, as organization and
configuration are still subject of considerable reorganization and
standardization.SeeCONTRIBUTING.mdif you are interested in
internals and/or contributing to the project.Code status:travis-ci.org (master branch)InstallationDebian-based systemsOn Debian-based systems we recommend to enableNeuroDebianfrom which we will soon
provide recent releases of NICEMAN (as soon as there is something to
release). We will also provide backports of all necessary packages from
that repository.Other Linux’es, OSX (Windows yet TODO) via pipBy default, installation via pip installs core functionality of niceman
allowing for managing datasets etc. Additional installation schemes are
available, so you could provide enhanced installation viapip install niceman[SCHEME]whereSCHEMEcould betests to also install dependencies used by unit-tests battery of the
nicemanfull to install all of possible dependencies.For installation throughpipyou would need some external
dependencies not shipped from it (e.g.docker,singularity,
etc.) for which please refer to the next section.DependenciesOursetup.pyand corresponding packaging describes all necessary
dependencies. On Debian-based systems we recommend to enableNeuroDebiansince we use it to provide
backports of recent fixed external modules we depend upon. Additionally,
if you would like to develop and run our tests battery seeCONTRIBUTING.mdregarding additional dependencies.Later we will provide bundled installations of NICEMAN across popular
platforms.LicenseMIT/ExpatDisclaimerIt is in a alpha stage –nothingis set in stone yet and nothing is
usable ATM – subscribe and wait for the first release.
Package: niceml
This is the readme for niceMLniceMLis a tool to help you set up your machine learning projects faster.
It provides pipelines for a variety of ML tasks, likeObject Detection,Semantic Segmentation,Regression,Classificationand others.All you have to do is configure your pipeline, and you're ready to go!You can also add your own components to the build-in dashboard,
where you can compair the results and performance of your ML models.Further documentation is available atniceML.io.A lot more documentation will follow soon!
Package: nice-oom-daemon
Permission is hereby granted, free of charge, to any person obtaining a copyof this software and associated documentation files (the "Software"), to dealin the Software without restriction, including without limitation the rightsto use, copy, modify, merge, publish, distribute, sublicense, and/or sellcopies of the Software, and to permit persons to whom the Software isfurnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in allcopies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THEAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHERLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THESOFTWARE.Description:# Use`nice-oom-daemon --stop-signal sigint=50 sigterm=50 sigkill=5 --grace-period 5`# Use in Docker```docker build -t nice-oom-daemon .docker run -v /var/run/docker.sock:/var/run/docker.sock nice-oom-daemon```Platform: UNKNOWNClassifier: Development Status :: 4 - BetaClassifier: Intended Audience :: DevelopersClassifier: Natural Language :: EnglishClassifier: License :: OSI Approved :: MIT LicenseClassifier: Programming Language :: PythonClassifier: Programming Language :: Python :: 2Classifier: Programming Language :: Python :: 2.7Classifier: Programming Language :: Python :: 3Classifier: Programming Language :: Python :: 3.6Requires-Python: >=2.7Description-Content-Type: text/markdown
Package: nice-parser
NiceFerramenta para facilitar a normalização de JSON's via Amazon AthenaGetting StartedNice é uma aplicação que tem como finalidade a identificação de possíveis tabelas em um JSON, gerando querys para suanormalizaçãovia Amazon Athena.InstallationInstalar o pacote via PyPI:pip install nice_parserUsageGerar QueryI. Importar Módulosfrom nice import MappingJsonII. Criar Query a partir de JSON, passando como parâmetro aorigemde seu dado, o nome dodatabasee databelaparser = MappingJson('path/sample.json', databaseName='database', tableName='table')
database = parser.parse()
query = database.createQuery()
query.Save('output.sql', database)UmJSONcom os seguintes valores:{"shipping_address":{"province_code":"TX","latitude":-87.561414},"buyer_accepts_marketing":true,"token":"a4b8c15d16e42","total_weight":0,"line_items":[{"destination_location_id":4815162342,"requires_shipping":true,"variant_title":"rb_nice"}]}Será convertido e disponibilizado em um arquivo.sql, como no exemplo abaixo:/* ---------------------------------------------** Descrição: Tabela Principal - tableName      ** ---------------------------------------------*/SELECTCAST("json_extract"(table,'$.shipping_address.province_code')ASVARCHAR)"shipping_address_province_code",CAST("json_extract"(table,'$.shipping_address.latitude')ASDECIMAL)"shipping_address_latitude",CAST("json_extract"(table,'$.buyer_accepts_marketing')ASBOOLEAN)"buyer_accepts_marketing",CAST("json_extract"(table,'$.token')ASVARCHAR)"token",CAST("json_extract"(table,'$.total_weight')ASBIGINT)"total_weight"FROMdatabase.table/* ---------------------------------------------** Descrição: Tabela Secundária - tableName     ** ---------------------------------------------*/SELECTCAST("json_extract"("line_items",'$.destination_location_id')ASBIGINT)"destination_location_id",CAST("json_extract"("line_items",'$.requires_shipping')ASBOOLEAN)"requires_shipping",CAST("json_extract"("line_items",'$.variant_title')ASVARCHAR)"variant_title",FROM(database.tableCROSSJOINUNNEST(CAST("json_extract"(table,'$.line_items')ASarray(json)))t(line_items))...Getting HelpPara bug report, perguntas ou solicitações, devem ser utilizadas asIssuesdo Bitbucket.ContributingLeia o documentoCONTRIBUTING.mdantes de enviar qualquer Issue ou Pull Request, garantindo assim, que tenhamos todas as informações necessárias para responder efetivamente à sua contribuição.
Package: nicepay
NICEPayPayment Solution is a product of PT IONPAY NETWORKS that provide
various electronic payment over the Internet safely without separate security device.
NICEPay have integrate with Banks via host to host connection for better and faster performance.
Using latest technology, NICEPay confidence to grow your business together.Docs:https://docs.nicepay.co.idInstallationInstall from PyPI:$ pip install nicepayRunning testsTo run the test use:$ python tests.py             # without the response
$ python tests.py show_print  # show the response