Saravanan666/pypi-org huggingface dataset:
Package: haikus
haikusは、与えられた俳句から季語を識別し、その俳句の形式と構造を分析するPythonモジュールです。機能季語の識別: 俳句に含まれる季語を識別します。
俳句の形式分析: 与えられた文字列がどのような種類の詩であるか判別します使用方法まず、モジュールをインポートします：fromhaikusimporthaikus次に、分析したい俳句をリスト形式で関数に渡します：user_haiku=["古池","蛙飛び込む","水の音"]#入力配列は３項目or５項目がサポートされますresult=haikus(user_haiku)print(result){'kigo':['蛙'],'yomi':['ふるいけや','かえるとびこむ','みずのおと'],'type':'haiku','len':[0,0,0]}このような形で季語（配列）俳句の読み（配列）俳句の種類（俳句or川柳or短歌or自由律俳句）既定文字数の過不足　字足らず、字余り（配列）
が出力されます。依存関係PyKakasi: 日本語テキストのカナ変換に使用されます。ライセンスこのプロジェクトは、季語検索によって使用された季語リストを含みます。この季語リストは、Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ライセンスの下で提供されています。
クレジットhttps://jpsearch.go.jp/database/irc04季語リストの原作者：国際日本文化研究センターライセンスこの季語リストは、以下の条件に従って使用されています：表示：あなたは適切なクレジットを表示し、ライセンスへのリンクを提供し、変更があった場合はその旨を示さなければなりません。これらの条件は合理的な方法で行われるべきですが、ライセンス提供者によって提案された方法であることを意味するものではありません。
継承：あなたがこの素材をリミックス、変形、またはビルドアップする場合、あなたの貢献はオリジナルのライセンスと同じCC BY-SA 4.0 ライセンスの下で配布されなければなりません。このライセンスの全文と情報は、次のリンクで確認できます：https://creativecommons.org/licenses/by-sa/4.0/
Package: hail
HailHailis an open-source, general-purpose, Python-based data analysis tool with additional data types and methods for working with genomic data.Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS).Hail is exposed as a Python library, using primitives for distributed queries and linear algebra implemented in Scala,Spark, and increasingly C++.See thedocumentationfor more info on using Hail.CommunityHail has been widely adopted in academia and industry, including as the analysis platform for thegenome aggregation databaseandUK Biobank rapid GWAS. Learn more aboutHail-powered science.ContributeIf you'd like to discuss or contribute to the development of methods or infrastructure, please:see theFor Software Developerssection of the installation guide for info on compiling Hailchat with us about development in ourZulip chatroomvisit theDevelopment Forumfor longer-form discussionsHail uses a continuous deployment approach to software development, which means we frequently add new features. We update users about changes to Hail via theDiscussion Forum. We recommend creating an account on the Discussion Forum so that you can subscribe to these updates as well.MaintainerHail is maintained by a team in theNeale labat theStanley Center for Psychiatric Researchof theBroad Institute of MIT and Harvardand theAnalytic and Translational Genetics UnitofMassachusetts General Hospital.Contact the Hail team athail@broadinstitute.org.Citing HailIf you use Hail for published work, please cite the software. You can get a
citation for the version of Hail you installed by executing:importhailashlprint(hl.citation())Which will look like:Hail Team. Hail 0.2.13-81ab564db2b4. https://github.com/hail-is/hail/releases/tag/0.2.13.AcknowledgementsThe Hail team has several sources of funding at the Broad Institute:The Stanley Center for Psychiatric Research, which together with Neale Lab has provided an incredibly supportive and stimulating home.Principal Investigators Benjamin Neale and Daniel MacArthur, whose scientific leadership has been essential for solving the right problems.Jeremy Wertheimer, whose strategic advice and generous philanthropy have been essential for growing the impact of Hail.We are grateful for generous support from:The National Institute of Diabetes and Digestive and Kidney DiseasesThe National Institute of Mental HealthThe National Human Genome Research InstituteThe Chan Zuckerberg InitiativeWe would like to thankZulipfor supporting
open-source by providing free hosting, and YourKit, LLC for generously providing
free licenses forYourKit Java
Profilerfor open-source development.
