Saravanan666/pypi-org huggingface dataset:
Package: hover
Explore and label on a map of raw data.Get enough to feed your model in no time.hoverspeeds up data labeling throughembedding + visualization + callbacks.You just need raw data and an embedding to get a map.Explore the map to find "zones" that are easy and those that are tricky.Start the conquest of your data by coloring the zones through wisdom!:rocket: Live DemosWith codeedit & run code in your browser to get a labeling interface, with guides along the way.Without codego directly to a labeling interface, pre-coded and hosted on Binder.:sparkles: Features(click bullets to show GIF)It's fast because it labels data in bulk.:telescope: A semantic scatter plot of your data for labeling, equipped withTooltipfor each point on mouse hoverTable viewfor inspecting all selected pointsTogglebuttons that clearly distinguish data subsetsSearchwidgets for ad-hoc data highlightIt's accurate because multiple angles work together.:microscope: Supplementary views to use in conjunction with the annotator, includingFinder:filter data by search criteriaSoftlabel:active learning by in-the-loop model prediction scoreSnorkel:custom functions for labeling and filteringIt's flexible (and fun!) because the process never gets old.:toolbox: Additional tools and options that allow you toGo tohigher dimensions(3D? 4D?) and choose your xy-axesConsecutively selectacross areas, dimensions, and viewsKick outliersandfix mistakes:bulb: Click on the bullet points above to view GIFs!:package: InstallPython: 3.8+OS: Linux & Mac & WindowsPyPI (for all releases):pip install hoverConda-forge (for 0.6.0 and above):conda install -c conda-forge hoverFor Windows users, we recommendWindows Subsystem for Linux.On Windows itself you will needC++ build toolsfor dependencies.:book: ResourcesTutorialsBinder repoChangelogDocumentation:flags: AnnouncementsJan 21, 2023version 0.8.0 is now available. Check out thechangelogfor details :partying_face:.:bell: RemarksShoutoutsThanks toBokehbecausehoverwould not exist without linked plots and callbacks, or be nearly as good without embeddable server apps.Thanks toPhilip Volletfor sharinghoverwith the community even when it was really green.ContributingAll feedbacks are welcome,especially what you find lacking and want it fixed!./requirements-dev.txtlists required packages for development.Pull requests are advised to use a superset of the pre-commit hooks listed in.pre-commit-config.yaml.CitationIf you have foundhoveruseful to your work, pleaselet us know:hugs:@misc{hover,
  title={{hover}: label data at scale},
  url={https://github.com/phurwicz/hover},
  note={Open software from https://github.com/phurwicz/hover},
  author={Pavel Hurwicz and
    Haochuan Wei},
  year={2021},}
Package: hoverboard
MIT LicenseCopyright (c) 2023 Yossi RosilioPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, 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 THE
SOFTWARE.
Package: hoverpy
HoverPy=======HoverPy speeds up and simplifies Python development and testing that involves downstream HTTP / HTTPS services. It does so by using a high-performance Go caching proxy to capture, simulate, modify and synthesize network traffic... code:: pythonfrom hoverpy import capture, simulateimport requests@capture("requests.db")def captured_get():print(requests.get("http://time.jsontest.com").json())@simulate("requests.db")def simulated_get():print(requests.get("http://time.jsontest.com").json())captured_get()simulated_get()This grants several benefits:-  Increased development speed-  Increased test speed-  Ability to work offline-  A deterministic test environment-  Ability to modify traffic-  Ability to sythesize traffic-  Ability to simulate network latency.. code:: pythonfrom hoverpy import capture, modifyimport requests@simulate("requests.db", delays=[("time.json.com", 1000)])def simulated_latency():print(requests.get("http://time.jsontest.com").json())@modify(middleware="python middleware.py")def modified_request():print(requests.get("http://time.jsontest.com").json())simulated_latency()modified_request()If/when the downstream service you are testing against changes, then you can simply delete your db file, and capture the test results again. Or you could have versioned db files for services that use versioning.HoverPy uses `Hoverfly <http://hoverfly.io>`__ a Service Virtualisation server written in GoLang. For this reason it is rock solid in terms of speed and