Saravanan666/pypi-org huggingface dataset:
Package: readycheck
readycheckPython package providing utilities to check classes attributes when accessing it.InstallationUse the package managerpipto install readycheck:pipinstallreadycheckDependenciesPython≥ 3.5GoalThe goal of this module is to allow to add custom checks when accessing class
attributes.It is designed to build classes that store objects not available at import time
(e.g. later fetched from a distant service).  It avoid us the pain to check if
the connection has been established each time we need these objects: trying to
access them will automagically raise an exception if they are not ready.UsageThis package's main external API in theReadyCheckclass.This class isnot meantto be instantiated (see documentation for details),
but to be subclassed using a custom check function:fromreadycheckimportReadyCheckclassUser(ReadyCheck,check_type=dict,check=lambdaval:"name"inval):john={"id":"<john_id>","area":2}jane={"id":"<jane_id>","area":5}Usage:>>> User.john
Traceback (most recent call last):
  ...
readycheck.NotReadyError: 'john' is not ready yet!
>>> john_data = User.get_raw("john")
>>> User.john = some_lib.load_user(john_data)
>>> User.john
{'id': '<john id>', 'area': 2, 'name': 'John Doe', 'subscribed': False}Readthe docsfor more information.ContributingPull requests are welcome. Do not hesitate to get in touch with me (see below)
for any question or suggestion about this project!LicenseThis work is shared underthe MIT license.© 2022 Loïc Simon (loic.simon@espci.org)
Package: readycloud
Python client for ReadyCloud API.Free software: BSD licenseDocumentation:https://readycloud-python-client.readthedocs.org.FeaturesGET, POST, PUT, PATCH, DELETE requests to ReadyCloud.CRUD functions for Order, WebHooksSupport for Python2 and Python3.InstallationpipinstallreadycloudUsagefromreadycloudimportReadyCloudrc=ReadyCloud(token='your token')orders=rc.get_orders()order=orders['objects']['0']order['message']='New message'rc.update_order(order['id'],order)rc.delete_order(order['id'])boxes=rc.get('/api/v1/boxes')History0.3.1 (2015-11-24)Addedpatchmethod to perform PATCH HTTP requests.0.3.0 (2015-10-09)Added support for the 2.0 API.0.2.0 (2014-08-16)Added register webhooks methodsFixed few issues0.1.0 (2014-08-12)First release on PyPI.
Package: readyrector
Easy to implement redirect middleware, which loads redirects from a configurable backend.InstallationpipinstallreadyrectorUsage# settings.pyINSTALLED_APPS=(# ...'readyrector',# ...)# put at the end of the middleware list as a last resortMIDDLEWARE=(# ...'readyrector.middleware.RedirectDatabaseMiddleware',)RedirectDatabaseMiddlewareGets redirects from database records. Implement a view or API to manage these records.RedirectURLConfMiddlewareGets redirects from a URL conf file. Requires settingREADYRECTOR_URLCONFSettingsREADYRECTOR_URLCONFDefault:''Required whenRedirectURLConfMiddlewareis used. Expects a path to a URL conf file.
Package: ready-set-deploy
Ready-Set-Deploy!RSD is a deployment framework designed to work offline-first without a centralized controller.
RSD is not an execution framework, nor does it specify how desired state is defined.UsagersdgatherPROVIDER1.ID>provider1_state.json
rsdgatherPROVIDER2.ID>provider2_state.json
rsdcombineprovider1_state.jsonprovider2_state.json>host_state.json
rsddiffhost_state.jsonrole_state.json>plan.json
rsdcommandsplan.json# As individual steps with some shortcutsbash-x<(rsddiff<(rsdprovidersrole_state.json|rsdgather-all)role_state.json|rsdcommands-)# Or all together in a single commandbash-x<(rsdapply-localrole_state.json)DesignRSD is split into three basic parts: gathering the state, operations on the theoretical state, and rendering a diff into commands.
The main design goal is to minimize computational effort and enabling offline manipulation of the ideal system configuration state.
