Saravanan666/pypi-org huggingface dataset:
Package: fast-installer
fast-installermake fast install configurations using config file:Installpip3installfast-installerUsagecli:fastinstall# this will run current directory config.yaml file.to install all steps:fastisntall-afor more:fastinstall-hconfig fileExampleoptions:-description:"Zshconfigurations"key:zshsteps:-description:"Linkingfiles"links:~/.zshrc.d:assets/zsh/zshrc.d/~/.zshrc:assets/zsh/zshrcshell:-echo "test"-command:ls not_existsallow_fail:true-command:tail -f /dev/nullallow_fail:truetimeout:1# 1 secondsStructureKeyTypeIs RequiredCommentsoptionslistyeslist ofoptionsoptiona dictionary with the following keys:KeyTypeIs RequiredCommentsdescriptionstringyeskeystringyesstepslistyeslist ofstepsThe steps will be run from the first to the last.stepa dictionary with the following keys:KeyTypeIs RequiredCommentsdescriptionstringyeslinksdictionarynomake a soft linkshelldictionary/stringnoexecute shell commandlinksa dictionary where the key is the dest soft link location and value is the source location.Examplelinks:~/.zshrc.d:assets/zsh/zshrc.d/shellexecute a shell command.
can be either a string (which is the shell command to be executed).
or a dictionary:KeyTypeIs RequiredCommentscommandstringyescommand to be executedallow_failboolnoallow command to fail - default Truetimeoutintnotimeout for the command - default 10 seconds
Package: fast-intensity
fast-intensityauthors: Thomas A. Lasko, Jacek Bajor, John StillOverviewFast density inference. Generates intensity curves from given events.InstallationWe only support Python 3 and above.  We recommend usingfast_intensityin avirtual environment; however,
if you choose to install to a system-wide version of Python, be aware that some
distributions will alias Python 3'spipaspip3.  You should be able to
verify which Pythonpipis associated with by runningpip --version.If you prefer to install a precompiled binary, we provide wheels for OS X and
Linux (via themanylinuxproject).  The
basic pip install command line$ pip install fast-intensityshould prefer one of our prebuilt binaries. Installation from source requires
an environment with Cython, numpy, and scipy preinstalled.$ pip install cython numpy scipyThen you may install a release from source by specifyingnotto use a binary:$ pip install fast-intensity --no-binary fast-intensity(Yes, it is necessary to specifyfast-intensitytwice.)  Alternately, to
install the bleeding edge version:$ git clone https://github.com/ComputationalMedicineLab/fast-intensity.git
$ cd fast-intensity
$ pip install -e .Usage%matplotlibinline%load_extcythonimportmatplotlib.pyplotaspltimportnumpyasnpimportdatetimeasdt# If working locally rather than from a wheel or other binaryimportpyximportpyximport.install(language_level=3)fromfast_intensityimportinfer_intensitynp.random.seed(42)# Specify a series of 100 events spread over a yeardays=np.arange(0.0,365)np.random.shuffle(days)events=np.sort(days[:100])# Specify times (as reals) where we want to calculate the intensity of event occurrencegrid=np.linspace(1,365,num=12)# Generate the intensity curve - the unit is events per time unitcurve=infer_intensity(events,grid)print(curve)#     array([0.38953   , 0.27764734, 0.33549508, 0.27285165, 0.22284481, 0.16997545,#            0.26651725, 0.23580527, 0.23351076, 0.25272662, 0.33146159, 0.28486727])plt.style.use('ggplot')fig,ax=plt.subplots(figsize=(9,9))ax.scatter(events,np.zeros(len(events)),alpha='0.4',label='Events')ax.scatter(grid,np.zeros(len(grid))+0.025,label='Grid')ax.plot(grid,curve,label='Intensity')plt.legend()plt.show()You can see how the intensity graph dips in the middle, where events are more thinly spaced,
and rises near the beginning (where we have a high density of events).