reliability.Library Support~~~~~~~~~~~~~~~HoverPy works great with the following HTTP clients out of the box:-  tornado-  twisted-  requests-  urllib2-  urllib3-  pysimplesoap-  etc.Since HoverPy can act as a proxy or a reverse proxy, it can easily be made to work with any networking library or framework.Source~~~~~~https://github.com/SpectoLabs/hoverpy/.. image:: hoverpy_logo.pngLicense~~~~~~~HoverPy uses Apache License V2. See LICENSE.txt for more details... |PyPI version| image:: https://badge.fury.io/py/hoverpy.svg:target: https://pypi.python.org/pypi/hoverpy.. |RTD badget| image:: https://readthedocs.org/projects/hoverpy/badge/?version=latest:target: http://hoverpy.readthedocs.io/en/latest/.. |Build Status| image:: https://travis-ci.org/SpectoLabs/hoverpy.svg?branch=master:target: https://travis-ci.org/SpectoLabs/hoverpyContents========.. toctree:::maxdepth: 3pages/installationpages/introductionpages/usage/usageIndices and tables==================* :ref:`genindex`* :ref:`modindex`* :ref:`search`||PyPI version| |RTD badget|  |Build Status||
Package: howabout
Howabout is a Python library to suggest similar strings. Use it in a command-line project or anywhere where simple misspellings occur.fromhowaboutimportbest_matchchoices=['sat','sot']# returns 'sat', since#   'sat' -> 'sate'#   'sot' -> 'sat' -> 'sate'best_match('sate',choices)Examples can be found underexamples/.ArgparsePython’sargparselibrary is not supported out of the box as it cannot be extended or patched.InstallationInstall with pip:$pipinstallhowaboutTestingUsetoxto run the tests.$virtualenv--no-site-packages.python&&source.python/bin/activate$pipinstall-rrequirements.txt$tox
Package: howard
howardPython datatype marshallingThis library marshalls dictionaries [read json] into instances of defined dataclasses and back.i.e.fromdataclassesimportdataclassimporthoward@dataclassclassPerson:name:strage:intmy_dict={'name':'Bob','age':24}person=howard.from_dict(my_dict,Person)assertperson.name=='Bob'assertperson.age==24The main purpose is to use python dataclasses as a schema definition rather than having to hand-write a schema.
Howard does not currently include logic itself for generating json-schema (swagger) object documentation, but that is a long term goal of it.Howard differs from a standard dataclass because it can recursively marshall and unmarshall.
It also supports more types out of the box thandataclasses.asdictdoes. Some supported types:EnumsTypedDictCollections (lists/dictionaries)Datetimeall primitives (int/string/boolean/float)All of the logic for howard is in your dataclass definition, not in howard. Howard just has ato_dictandfrom_dictmethod,
and it bases all decisions off of your dataclass. There is no inheritance on custom types, everything is standard, built-in python. (3.7+)InstallingpipinstallhowardMore examplesFor more examples, you can go look at the tests attests/test_howard.pyHere is a basic example of recursive types and how it can work with howard:fromenumimportEnumfromdataclassesimportdataclass,fieldfromtypingimportListimporthoward@dataclassclassSuit(Enum):heart='h'spade='s'diamond='d'club='c'defvalidate_rank(i:int)->int:lb,ub=1,13iflb<=i<=ub:returniraiseValueError(f'{i}is not between{lb}and{ub}')@dataclassclassCard:rank:int=field(metadata={'howard':{'decoder':validate_rank}})suit:Suit@dataclassclassHand:hand_id:int=0cards:List[Card]=field(default_factory=list)d={'hand_id':2,'cards':[{'rank':2,'suit':'c'},{'rank':10,'suit':'h'}]}# d is a dictionary, now we turn it into the dataclassobj=howard.from_dict(d,Hand)assertisinstance(obj,Hand)assertobj.hand_id==2assertlen(obj.cards)==2assertisinstance(obj.cards[0],Card)assertobj.cards[0].suit==Suit.club# and back to a dictionaryjson_dict=howard.to_dict(obj)In the above example, you can see a couple things.AHandcontains a list ofCard.The sub-objectCardalso gets unmarshalled correctly.TheSuitobject is an enum, which is like a string in json form, but only has 4 possible values.TheCardhas a field calledrankwhich has its own custom decoder.
In this case, the decoder acts as a validator, but can also be used for custom decode logic.FAQWhy not just usedataclasses.asdictandMyDataclass(**my_dict)?dataclasses.asdictdoesn't work on all types, for example, Enums or datetimes.MyDataclass(**my_dict)will not recursively turn the subobjects into their respective datatype.What about custom types?You can specify custom decoders and encoders in a dataclassfield.metadatasection. See example above.