Package: ready-steady-go
Ready, Steady, Go!It’s fall 2022, and for the first time in years, buying a GPU for Deep
Learning experiments does not sound too crazy.Now, how do we pick one?Keep in mind, performance depends on many factors, not least your CPU
and often SSD.For experiments, you might be better off with 2 cheaper GPUs - one to
run in background, the other used interactively.My ResultsimportwandbimportpandasaspdfrommatplotlibimportpyplotaspltimportseabornassnsimportnumpyasnpI ran the benchmark on a variety of GPUs fromvast.ai. The results are automatically synced toWeights & Biases.sns.set_theme(style="whitegrid")sns.set_color_codes("pastel")api=wandb.Api()runs=api.runs("xl0/ready-steady-go")summaries=[dict(r.summary)|{"id":r.id}forrinrunsifr.state=="finished"]df=pd.DataFrame.from_records(summaries)df=df[["device_name","model","bs","fp16","throughput"]]df["fp16"]=df["fp16"].apply(lambdax:"FP16"ifxelse"FP32")df=df.replace({"device_name":{"NVIDIA*":"","GeForce":"","Tesla":"","-":" "}},regex=True)df.dropna(inplace=True)# For each model, normalize performance by top throughput.formodelindf.model.unique():df.loc[df.model==model,"throughput"]/=df.loc[df.model==model,"throughput"].max()df["throughput"]*=100# For each device+model+fp, get the index of the entry with the highest throughput.max_bs_idx=df.groupby(["device_name","model","fp16"])["throughput"].idxmax()formodelindf.model.unique():f,ax=plt.subplots(figsize=(15,6))tops=df.loc[max_bs_idx].query(f"model == '{model}'").sort_values("throughput",ascending=False)sns.set_color_codes("pastel")sns.barplot(ax=ax,data=tops.query("fp16 == 'FP16'"),x="throughput",y="device_name",label="FP16",color="b",alpha=1)sns.set_color_codes("muted")sns.barplot(ax=ax,data=tops.query("fp16 == 'FP32'"),x="throughput",y="device_name",label="FP32",color="b",alpha=0.8,order=tops.query("fp16 == 'FP16'").sort_values("throughput",ascending=False).device_name)ax.legend(ncol=2,loc="lower right",frameon=True)ax.set(ylabel=None,xlabel=None,title=model)f,ax=plt.subplots(figsize=(15,6))tops=df.loc[max_bs_idx].sort_values("throughput",ascending=False)# topsfp16s=df.loc[max_bs_idx].query("fp16=='FP16'")grouped=fp16s.groupby(["device_name"],as_index=False)["throughput"]display_order=grouped.mean().sort_values("throughput",ascending=False)sns.set_color_codes("pastel")tt=sns.barplot(ax=ax,data=tops.loc[tops.fp16.eq('FP16')],x="throughput",y="device_name",label="FP16",color="b",errwidth=0,order=display_order.device_name)# f, ax = plt.subplots(figsize=(15, 6))sns.set_color_codes("muted")sns.barplot(ax=ax,data=tops.loc[tops.fp16.eq('FP32')],x="throughput",y="device_name",label="FP32",color="b",errwidth=0,order=display_order.device_name)ax.legend(ncol=2,loc="lower right",frameon=True)_=ax.set(ylabel=None,xlabel=None,title="Average between all models")
Package: reagent
Applied Reinforcement Learning @ FacebookOverviewReAgent is an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook. ReAgent is built in Python and uses PyTorch for modeling and training and TorchScript for model serving. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, and optimized serving. For more detailed information about ReAgent see the white paperhere.The platform was once named "Horizon" but we have adopted the name "ReAgent" recently to emphasize its broader scope in decision making and reasoning.Algorithms SupportedDiscrete-ActionDQNParametric-Action DQNDouble DQN,Dueling DQN,Dueling Double DQNDistributional RL:C51andQR-DQNTwin Delayed DDPG(TD3)Soft Actor-Critic(SAC)InstallationReAgent can be installed via. Docker or manually. Detailed instructions on how to install ReAgent can be foundhere.UsageDetailed instructions on how to use ReAgent Models can be foundhere.The ReAgent Serving Platform (RASP) tutorial is availablehere.LicenseReAgent is released under a BSD 3-Clause license.  Find out more about ithere.Citing@article{gauci2018horizon,
title={Horizon: Facebook's Open Source Applied Reinforcement Learning Platform},
author={Gauci, Jason and Conti, Edoardo and Liang, Yitao and Virochsiri, Kittipat and Chen, Zhengxing and He, Yuchen and Kaden, Zachary and Narayanan, Vivek and Ye, Xiaohui},
journal={arXiv preprint arXiv:1811.00260},
year={2018}
}
Package: reagex
The goal ofreagex(from“readable regular expression”)
is to suggest a way for writing complex regular expressions with
many capturing groups in a readable way.At the moment, it contains just one very simple function
(calledreagex) and an utility function, but any function
which could be useful for writing readable patterns is welcome.Note:Publishing this ridiculously small project is an excuse to familiarize
with python packaging, DevOps tools and the entire workflow behind the publication
of an open-source project.