Package: fastiqa
ArXiv|Website|Setup|DocumentPaQ-2-PiQCode for our paper"From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality"@article{ying2019patches,
  title={From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality},
  author={Ying, Zhenqi
  ang and Niu, Haoran and Gupta, Praful and Mahajan, Dhruv and Ghadiyaram, Deepti and Bovik, Alan},
  journal={arXiv preprint arXiv:1912.10088},
  year={2019}
}Featuressupport cpu-only, just install pytorch-cpu and followed by fastaiSetuppython 3.6/3.7python --versioninstall prerequisites bypip install -r requirements.txtDownload the pretrained models and put them under a folder namedmodelsRoIPoolModel-fit.10.bs.120.pthOpen a Jupyter notebook and rundemo.ipynb:arrow_up: back to top
Package: fastjet
Official FastJet bindings to Python and Awkward Array.Main features of Fastjet:Contains Vectorized, Out-of-core Vectorized (dask), as well as Non-Vectorized interface for Fastjet.Compiled against the complete Fastjet library in C++.Has Awkward Array, Vector, and optionally Dask as dependencies.Provides the functionality to cluster multiple events at a time.Input data can be in any coordinate system.InstallationThe package can be installed from PyPI using the following command:python-mpipinstallfastjetTutorialFor a tutorial please look at the tutorial section of readthedocs page of this package.Installation For DevelopersClone this repository recursively to get the dependencies.git clone --recursive https://github.com/scikit-hep/fastjet.gitBuild dependenciesThere are still external build-time dependencies for the C++ components offastjetthat need to be installed on the build machine.
To install the build-time dependencies run the following installation commands for your respective operating system:Debian/Ubuntusudoapt-getupdate&&sudoapt-getinstall-ylibboost-devlibmpfr-devlibgmp-devswigautoconflibtoolBuild and installThen you can build it using the following command:python-mpipinstall'.[test]'
Package: fast-jieba
FastJiebafast_jieba for python, create by pyo3, use rust cratejieba_rsimportfast_jiebafromfast_jieba.analyseimportextract_tags,textrankwords=fast_jieba.tokenize("小明就读北京清华大学物理系")print(words)tags=extract_tags("小明就读北京清华大学物理系")print(tags)tags=textrank("小明就读北京清华大学物理系")print(tags)print("**************")texts=["小明就读北京清华大学物理系"for_inrange(4)]words=fast_jieba.batch_tokenize(texts)print(words)words=fast_jieba.batch_cut(texts)print(words)words=fast_jieba.batch_posseg(texts)print(words)
Package: fastjmd95
This package provides aNumbaimplementation the JMD95 equation of state.Usagefastjmd95 provides three ufuncs:>>>fromfastjmd95importrho,drhods,drhodt>>>rho(35.5,3.,3000.)1041.83267>>>drhodt(35.5,3.,3000.)-0.17244>>>drhods(35.5,3.,3000.)0.77481TutorialTutorial notebook located atdoc/fastjmd95_tutorial.ipynb.