Package: hailhydro
hailhydroA module for computation of Hagen-Poiseuille flows and Advection-Diffusion+Absorption landscapes on Kirchhoff networks. For further documentation see:https://felixk1990.github.io/kirchhoff-hydro/IntroductionThe module 'hailhydro' is part of a series of pyton packages encompassing a set of class and method implementations for a kirchhoff network datatype, in order to to calculate flow/flux on lumped parameter model circuits. The flow/flux objects are embedded in the kirchhoff networks, and can be altered independently from the underlying graph structure. This is meant for fast(er) and efficient computation in the follow-up module 'goflow' and dependend of 'kirchhoff'.Installationpip install hailhydroUsageGenerally, just take a weighted networkx graph and create a kirchhoff circuit from it (giving it a defined spatialy embedding and conductivity structure)import kirchhoff.circuit_flow as kfc
import hailhydro.flow_init as hf
circuit=kfc.initialize_flow_circuit_from_crystal('simple',3)
flow=hf.initialize_flow_on_circuit(circuit)To set node and edge attributes ('source','potential' ,'conductivity','flow_rate') use the set_source_landscape(), set_plexus_landscape() methods of the kirchhoff class. Further offering non-trivial random flow patterns for complex net adapation models(see 'goflow')import kirchhoff.circuit_flow as kfc
import hailhydro.flow_random as hfr

circuit1=kfc.initialize_flow_circuit_from_crystal('simple',3)
circuit1.set_source_landscape('root_multi',num_sources=1)
circuit1.set_plexus_landscape()
random_flow=hfr.initialize_random_flow_on_circuit(circuit1)

circuit2=kfc.initialize_flow_circuit_from_crystal('simple',3)
circuit2.set_source_landscape('root_multi',num_sources=1)
circuit2.set_plexus_landscape()
rerouting_flow=hfr.initialize_rerouting_flow_on_circuit(circuit2)Furter, extra classes for flux of solute along a kirchhoff network are implemented (use hailhydro.flux_overflow to handle critical regimes of large Peclet numbers)import hailhydro.flux_init as hfx
import kirchhoff.circuit_flux as kfx
n=2
pars={
    'plexus':nx.grid_graph(( n,n,1)),
    'absorption':0.1,
    'diffusion':1.
}
circuit=kfx.setup_default_flux_circuit(pars)
flux=hfx.initialize_flux_on_circuit(circuit)
overflow=hro.initialize_overflow_on_circuit(circuit)Further examples and recipes can be found here:Requirementsnetworkx==2.5
numpy==1.19.1
scipy==1.5.2
kirchhoff==0.2.7Acknowledgementhailhydrowritten by Felix Kramer
Package: haip
Haip"haip.*" are namespace based python packages for various purposes. We use namespaced packages to avoid conflicts with existing packages and projects.Normally you should not get in touch with the base-package ("haip") at all but use the subpackages directly. A design goal is to keep these subpackages as independent as possible to be able to use them seperatly (as you normaly would expect from a global python package).Subpackages (ongoing)haip.config - a simple configuration manager for yaml based config files
Package: haip-config
haip.confighaip.config is a simple yaml configuration handling module for Python 3.6+.Featuresdirectory based: place your config in multiple files in your configuration directory. haip.config will merge them automatically.environment overlay: place your environment specific configuration in a subdirectory. haip.config will overwrite the base configuration.attribute-style: a.b.c = a['b']['c']Getting StartedInstallingpipinstallhaip-configor from source:gitclonehttps://github.com/haipdev/config.gitExampleconfig-files/path-to-my-config-dir/databases.ymldatabases:test:username:testuserhost:127.0.0.2/path-to-my-config-dir/dev/databases.ymldatabases:test:password:testpasswordpython implementationimporthaip.configasconfigconfig.load('/path-to-my-config-dir','dev')cfg=config.get('databases','test',username=config.MANDATORY,password=config.MANDATORY,host='127.0.0.1',port=3306)username=cfg.databases.test.username# <-- 'testuser' from base-configpassword=cfg.databases.test.password# <-- 'testpassword' from dev subdirhost=cfg.databases.test.host# <-- '127.0.0.2' from base-config (default not used)port=cfg.databases.test.port# <-- 3306 from defaultRunning the testsTests are written using pytest and located in the "tests" directory.pytesttestsContributingFeel free to use and enhance this project. Pull requests are welcome.AuthorsReinhard Hainz-Initial work-haipdevLicenseThis project is licensed under the MIT License - see theLICENSEfile for detailsDependenciespoyo