The project template was generated usinghttps://github.com/ionelmc/cookiecutter-pylibrary/which is obviously an overkill for a “one-function-project”.Free software: BSD 2-Clause LicenseUsageThe core functionreagexis just a wrapper ofstr.formatand it works
in the same way. See the exampleimportrefromreageximportreagex# A sloppy pattern for an italian address (just to show how it works)pattern=reagex('{_address},{postcode}{city}{province}',# groups starting with "_" are non-capturing_address=reagex('{street}{number}',street='(via|contrada|c/da|c[.]da|piazza|p[.]za|p[.]zza) [a-zA-Z]+',number='snc|[0-9]+'),postcode='[0-9]{5}',city='[A-Za-z]+',province='[A-Z]{2}')matcher=re.compile(pattern)match=matcher.fullmatch('via Roma 123, 12345 Napoli NA')print(match.groupdict())# prints:#   {'city': 'Napoli',#    'number': '123',#    'postcode': '12345',#    'province': 'NA',#    'street': 'via Roma'}Groups starting by'_'are non-capturing. The rest are all named capturing
groups.Why not…Why not using just re.VERBOSE?I thinkreagexis easier to write and to read:with reagex, you first describe the structure of the pattern in terms of groups,thenyou provide a pattern for each group;
with re.VERBOSE you have to define the groups in the exact position they
must be matched: to get the high-level structure of the pattern you may need
to read multiple lines at the same indentation levelwith re.VERBOSE you just write a big string; with reagex you get
syntax highlighting which helps readabilitywhite-spaces don’t need any special treatment“{group_name}” is nicer than “(?P<group_name>)”Installationpip install reagexDocumentationhttps://python-reagex.readthedocs.io/DevelopmentPossible improvements:make some meaningful use of theformat_specin{group_name:format_spec}add utility functions likerepeatedto help writing
common patterns in a readable wayTestingTo run all the tests:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.2 (2018-12-16)Fix little mistake in the example (which is showed in PyPI, so a release
was necessary to update the PyPI page).0.1.1 (2018-12-12)Minor fixes and modifications to documentation0.1.0 (2018-12-08)First release on PyPI.
Package: reahl-stubble
Reahl is a web application framework that allows a Python programmer to work in
terms of useful abstractions - using a single programming language.Stubble (a part of the Reahl development tools) is a collection of
tools for writing stubs in unit tests. Stubble can be used independently
of the Reahl web framework.Using stubs allows one to decouple one unit test from real code
unrelated to it - you write a fake class to take the place of
a real one (which this test is not testing).Stubble ensures, however, that the test will break should the
interface of the stub differ from that of the real class it is a stub
for.
Package: reahl-tofu
Reahl is a web application framework that allows a Python programmer to work in
terms of useful abstractions - using a single programming language.Tofu is part of the Reahl development tools.