Package: fast-job
fast_jobname = "fast_job"description = "Provides scheduling apis and scheduling and task-related services"authors = ["Euraxluoeuraxluo@qq.com"]license = "The MIT LICENSE"repository = "https://github.com/Euraxluo/fast_job"coverage : 74%version : 0.2.*installpip install fast-jobUseAge1.wrapper function to build taskfrom fast_job import *


@schedule.task('task1', summer="test_task_1", tag='test', description="test_task_1")
def test(tag: int):
    print({"msg": "test_task_1", "tag": tag})
    return {"msg": "test_task_1", "tag": tag}


@schedule.task('task2', summer="test_task_2", tag='test', description="test_task_2")
def test2(tag: int):
    print({"msg": "test_task_2", "tag": tag})
    return {"msg": "test_task_2", "tag": tag}


@schedule.task('task3', summer="test_task_3", tag='test', description="test_task_3")
def task3(tag: int):
    raise Exception(str({"msg": "test_task_2", "tag": tag}))2.include in your fastApifromloguruimportloggerfromfastapiimportFastAPIfromexample.jobsimportschedule,fast_job_api_routerfromexample.conftestimportrdbasredisapp=FastAPI()@app.on_event("startup")asyncdefregistry_schedule():schedule.setup(prefix='test:',logger=logger,redis=redis,distributed=True)@app.on_event("shutdown")# 关闭调度器asyncdefshutdown_connect():schedule.shutdown()prefix="/test"app.include_router(fast_job_api_router,prefix=prefix,tags=["jobs"])# include router
Package: fast-json
Combines best parts of json and ujson for fast serialization.importfast_jsonprint(fast_json.dumps({"foo":"bar","now":datetime.datetime.now()}))Serializing custom typeimportfast_jsonfromcollectionsimportnamedtupleMyType=namedtuple("MyType",["name","value"])@fast_json.convert.register(MyType)def_(value):return"name={0.name}value={0.value}".format(value)print(fast_json.dumps({"one":MyType(name="foo",value="bar")}))
Package: fast-json-pointer
ImplementsRFC 6901JSON pointers, andrelative JSON pointerresolution.This module is not necissarily fast (yet), but there are enough variations onjson-pointerin pypi to merit picking some prefix to differentiate, and “fast”
seemed a relatively short and punchy choice.If you need this toreallybe fast, open an issue and let me know. I want to do
a rust extension module at some point. That ought to be fast enough to claim we’re
fast.PackagebuildGit
Package: fastjsonrpc
IntroductionA library for writing asynchronous JSON-RPC servers and clients in Python,
using Twisted. It aims to be as simple and easy to understand (and hack)
as possible.Read more about JSON-RPC athttp://www.jsonrpc.org/FeaturesSupport for HTTP and HTTPS as protocol (via twisted.web) and for more
lightweight netstring (seehttp://cr.yp.to/proto/netstrings.txt)Support for HTTP authentization - only basic, not digest; use SSL for
encrypted credentials.Support for HTTP persistent connections and Factory to create proxies
to different URLsSupport for HTTP compressionFull standards compliance.Support both JSON-RPC standards at once - great if you don’t control your
clients.‘Just work’ with various clients (i.e. PHP, C++, JavaScript…).Detailed examples :-) .TODOMore tests, better (functionality) coverage. This applies to every project,
always :-) .Refactoring, mostly tests.test_server and test_jsonrpctest_client and test_netstringclientNotesJSON is well readable for a human. It’s easy to use Wireshark
(www.wireshark.org) or similar for debugging.JSON-RPC version 1 doesn’t talk about batch requests. In order to support both
standards at once, fastjsonrpc supports it just like in version 2. It ties
JSON-RPC version to the method call, not the request as a whole.I didn’t test the JSON Class hinting, as mentioned in the version 1 spec. I
leave this to the JSON parsing capabilities of respective libraries.SSL client test raises an error after shutDown. Looks like a bug in Trial
and we can ignore it.
Package: fastjws
FastJWSUsing this module you can quickly and easily perform JWT authentication.
The main advantage is the use of the RSA-256 algorithm for signing tokens.