Package: haip-confluence
haip.confighaip.confluence is a simple module to communicate with confluence REST API.FeaturesgetBody: get confluence page by idsetBody: set confluence page content by idgetChildren: get children of a page --> (version, title, body)Getting StartedInstallingpipinstallhaip-confluenceor from source:gitclonehttps://github.com/haipdev/confluence.gitExampleconfig-files/path-to-my-config-dir/confluence.ymlconfluence:url:https://myconfluence.domain.com/rest/api/latestusername:userpassword:passtimeout:10python implementationimporthaip.configasconfigimporthaip.confluenceasconfluenceconfig.load('/path-to-my-config-dir','dev')content=awaitconfluence.getBody(12345)page=awaitconfluence.setBody(12345,'hello world')children=awaitconfluence.getChildren(12344)Running the testsTests are written using pytest and located in the "tests" directory.pytesttestsContributingFeel free to use and enhance this project. Pull requests are welcome.AuthorsReinhard Hainz-Initial work-haipdevLicenseThis project is licensed under the MIT License - see theLICENSEfile for detailsDependencieshaip-config
Package: haipproxy2
在原先SpiderClub/haipproxy cli的基础上，稍微修改了下可以通过scrapy中的settings配置来覆盖默认配置，编写了scrapy download middleware ,自动配置proxy,前提是你通过haipproxy配置好了redis proxy pooltododelete proxy 逻辑,删除连接失败的代理
Package: haip-template
haip.templatehaip.template is Jinja2 based async template rendering module.Featuresminimalistic: a single exported async function "render"autoreturn-type: the return data type is derived from the file suffixGetting StartedInstallingpipinstallhaip.templateor from source:gitclonehttps://github.com/haipdev/template.gitUsageasync def render(template_filename, *, template_dir=None, **args)The template-file is defined byconfig.template_dir/template_dir/template_filename. **args are used to fillin the placeholders in the template_file and the suffix of the template_file determines the return type. E.g. "template_name.json" will return a json structure (dict).renderwill propagate exceptions as they are raised by the underlaying modules.Suffix handling is as follows:.json : json struct (dict).sql : string (but **args values will be escaped).* : stringExampleconfig (using haip.config)template-dir:/path-to-template-dir/mytemplate.jinja2Hello my name is {{ firstname }} {{ lastname }}!python implementationimporthaip.configasconfigimporthaip.templateastemplateconfig.load('/path-to-my-config-dir')document=awaittemplate.render('mytemplate.jinja2',firstname='Reinhard',lastname='Hainz')If mytemplate.jinja is in a subdirectory of the template-dir (e.g. /path-to-template-dir/mysubdir/):document=awaittemplate.render('mytemplate.jinja2',template_dir='mysubdir',firstname='Reinhard',lastname='Hainz')You can also use absolute paths in the template_dir argument of the render function.Running the testsTests are written using pytest and located in the "tests" directory.pytesttestsContributingFeel free to use and enhance this project. Pull requests are welcome.AuthorsReinhard Hainz-Initial work-haipdevLicenseThis project is licensed under the MIT License - see theLICENSEfile for detailsDependencieshaip_configjinja2
Package: haipy
HaipyWhat is it?A CLI tool to identify hash types (hash type identifier).This project is a Python porting of "haiti"https://github.com/noraj/haitiwritten in Ruby.Features500+ hash types detectedCan be used as Python libraryModern algorithms supported (SHA3, Keccak, Blake2, etc.)Hashcat and John the Ripper referencesCLI toolHackableUsagehaipy [hash code to guess]Usage as Python LibraryHaipy can be used as a Python Library like shown in the following snippet of code:>>> import haipy as haipy

>>> haipy.detect("$6$qoE2letU$wWPRl.PVczjzeMVgjiA8LLy2nOyZbf7Amj3qLIL978o18gbMySdKZ7uepq9tmMQXxyTIrS12Pln.2Q/6Xscao0")

>>> [{'name': 'SHA-512 Crypt', 'hashcat': 1800, 'john': 'sha512crypt'}]InstallationTo install from pypi:pip install haipyTo install from the source directory:pip install .AuthorPorted by @gcarmix, derived from haiti by @noraj
Package: haiqv-streaming-dag-editor
Code Editor Plugin for Haiqv-streamingA plugin forApache Airflowthat allows you to edit DAGs in browser.