Tofu can be used independently of the Reahl web framework.Tofu allows you to have
a hierarchy of test fixtures that iscompletelydecoupled from your
hierarchy of tests or test suites. Tofu includes a number of other
related test utilities. It also includes a plugin for nosetests
that makes using it with nose seamless.Tofu can also be used to run the set_ups of fixtures from the command
line.  This is useful for acceptance tests whose fixtures create data
in databases that can be used for demonstration and user testing.
Package: reahl-web
Reahl is a web application framework that allows a Python programmer to work in
terms of useful abstractions - using a single programming language.This package contains the core of the Reahl framework.With Reahl, programming is done purely in Python, using concepts familiar
from GUI programming—like reusable Widgets and Events. There’s no need
for a programmer to know several different languages (HTML, JavaScript,
template languages, etc) or to keep up with the tricks of these trades.The abstractions presented by Reahl relieve the programmer from the burden
of dealing with the annoying problems of the web: security, accessibility,
progressive enhancement (or graceful degradation) and browser quirks.Install Reahl by installing with extras, eg: pip install “reahl[declarative,sqlite,dev,doc]”
to install everything needed to run Reahl on sqlite, the dev tools and documentation.Seehttp://www.reahl.org/docs/current/tutorial/gettingstarted-install.d.htmlfor
installation instructions.
Package: reahl-web-elixirimpl
Reahl is a web application framework that allows a Python programmer to work in terms of useful abstractions - using a single programming language.Some core elements of Reahl can be implemented for use with different persistence technologies. This is such an implementation based on SqlAlchemy/Elixir.Seehttp://www.reahl.org/docs/current/tutorial/gettingstarted.d.htmlfor installation instructions.
Package: real-brasileiro
Real brasileiroStatus do projeto: Em desenvolvimentoeste projeto tem como objetivo me ajudar na formatação, sanitização e apresentação dos valores com os quais eu diariamente trabalho.Download$pipinstallreal-brasileiroEntradasfromreal_brimportRealprint(Real(10.10),Real(10),Real('R$ 100,55'))Nos testes você consegue conferir melhor em quais formatos a classe aceita os valores.Criando objetofromreal_brimportRealvalor=10real=Real(valor)print(real)você também pode obter o equivalente em centavos usandofromreal_brimportRealvalor=10real=Real(valor)print(real.centavos)A classe retorna float, str e int (int é retornado em centavos)fromreal_brimportRealvalor=10real=Real(valor)print(float(real),int(real),str(real))A classe possui 100% de cobertura de testespytest-cov
Package: realCfg
#realDB
基于sqlite的单机配置管理器###版本日志：1.0.0一个配置项可以有：父配置项子配置项配置项自身的值配置项的值支持如下类型：数字（int、float）字符串（str）布尔值（bool）数组（list）字典（dict）后续会考虑提升配置项大批量增删改的效率###关于作者
Real Hei874u@hotmail.com
Package: realclearpolitics
Python client for RealClearPolitics.Installpip install realclearpoliticsUsageusage: rcp [-h] [--output [OUTPUT]] [--generate-table] url [url ...]

positional arguments:
  url                The url of the polling data.

optional arguments:
  -h, --help         show this help message and exit
  --output [OUTPUT]  The output file name.
  --generate-table   Pass this argument to generate a table.ExamplesGet the US general election results.rcp http://www.realclearpolitics.com/epolls/2016/president/us/general_election_trump_vs_clinton-5491.html --output general.csvDownload multiple polls.rcp http://www.realclearpolitics.com/epolls/2016/president/us/general_election_trump_vs_clinton-5491.html \
> https://www.realclearpolitics.com/epolls/other/president_trump_job_approval_economy-6182.html \
> https://www.realclearpolitics.com/epolls/other/president_trump_job_approval_foreign_policy-6183.htmlAPI UsageSearch for Fox News poll numbers for Trump:fromrcpimportget_polls,get_poll_datafrompprintimportpprintpolls=get_polls(candidate="Trump",pollster="Fox")forpollinpolls:td=get_poll_data(poll['url'])pprint(td)The will return a dictionary structured like this:[
    {
        'data': [{'Biden (D)': '49.6',
                'Date': '3/27 - 7/9',
                'MoE': '--',
                'Poll': 'RCP Average',
                'Sample': '--',
                'Spread': 'Biden +8.5',
                'Trump (R)': '41.1'},
        ...