The library is also great for small microservices.Installpip:poetry:Speed testsActionTimeCreate token0.11 secVerify0.10 secExamples of usingfromfastapiimportFastAPI,DependsfromfastjwsimportSingJWT,AuthJWTfrompydanticimportBaseModelimportdatetime# Open keys !!!not necessarily like here)private_key=open('rs256.rsa',"r").read()public_key=open('rs256.rsa.pub',"r").read()# Creating an instance of a class.jwt_sing=SingJWT(private_key)jwt_auth=AuthJWT(public_key)# Pydantic model for example, this is optional)classAuthScheme(BaseModel):fake_login:strfake_password:strapp=FastAPI()@app.post("/token")deftoken(fake_data:AuthScheme):# Retrieving data from a database.fake_user:dict=fake_database# Selecting the token lifetime.expire=datetime.timedelta(minutes=15)# Receiving a token!)token:str=jwt_sing(fake_user,expire)@app.get("/data")asyncdefget_data(token_data=Depends(jwt_auth)):returntoken_data
Package: fastjwt
fastjwtFastAPI Plugin for reusable JWT Authentication Managementfastjwtenables easy JSON Web Tokens management within your FastAPI application.fastjwtis heavily inspired from its Flask equivalentFlask-JWT-ExtendedDocumentation:https://ocarinow.github.io/fastjwt/FeaturesEncode/Decode JWT for application AuthenticationAutomatic JWT detection in requestJWT in HeadersJWT in CookiesJWT in Query stringsJWT in JSON BodyImplicit/Explicit token refresh mechanismFreshness state of tokenRoute protectionToken type based protection(access/refresh)Token freshness protectionPartial route protectionHandle custom user logic for revoked token validationHandle custom logic for token recipient retrieval(ORM, pydantic serialization...)Provide FastAPI compliant dependency injection APIAutomatic error handlingSetupRequirementsFastJWT is built on top of the following dependencies:FastAPIas web frameworkPydanticas data validationPyJWTas python implementation of the JSON Web Token standardFastJWT also relies ontyping-extensionsfor backward compatibility(python3.9)Install# With pippipinstallfastjwt# With poetrypoetryaddfastjwt# With pipenvpipenvinstallfastjwtExamplefromfastapiimportFastAPI,DependsfromfastjwtimportFastJWTapp=FastAPI()security=FastJWT()@app.get('/login')deflogin():returnsecurity.create_access_token(uid='foo')@app.get('/protected',dependencies=[Depends(security.access_token_required())])defprotected():return"This is a protected endpoint"DevelopmentWORK IN PROGRESSThe development guide is not available yetContributingWORK IN PROGRESSThe contribution guide is not available yetLicenseThis project is open source and underMIT License
Package: fastk
FASTK_pythonPython interface forFASTK(currently Histex and Profex). Python >= 3 is required.How to install$gitclonehttps://github.com/yoshihikosuzuki/FASTK_python
$cdFASTK_python
$make
$pip3install.How to useQuick usageimportfastk# Histexhist=fastk.histex("/path/to/source[.hist]")# Profexprof=fastk.profex("/path/to/source[.prof]",read_id=1)histex()Signature: fastk.histex(fastk_prefix: str, min_count: int = 1, max_count: int = 100) -> bits.util._counter.RelCounter
Docstring:
Run Histex and return a histogram of k-mer count frequencies.

positional arguments:
  @ fastk_prefix : Prefix of the output files of FastK.

optional arguments:
  @ [min|max]_count : Specify the range of the k-mer count.profex()fastk.profex(
    fastk_prefix: str,
    read_id: int,
    zero_padding: bool = False,
    return_k: bool = False,
) -> Union[List[int], Tuple[List[int], int]]
Docstring:
Run Profex and return the k-mer count profile of a single read.

positional arguments:
  @ fastk_prefix : Prefix of the output files of FastK.
  @ read_id      : Read ID (1, 2, ...)
  @ zero_padding : If True, add (K - 1) zero counts to the prefix.
  @ return_k     : If True, return the value of K as well.
Package: fastkaggle
fastkaggleInstallEither:pip install fastkaggleor:mamba install -c fastai fastkaggle(or replacemambawithcondaif you don’t mind it taking much longer
to run…)How to useCompetitionThis little library is where I’ll be putting snippets of stuff which are
useful on Kaggle. Functionality includes the following:It definesiskagglewhich isTrueif you’re running on Kaggle:'Kaggle'ifiskaggleelse'Not Kaggle''Not Kaggle'It provides asetup_compfunction which gets a path to the data for a competition, downloading it
if needed, and also installs any modules that might be missing or out of
data if running on Kaggle:setup_comp('titanic')Path('titanic')There’s alsopush_notebookto push a notebook to Kaggle Notebooks, andimport_kaggleto use the Kaggle API (even when you’re on Kaggle!) See thefastkaggle.coredocs for details.DatasetsThis section is designed to make uploading pip libraries to kaggle
datasets easy. There’s 2 primary high level functions to be used. First
we can define our kaggle username and the local path we want to use to
store datasets when we create them.Usage tipThe purpose of this is to create datasets that can be used in no
internet inference competitions to install libraries usingpip install -Uqq library --no-index --find-links=file:///kaggle/input/your_dataset/lib_path=Path('/root/kaggle_datasets')username='isaacflath'List of LibrariesWe can take a list of libraries and upload them as seperate datasets.