It provides a file managing interface within specified directories and it can be used to edit and download your files.
The DAGs are stored in a Git repository. You may use it to view Git history, review local changes and commit.System ConditionAirflow Versionsdev 2.0.0Deployment InstructionsInstall the plugin in containerAdd a below code on dockerflie for haiqv-streaming
RUN pip install airflow-code-editorStart Haiqv-streamingOpen Admin - DAGs Code EditorConfig OptionsYou can edit yourairflow.cfgadding any of the following settings in the [code_editor] section.git_cmdgit command (optional path)git_default_argsgit arguments added to each call (default: -c color.ui=true)git_author_namehuman-readable name in the author/committer (default logged user first and last names)git_author_emailemail for the author/committer (default: logged user email)git_init_repoinitialize a git repo in DAGs folder (default: True)root_directoryroot folder (default: Airflow DAGs folder)mount_nameconfigure additional file folder name (mount point)mount_pathconfigure additional file pathExample:[code_editor]
   git_cmd = /usr/bin/git
   git_default_args = -c color.ui=true
   git_init_repo = False
   root_directory = /home/airflow/dags
   mount_name = data
   mount_path = /home/airflow/data
   mount1_name = logs
   mount1_path = /home/airflow/logsLinksApache Airflow -https://github.com/apache/airflowCodemirror, In-browser code editor -https://github.com/codemirror/codemirrorGit WebUI, A standalone local web based user interface for git repositories -https://github.com/alberthier/git-webui
Package: hairball
# HairballHairball is a plugin-able framework useful for static analysis of Scratchprojects.The paper and presentation slides for Hairball can be found at:http://cs.ucsb.edu/~bboe/p/cv#sigcse13A Hairball demo web service is running and available at:http://hairball.herokuapp.com## Hairball installationWith a proper python environment (one which has `pip` available), installationis as simple as `pip install hairball`. `easy_install` can also be used via`easy_install hairball`.To install from source, first checkout this project and then navigate yourcommand-line interface to the outer hairball directory that contains`setup.py`. Then run `python setup.py install`.## Running HairballOnce installed, to see how to use hairball run `hairball --help`. That willproduce output similar to the following:```Usage: hairball -p PLUGIN_NAME [options] PATH...PATH can be either the path to a scratch file, or a directory containingscratch files. Multiple PATH arguments can be provided.Options:--version             show program's version number and exit-h, --help            show this help message and exit-d DIR, --plugin-dir=DIRSpecify the path to a directory containing plugins.Plugins in this directory take precedence oversimilarly named plugins included with Hairball.-p PLUGIN, --plugin=PLUGINUse the named plugin to perform analysis. This optioncan be provided multiple times.-k KURT_PLUGIN, --kurt-plugin=KURT_PLUGINProvide either a python import path (e.g, kelp.octopi)to a package/module, or the path to a python file,which will be loaded as a Kurt plugin. This option canbe provided multiple times.```## Available PluginsBelow are a list of available plugins that can be used as the `-p PLUGIN_NAME`option:* blocks.BlockCounts* blocks.DeadCode* checks.Animation (not fully tested)* checks.BroadcastReceive* checks.SaySoundSync (not fully tested)* duplicate.DuplicateScripts* initialization.AttributeInitialization* initialization.VariableInitialization (not fully tested)Note: The output for each plugin is not yet completely standardized. Pleasefeel free to file any issues or make improvements and send pull requests.## Caching SupportThe python Kurt package unfortunately is pretty slow to parse Scratch 1.4 (andsimilar) files. To remedy this situation, Hairball has built-in support forcaching a serialized version of the Kurt object. On subsequent passes throughthe same data you should notice a TREMENDOUS speed improvement.__Note__: At the moment the cache is unbounded, so keep an eye on your diskspace. The cache location can be discovered by running:python -c "import appdirs; print appdirs.user_cache_dir('Hairball', 'bboe')"
Package: hair-color-detector