Package: how-are-we-stranded-here
Python package for testing strandedness of RNA-Seq fastq filesEver get RNA-Seq data where the library prep or strandedness has been omitted in the methods?This should save some headaches later in your pipeline and analysis when you realise you’ve used the wrong strandedness setting (RF/fr-firststrand, FR/fr-secondstrand, unstranded)Requirementshow_are_we_stranded_here requires the following packages be installed:kallisto == 0.44.xpython >= 3.6.0RSeQCIt also requires a transcriptome annotation (.fasta file - e.g. ensembl’s .cdna.fasta, or a prebuilt kallisto index), and a corresponding gtf.Sometimes pseudoalignments will not work with newer versions of kallisto. If this is an issue, we suggest downgrading to 0.44.0.Installationpip install how_are_we_stranded_hereUsageFor basic usage, run check_strandedness with a gtf transcript annotation, transcripts fasta file and fastq read files from one sample.check_strandedness --gtf Yeast.gtf --transcripts Yeast_cdna.fasta --reads_1 Sample_A_1.fq.gz --reads_2 Sample_A_2.fq.gzOutputcheck_strandedness will print to console the results of infer_experiment.py (http://rseqc.sourceforge.net/#infer-experiment-py), along with an interpretation.checking strandedness
Reading reference gene model stranded_test_WT_yeast_rep1_1_val_1_1/Saccharomyces_cerevisiae.R64-1-1.98.bed ... Done
Loading SAM/BAM file ...  Total 20000 usable reads were sampled
This is PairEnd Data
Fraction of reads failed to determine: 0.0595
Fraction of reads explained by "1++,1--,2+-,2-+": 0.0073 (0.8% of explainable reads)
Fraction of reads explained by "1+-,1-+,2++,2--": 0.9332 (99.2% of explainable reads)
Over 90% of reads explained by "1+-,1-+,2++,2--"
Data is likely RF/fr-firststrandAny intermediate files are written to a folder in your current working directory derived from the name of the reads_1 file.How it Workscheck_strandedness.py runs a series of commands to check which direction reads align once mapped in transcripts.It first creates a kallisto index (or uses a pre-made index) of your organisms transcriptome.It then maps a small subset of reads (default 200000) to the transcriptome, and uses kallisto’s –genomebam argument to project pseudoalignments to genome sorted BAM file.It finally runs RSeQC’s infer_experiment.py to check which direction reads from the first and second pairs are aligned in relation to the transcript strand, and provides output with the likely strandedness of your data.
Package: howdididolib
AboutLibrary for getting golf booking & fixture information from How Did I Do website (https://www.howdidido.com).Installation$pipinstallhowdididolibUsage$howdidido--help
usage:howdidido[-h][--usernameUSERNAME][--passwordPASSWORD][--save_auth][--bookings][--fixtures][--debug]HowDidIDo.

options:-h,--helpshowthishelpmessageandexit--usernameUSERNAMEProvideusername--passwordPASSWORDProvidepassword--save_authSaveauthenticationcookie--bookingsGetbookings--fixturesGetfixtures--debugEnabledebugloggingLicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.
Package: howe
OverviewThe howe project contains the Howe class. A Howe object is similar to a zip object, except that all the iterators have to run out at the same time. Otherwise an ExceptionGroup (containing the StopIteration exceptions from the iterators that did run out) will be raised. The project is named for E. Howe, one of the intentors of the zipper.InstallationTo install howe, you can usepip. Open your terminal and run:pipinstallhoweLicenseThis project is licensed under the MIT License.LinksDownloadSourceCreditsAuthor: JohannesEmail:johannes-horler@posteo.orgThank you for using howe!