        }],
    'poll': 'https://www.realclearpolitics.com/epolls/2020/president/us/general_election_trump_vs_biden-6247.html'

]Write a poll to CSV:fromrcpimportget_polls,get_poll_data,to_csvpolls=get_polls(candidate="Biden")[0]data=get_poll_data(polls['url'],csv_output=True)to_csv('output.csv',data)Create table:fromrcpimportget_poll_datafromprettytableimportPrettyTablex=PrettyTable()td=get_poll_data("https://www.realclearpolitics.com/epolls/other/president_trump_job_approval-6179.html")x.field_names=list(td[0]["data"][0].keys())x.align="l"forrowintd[0]["data"]:x.add_row(row.values())print(x)
Package: realdata
realdataA mini-package for people who just want some real time-series data, any real data. Think of this as the alternative toimport np
 x = np.cumsum(np.random.randn(1000))Installpip install realdataUnivariate live data (around 1000 points)from realdata import get_live
values = get_live()Multivariate historical data (around 30,000 points for 20 variables)from realdata import get_historical
df = get_historical()AlternativesSeeother waysto grab the live data directly, or see this shorttutorialon retrieving historical data using the microprediction package.The historical data is fromprecisedata.What's the data?All sorts of things: traffic, electricity, emoji usage, hospital wait times, altitude of UFOs, security wait times at airports, changes in crypto-currencies, ... you get the idea.Streams are live so the next time you get data, it will be different.Can I add to the collection?Yes see thetutorialon making a new stream of data.
Package: realDB
#realDB
支持多个数据库的python统一封装###功能说明版本日志：0.2.2修改了一些发布方面信息不一致的小问题0.2.1将对psycopg2的依赖改为psycopg2-binary0.2.0能够完全正常的打包上传pypi支持 all_tables 方法查询指定schema下面所有的表0.1.1/2/3/4与0.1.0没有区别，修改了导致上传失败的setup.py的一些问题0.1.0目前支持：sqlite、pg后续会支持 mysql、mariadb###关于作者
Real Hei874u@hotmail.com
Package: real-deepfakes-detect
The rapid development of deep learning methods enables the generation of fake images and videos. As one of the most impressive topics in the field, Deepfake has gained a lot of attention over the years. However, the malicious use of videos with fake faces generated by Deepfake, such as to defame or attack face recognition systems, raises serious social and security concerns. To address this issue, our project aims to propose a novel and easy-to-use Deepfake video detection toolkit and improve the generalization capabilities of the current state-of-the-art methods. Specifically, we propose a learning framework based on a two-stream network, combined with spatial-temporal and frequency models. The spatial-temporal model, which is based on X3D visual backbone, captures the RGB feature. The frequency model extracts frequency features by applying patch-wise Discrete Cosine Transform (DCT) operation. Extensive experiments on the Faceforensics++ dataset show that our method reaches the state-of-the-art face forgery detection performance. An open-source and easy-to-use toolbox for deploying the proposed robust Deepfake detection system is released on GitHub.
Package: real-easypy
easypyeasypy makes python even easier!Many boiler-plate patterns are reduced to simple yet richly featured constructs:concurrency - running asynchronous code easily and manageablyresiliency - retrying or swallowing exceptionssynchronization - poll-and-wait for long-running process to completecollections - filter and choose objects using keywords and predicatesIn addition, there are humanization modules that help both code and logs more user-friendlyrepresting data-size and time unitsrandomizing stringscoloring and structured loggingtokens... And much more!Contributers please readCONTRIBUTING.md.