Hair Color DetectorpypiSegmentator based fromnganngants/hair-dye-web-appOverviewThis project allows you to get hair color of a person from an image.Basic Usagefromhair_color_detectorimportHairColorDetectorhcd=HairColorDetector()hcd.get_color('test.png',save_result=True,n_clusters=10)
Package: hairgap
HairgapBasic protocol to send files using thehairgap binary.
The goal is to send random files through a unidirectionnal data-diode using UDP connections.By default, hairgap can only send a file, without its name nor metadata. This Python library implements a basic protocol to send complete directories
with checksums.This protocol is customizable and the sender can add some attributes (aDict[str, str]) to each transfer.We assume that the hairgap binary is installed and in the PATH environment variable.The MAC adress of the destination must be known from the sender machine. You can inject this information into the ARP cache of the sender machine:DESTINATION_IP="the IP address of the destination machine"DESTINATION_MAC="the MAC address of the destination machine"arp-s${DESTINATION_IP}${DESTINATION_MAC}First, you must start the receiver on the destination side:python3-mpipinstallhairgapDESTINATION_IP="the IP address of the destination machine"pyhairgapreceive${DESTINATION_IP}directory/Then you can send directories:python3-mpipinstallhairgapDESTINATION_IP="the IP address of the destination machine"pyhairgapsend${DESTINATION_IP}directory/Hairgap binariesYou need to compile hairgap binaires yourself since no official package exists.
However, a Python package (hairgap-binaries) has been created to distribute precompiled binaries for Linux x86_64.
This package is automatically detected and used.python3-mpipinstallhairgap-binaries
Package: hairgap-binaries
hairgap-binariesProvide compiled binaries ofhairgapunder the form of a Python package.usepython3-mpipinstallhairgap-binarieshairgap-binariesonly providesget_hairgaprandget_hairgapsfunctions that return absolute paths ofhairgaprandhairgaps.
If there are not available for your architecture (e.g., anything else than Linux x86_64),Noneis returned.importosfromhairgap_binariesimportget_hairgapr,get_hairgapsassertos.path.isfile(get_hairgapr())assertos.path.isfile(get_hairgaps())buildingRequirements:python3 (>= 3.5),wheel Python package (in the current virtualenv) for creating the .whl package,vagrant with the scp plugin.tox-epy
Package: hairyBH
Hairy Black HoleEste es un paquete que usa numpy, math y scipy para calcular algunos parametros importantes de un agujero negro con pelo estático. Los parámetros que calculo por ejemplo son: la masa, la posicion del horizonte, tanto en x como en r coordenadas.Algunas cosas a tomar en cuentaNo utiliza el sistema internacional de unidades.La distancia se mide en unidades astronómicas, la masa en masas solares, y el tiempo en años.Tambien calcula las geodésicas siguiendo un metodo de RK4, las geodésicas tipo tiempo y tipo null.Para instanciar un agujero negro importamos black_hole y le damos los 3 parámetros necesarios que son nu, eta y alpha.Para instanciar una geodésica importamos particula_time_like y le damos los 5 parametros necesarios que son nu, eta, alpha, la energia, el momentum angular (todo en las unidades antes mencionadas). En el caso null importamos particula_null y le damos los 4 parametros necesarios que son nu, eta, alpha, b (parámetro de impacto).Esta es una primera versión que se ira mejorando, seguramente presentara varios errores con ciertos parámetros.