For example the below will create alibrary-fastcoreandlibrary-timmdataset. If they already exist, it will push a new
version if there is a more recent version available.libs=['fastcore','timm']create_libs_datasets(libs,lib_path,username)Processing fastcore as library-fastcore at /root/kaggle_datasets/library-fastcore
-----Downloading or Creating Dataset
-----Checking dataset version against pip
-----Kaggle dataset already up to date 1.5.16 to 1.5.16
Processing timm as library-timm at /root/kaggle_datasets/library-timm
-----Downloading or Creating Dataset
-----Checking dataset version against pip
-----Kaggle dataset already up to date 0.6.7 to 0.6.7
CompleteThis creates datasets in kaggle with the needed files.requirements.txtWe can also create a singular dataset with multiple libraries based on arequirements.txtfile for the project. If there are any different
files it will push a new version.create_requirements_dataset('test_files/requirements.txt',lib_path,'libraries-pawpularity',username)Processing libraries-pawpularity at /root/kaggle_datasets/libraries-pawpularity
-----Downloading or Creating Dataset
Data package template written to: /root/kaggle_datasets/libraries-pawpularity/dataset-metadata.json
-----Checking dataset version against pip
-----Updating libraries-pawpularity in Kaggle
CompleteThis creats a dataset in kaggle with the needed files.
Package: fast-keywords
Fast Keyword IdentificationFast keyword identification with n-gram vector string matching.OverviewThis package provides a generic pipeline for fuzzy-identification of keywords in large document collections. For example, if you wish to find all occurrences of the keyword "Walmart" in a large document collection, but expect some typos or variations in spelling, this module will allow you to quickly identify all matches. The matcher is based on a character n-gram vector model rather than the slower string edit distance. The module is originally intended for brand monitoring applications.Installationpip install fast-keywordsCLIpython -m fast_keywords --helpUsagepython -m fast_keywords -k keywords.txt -c corpus.csv -l english -b 0.75Training Models for Additional FilteringWhile the main script will search for keywords in the provided corpus, filtering according to the match confidence, you can also train and use simple text classifiers as an additional filter to remove dubious matches. For example, if you are searching for the company "apple," but find your searches frequently return references to fruit, you can train a model which will exclude those matches based on the surrounding text of matched keywords. Instructions for model training and usage are provided below.After searching for keywords you will find a column "Match is Invalid" in theoutput.xlsxfile.Modify this column, changing matches which should be filtered out to "1".Train a new model using the--trainflag, providing the modifiedoutput.xlsxfile and the original keywords file, as in the command below.python -m fast_keywords --train -d output.xlsx -k keywords.txtThe train command will create a directory with severalmodel.pbfiles which you can distribute and use for filtering. You should use the absolute path to this containing directory as the model path passed with the-mflag.You can use your models when predicting as in the below command. You can also pass previously-trained models using the-mflag to continue training on new data when running the train command.python -m fast_keywords -k keywords.txt -c corpus.csv -l english -b 0.75 -m model.pbNotesYour input .csv must have a "text" column containing documents.The main script will create a a fileoutput.xlsxsummarizing identified keywords and their metadata.