Package: how-fake-accounts
How Fake AccountsPacote python criado para gerar contas fakes, para o primeiro desafio do bootcamp de engenharia de dados da How. O pacote é a implementação de um provider customizado doFaker, conforme sugerido emhttps://faker.readthedocs.io/en/master/#how-to-create-a-provider.As contas feitas simulam o cadastro de usuários em um aplicativo e contém as seguintes informações para cada usuário cadastrado:nomeemailnúmero de telefonecpfdata de nascimentoendereçoempregoHow to installpipinstallhow-fake-accountsHow to useWe have mainly two ways to use the package:as a package itself, to be imported in python scriptsas a command line interfacePackage Interfaceimportosfromdatetimeimportdatetimefromhow_fake_accountsimportfake# generate 10 random accountsaccounts=fake.generate_accounts(10)print(accounts)filename=datetime.now().strftime('%Y-%m-%d-%H-%M-%S')+'-accounts.json'fake.save_into_s3(accounts,bucket='missy-aulas-how',key='bronze/desafio-01/'+filename,aws_access_key_id=os.getenv('AWS_ID'),aws_secret_access_key=os.getenv('AWS_KEY'))CLI InterfaceTo generate 50 accounts, just type into terminal:how_fake--n-accounts50orpython-mhow_fake--n-accounts50for help, just type:how_fake--help
Package: howfast-apm
Light instrumentation of your Python server for reporting performance data toHowFast APM.InstallTo install / update the module:pipinstallhowfast-apm[flask]UsageOnly the Flask middleware is currently available.fromhowfast_apmimportHowFastFlaskMiddleware# Create your Flask appapp=Flask(__name__,...)# Instanciate all your other middlewares first# Setup the APM middleware last, so that it can track the time spent inside other middlewaresHowFastFlaskMiddleware(app,app_id=HOWFAST_APM_DSN)ConfigurationYou can configure the APM through environment variables. If they are defined, those variables will
be used. Parameters passed to theHowFastFlaskMiddlewareconstructor take precedence over environment
variables.Only one variable is available for now:HOWFAST_APM_DSN: The DSN (application identifier) that you can find on your APM dashboard. Can also be passed to the constructor asapp_id.If the environment variable is defined you can then use:# Install the middlewareHowFastFlaskMiddleware(app)You can also choose to exclude some URLs from reporting:# Do not report performance data for some URLsHowFastFlaskMiddleware(app,endpoints_blacklist=['/some/internal/url/',# You can also use patterns accepted by Python's `fnmatch.fnmatch`, shell-like:'/admin/*','/jobs/*/results','/endpoint/?',# will blacklist /endpoint and /endpoint/],)
Package: howfo-frame
# HowfoFrame
这是一款基于flask的小型自定义框架，包含常用的组件：数据库、配置、日志
# 主要功能
+ 站点目录快速生成
+ 搭建快速测试模块
# 开发日志# 项目结构
├── LICENSE
├── README.md
├── MANIFEST.in
├── src
│ ├── howfo
│ │ ├── __init__.py
│ │ ├── app.py
│ │ ├── manage.py
│ │ ├── helpers.py
│ │ ├── logger.py
├── setup.cfg
├── setup.py
Package: howiml
howi-mlHoWi ML is a top-level machine learning package for prototyping and comparison between different scikit-learn, MLP, LSTM and GRU model architectures. It originates from a master thesis focusing on the use of Machine Learning regression models for the oil and gas domain.The package is published on PyPi. To install, do the following:Install Python 3.6Create a new virtual environmentpip install howimlAdditional packages like Tensorflow, Keras etc. are automatically installed.UsageCode documentation is available in the "doc" folderTwo examples using the stateless (default) and stateful module are seen in the top-level repository ("example_stateful.ipynb" and "example_stateless.ipynb", respectively).Some features of the package are:Stateless top-level module with most required functionality to define and compare machine learning regression modelsSimilar, stateful top-level module for inexperienced usersA lot of underlying functionality for more advanced users, available from howiml.utilsUsage is as follows:Make sure your dataset is available in .csv format, with column names in the first row and each data row in subsequent rowsDefine the required metadata for your dataset. It is suggested that you implement a local config file and import this in your project, e.g. configs.py with methods to extract all the same metadata as seen defined in the notebook examples
Package: how-is-my-bmi
how_is_my_bmi version 0.1.1A small library to be able to generate your BMI Index - Body Mass Index.Installationpip install bmiGet StartedHow to get the BMI Index:from bmi import BmiInstantiate the BMI Objectuser_2 = Bmi(105, 6)Call the BMI Calculator methodprint(user_2.calculate_bmi())The calc_bmi() method is deprecated. This version introduces the calculate_bmi().
Package: how-its-made
How it's madeHow the how it's made package is madeThis package recursively generates a title for the show "How It's Made" for n iterations, and can also read the title aloud.InstallationHow it's made has one requirement, pyttsx3, and can be installed using:pipinstallhow-its-madeUsageimporthow_its_madeashim# This will print "How It's Made"how=him.how_its_made()print(how.title)# This will print "How How How A Chair's Made Is Made Is Made"how=him.how_its_made(3,"a chair")print(how.title)# This will read aloud "How How How A Chair's Made Is Made Is Made"how.text_to_speech()
Package: howler-client
Le version français suitHowler Client LibraryThe Howler client library facilitates issuing requests to Howler.Running the TestsPrepare the howler-api:Start dependencieshowler-api > python howler/app.pyhowler-api > python howler/odm/random_data.pyRun Java integration tests:howler-client > mvn verifyRun python integration tests:howler-client/python > python -m venv envhowler-client/python > . env/bin/activatehowler-client/python > pip install -r requirements.txthowler-client/python > pip install -r test/requirements.txthowler-client/python > pip install -e .howler-client/python > pytest -s -v test======FrançaisBibliothèque client de HowlerLa bibliothèque client Howler facilite l'émission de requêtes à Howler.Exécution des testsPréparez l'interface howler-api :Démarrer les dépendanceshowler-api > python howler/app.pyhowler-api > python howler/odm/random_data.pyExécutez les tests d'intégration Java :howler-client > mvn verifyExécuter les tests d'intégration python :howler-client/python > python -m venv envhowler-client/python > . env/bin/activatehowler-client/python > pip install -r requirements.txthowler-client/python > pip install -r test/requirements.txthowler-client/python > pip install -e .howler-client/python > pytest -s -v test
Package: howlermonkey
Intended to allow for automated messaging to our DTG slack group at a controlled endpoint.Authentication is provided by only allowing connections over local unix domain sockets on a puppy VM. As such any user that can gain access to the host in order to connect must have sufficient monkeysphere auth and thus be a member of the DTG or at least equivalently privileged. Unauthenticated access by minor services can also be mediated by co-locating them on the same host.