Package: haisdk
Holistic AIThe Holistic AI SDK is a library that provides integration with the HAI PlatformHolistic Ai website:https://holisticai.comInstallationInstall the library with:pipinstallhaisdkHow to use the haisdkCreate a configfromhaisdk.configimportConfigconfig={'tenant_id':'','api_key':'','project_id':'','solution_id':'','instance_id':''}session=Config(config=config)Create an assessmentfromhaisdk.assessmentimportAssesssettings={'task':'','tbd':'tbd'}assess=Assess(session=session,settings=settings)assess.run(input=...,output=...,pred=...,model=...)
Package: hak
hakThis project is a collection of function test pairs for common tasks.
Mostly for my own use but others are welcome to leverage it.Required configurationgit config --global user.email "you@example.com"git config --global user.name "Your Namepip install xguRequired packagessudo apt install twineTest Cyclenodemon --exec python3 test.pyTODOReport empty double lines at end of test cycleReport unsorted importsReport empty line should follow last importReport foreign imports should preceed local importsReport unused importsFix test for pxyz
Package: hakai-api
Hakai Api Python ClientThis project exports a single Python class that can be used to make HTTP requests to the
Hakai API resource server.
The exportedClientclass extends the functionality of the
Pythonrequests libraryto supply Hakai
OAuth2 credentials with url requests.Table of ContentsInstallationQuickstartMethodsAPI endpointsAdvanced usageContributingInstallationPython 3.8 or higher is required. Install with pip:pipinstallhakai-apiQuickstartfromhakai_apiimportClient# Get the api request clientclient=Client()# Follow stdout prompts to get an API token# Make a data request for chlorophyll dataurl='%s/%s'%(client.api_root,'eims/views/output/chlorophyll?limit=50')response=client.get(url)print(url)# https://hecate.hakai.org/api/eims/views/output/chlorophyll...print(response.json())# [{'action': '', 'event_pk': 7064, 'rn': '1', 'date': '2012-05-17', 'work_area': 'CALVERT'...MethodsThis library exports a single client nameClient. Instantiating this class produces
arequests.Sessionclient from the Python requests library. The Hakai API Python
Client inherits directly fromrequests.Sessionthus all methods available on that
parent class are available. For details see
therequests documentation.The hakai_apiClientclass also contains a propertyapi_rootwhich is useful for
constructing urls to access data from the API. The
aboveQuickstart exampledemonstrates using this property to construct a
url to access project names.API endpointsFor details about the API, including available endpoints where data can be requested
from, see theHakai API documentation.Advanced usageYou can specify which API to access when instantiating the Client. By default, the API
useshttps://hecate.hakai.org/apias the API root. It may be useful to use this
library to access a locally running API instance or to access the Goose API for testing
purposes. If you are always going to be accessing data from a locally running API
instance, you are better off using the requests.py library directly since Authorization
is not required for local requests.fromhakai_apiimportClient# Get a client for a locally running API instanceclient=Client("http://localhost:8666")print(client.api_root)# http://localhost:8666You can also pass in the credentials string retrieved from the hakai API login page
while initiating the Client class.fromhakai_apiimportClient# Pass a credentials token as the Client Class is initiatedclient=Client(credentials="CREDENTIAL_TOKEN")Finally, you can set credentials for the client class using theHAKAI_API_CREDENTIALSenvironment variable. This is useful for e.g. setting credentials in a docker container.
The value of the environment variable should be the credentials token retrieved from the
Hakai API login page.ContributingSeeCONTRIBUTINGLicenseSeeLICENSE