Package: fastlab
FastLabAn extension library for FastAPI frameworkFeaturesLoggingModelsResponsePageDataUtilsTimeUtilsRoutersHealthRouterDecoratorsWithEnvConfigInstallationusepipto install the package:pipinstallfastlabGetting startedLoggingEasy to log string to console, see more:https://docs.python.org/3/library/logging.htmlfromfastlabimportlogslogs.warning('warn')# 2021-12-18 14:23:31.000  WARNING 88493 --- [  MainThread] test_logs        : warnlogs.info('info')# 2021-12-18 14:23:31.000     INFO 88493 --- [  MainThread] test_logs        : infologs.error('error')# 2021-12-18 14:23:31.000    ERROR 88493 --- [  MainThread] test_logs        : errorModelsCommon Models🔰 ResponsefromfastapiimportFastAPIfrompydanticimportBaseModelfromfastlab.modelsimportResponseclassItem(BaseModel):name:strversion:strapp=FastAPI()@app.get("/item",response_model=Response[Item])asyncdefitem():returnResponse(data=Item(name='fastlab',version='0.1.0'))Gethttp://localhost:8080/itemresponse:{"code":0,"message":"","data":{"name":"fastlab","version":"0.1.0"}}🔰 PageDatafromfastapiimportFastAPIfrompydanticimportBaseModelfromfastlab.modelsimportResponse,PageDataclassItem(BaseModel):name:strversion:strapp=FastAPI()@app.get("/items",response_model=Response[PageData[Item]])asyncdefitems(skip:int=0,limit:int=10):total=100data=[Item(name=f'fastlab-{i}',version=f'0.1.{i}')foriinrange(skip,skip+limit)]returnResponse(data=PageData(skip=skip,limit=limit,total=total,has_more=total>skip+limit,data=data))Utils🔰 TimeUtilsfromfastlab.utilsimportTimeUtils# Print now timestamp: 1639732030521print(TimeUtils.timestamp())Routers🔰 HealthRouterAPI for health check, endpoint/health.fromfastapiimportFastAPIfromfastlab.routersimportHealthRouterapp=FastAPI()app.include_router(HealthRouter)Decorators🔰 WithEnvConfigReplace the configuration with system environment variables. Follows:Change the setting name to uppercasePrefix it withprefixsettingEscape any underscores (_) by duplicating themConvert all periods (.) to underscores (_)fromfastlab.decoratorsimportWithEnvConfig@WithEnvConfig(prefix='FL_')defload_config():return{'name':'fastlab','version':'0.2.1','extra':{'memory_lock':False}}conf=load_config()For example,FL_EXTRA_MEMORY__LOCK=truetransformconf['extra']['memory_lock']asTrueTestingInstall this package locallypython setup.py developRun test casepython tests/test_logs.py
Package: fast-langdetect
fast-langdetectPython 3.8-3.11 support only.80x faster and 95% accurate language identification with FasttextThis library is a wrapper for the language detection model trained on fasttext by Facebook. For more information, please
visit:https://fasttext.cc/docs/en/language-identification.htmlThis repository is patched
fromzafercavdar/fasttext-langdetect, adding
multi-language segmentation and better packaging
support.Facilitates more accurate TTS implementation.Installationpipinstallfast-langdetectUsageFor more accurate language detection, please usedetect(text,low_memory=False)to load the big model.Model will be downloaded in/tmp/fasttext-langdetectdirectory when you first use it.fromfast_langdetectimportdetect_langsprint(detect_langs("Hello, world!"))# [en:0.9999961853027344]print(detect_langs("Привет, мир!"))# [ru:0.9999961853027344]print(detect_langs("你好，世界！"))# [zh:0.9999961853027344]Advanced usagefromfast_langdetectimportdetect,parse_sentence,detect_multilingualprint(detect("Hello, world!"))# {'lang': 'en', 'score': 0.1520957201719284}print(detect_multilingual("Hello, world!你好世界!Привет, мир!"))# [{'lang': 'ru', 'score': 0.39008623361587524}, {'lang': 'zh', 'score': 0.18235979974269867}, {'lang': 'ja', 'score': 0.08473210036754608}, {'lang': 'sr', 'score': 0.057975586503744125}, {'lang': 'en', 'score': 0.05422825738787651}]print(parse_sentence("你好世界！Hello, world！Привет, мир！"))# [{'text': '你好世界！Hello, world！', 'lang': 'ZH', 'length': 18}, {'text': 'Привет, мир！', 'lang': 'UK', 'length': 12}, {'text': '', 'lang': 'EN', 'length': 0}]AccuracyReferences to thebenchmark