Package: howmuch
HowMuch**Have you ever asked yourself, "How much space have I wasted on.ckpt and.safetensors checkpoints?"🤔
Say hello to HowMuch: Checking checkpoint wasted space since... well, now! 😄 Enjoy this somewhat unnecessary, yet "fun-for-the-whole-family" DiskSpaceAnalyzer tool. 😄OverviewHowMuchis a Python tool designed to scan your drives (or a specified directory) and report on the total space used by files with specific extensions, mainly.ckptand.safetensors.It outputs:The total storage capacity of each scanned drive or directory.The space occupied by.ckptand.safetensorsfiles.The free space available.A neat bar chart visualizing the above data.InstallationFrom PyPIYou can easily installHowMuchvia pip:pipinstallhowmuchFrom SourceClone the repository:gitclonehttps://github.com/1e-2/HowMuch.gitNavigate to the cloned directory and install:cdHowMuch
pipinstall.UsageRun the tool without any arguments to scan all drives:howmuchOr, specify a particular directory or drive to scan:howmuch--scanC:The results will be displayed in the console, saved to a text file (HowMuch_output.txt), and visualized in a bar chart (HowMuch_chart.png).ContributingFeel free to fork the repository, make changes, and open a pull request. All contributions are welcome!LicenseThis project is licensed under the MIT License. SeeLICENSEfor details.Authoridlebg-GitHubFor any additional questions or comments, pleaseopen an issue.
Package: howmuchtime
howmuchtime===========Countdown to a date.This package is a simple command line app for counting down (in days) toa date. Call ``hmt`` at any time to see the days remaining for all addeddates.For example:::$ hmt -a 2018-09-20 BirthdayDate added.$ hmtBirthday 2018-09-20: 118 days (2832 hours) remaining.Installation------------Install with:::$ pip install howmuchtime*If hmt is not installed as a script, try re-installing with* ``sudo pip install howmuchtime``^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^Usage-----Add a date to countdown to with:::$ hmt -a <YYYY-MM-DD> <optional name>If you no longer want the countdown to a date, remove it with:::$ hmt -r <YYYY-MM-DD>$ # or using a name$ hmt -r <name>Finally, to view the countdown, just call hmt:::$ hmtAdditional Help---------------::usage: hmt [-a date [name] | -r date/name | -h]Countdown to a date.optional arguments:-a date [name], --add-date date [name]Add date in YYYY-MM-DD format (with an optional name)for counting down to-r date/name, --remove-date date/nameRemove a currently tracked date by specifying the dateor its name-h, --help            Show this help message and exit
Package: how_now
how-nowExplain exceptions using AI.Github repository:https://github.com/habemus-papadum/how-now/Documentationhttps://habemus-papadum.github.io/how-now/Getting started with your projectFirst, create a repository on GitHub with the same name as this project, and then run the following commands:gitinit-bmain
gitadd.
gitcommit-m"init commit"gitremoteaddorigingit@github.com:habemus-papadum/how-now.git
gitpush-uoriginmainFinally, install the environment and the pre-commit hooks withmakeinstallYou are now ready to start development on your project! The CI/CD
pipeline will be triggered when you open a pull request, merge to main,
or when you create a new release.To finalize the set-up for publishing to PyPi or Artifactory, seehere.
For activating the automatic documentation with MkDocs, seehere.
To enable the code coverage reports, seehere.Releasing a new versionCreate an API Token onPypi.Add the API Token to your projects secrets with the namePYPI_TOKENby visitingthis page.Create anew releaseon Github.
Create a new tag in the form*.*.*.For more details, seehere.Repository initiated withfpgmaas/cookiecutter-poetry.