Package: realestate-com-au-api
realestate-com-au-api🏠Python wrapper for the realestate.com.au APIInstallationUsingPython >= 3.6:$ pip install -e git+https://github.com/tomquirk/realestate-com-au-api.gitExample usagefromrealestate_com_auimportRealestateComAuapi=RealestateComAu()# Get property listingslistings=api.search(location="seventeen seventy, qld 4677",channel="buy")Data classesListingData class for a listing. Seelisting.pyfor reference.Terms and ConditionsBy using this project, you agree to the following Terms and Conditions:UsageThis project may not be used for any of the following:Commercial useSpamStorage of any Personally Identifiable InformationPersonal abuse (i.e. verbal abuse)LegalThis code is in no way affiliated with, authorized, maintained, sponsored or endorsed by REA Group or any of its affiliates or subsidiaries. This is an independent and unofficial API. Use at your own risk.
Package: realestate-data
Realestate DataA tool to request real estate data from an unofficial realestate.com.au
"API".Installationpipinstallrealestate-dataUsageBuild out schematics to represent the search query you are after.Seeherefor available options, or take a look at ./realestate_data/schematics.pyThe below creates a search object that representsUnits and apartments for salein Melbourne Victoria, 3000and surrounding suburbsprice between $500,000 and $1,000,000minimum of 1 parking spaceminimum of 1 bathroomminimum of 2 bedrooms>>>fromrealestate_dataimportSearch,Locality,PriceRange,Filters>>>>>>p=PriceRange()>>>p.minimum=500000>>>p.maximum=1000000>>>>>>l=Locality()>>>l.locality='Melbourne'>>>l.subdivision=Locality.SUBDIVISION_VIC>>>l.postcode='3000'>>>>>>f=Filters()>>>f.property_types=[Filters.PROPERTY_TYPE_APARTMENT,Filters.PROPERTY_TYPE_UNIT]>>>f.surrounding_suburbs=True>>>f.minimum_bedrooms=2>>>f.minimum_bathrooms=1>>>f.minimum_parking_spaces=1>>>f.price_range=p>>>>>>s.channel=Search.CHANNEL_BUY>>>s.localities=[l]>>>s.filters=fCall validate() on the search object to ensure its valid. It will return
nothing if valid and an error if not>>>s.validate()Call to_primitive() to view the search object>>>s.to_primitive(){'channel':'buy','localities':[{'locality':'Melbourne','subdivision':'VIC','postcode':'3000'}],'filters':{'property-types':['apartment','unit'],'minimum-bedrooms':1,'minimum-bathrooms':1,'minimum-parking-spaces':1,'surrounding-suburbs':True,'price-range':{'minimum':500000,'maximum':1000000}}}Pass the created Search object to the paged_results generator. The generator
yields the json returned from each paged request.>>>fromrealestate_dataimportpaged_results>>>paged_data=[pageforpageinpaged_results(s)]Or use the Generator how ever you want>>>paged_data=paged_results(s)>>>page_one=next(paged_data)>>>page_two=next(paged_data)>>>forpageinpaged_results(s):>>>print(page)TodoTestingAuthorJeremy Storerstorerjeremy@gmail.com
Package: real-estate-scrape
real-estate-scrapeGet the estimated value of a property from Redfin and ZillowAutomated usageTo make charts like this by getting the value of a property once a day, seemikepqr/real-estate-scrape-eg.Advanced/manual usageExportREDFIN_URLandZILLOW_URL. To optionally use scraperapi, also exportSCRAPERAPI_KEY. Then to scrape and append to data.csv in the current working
directory:$ pip install real-estate-scrape
$ real-estate-scrapeWhyRedfin and Zillow both give you an estimate of the current market value of your
home. That estimate is given to the nearest dollar (!) and comes with a chart
that claims to be the history of that estimate. That historical chart is a lie.
The estimate for today bounces around a lot, but they retcon the history to
pretend it doesn't. The precision of today's estimate and the smoothness of the
chart make the estimate look ridiculously trustworthy. This scraper captures the
bouncing, which gives you a good idea how much to trust the current value
(probably good to 10-20% at most).