Saravanan666/pypi-org huggingface dataset:
Package: hapdoc
H a p d o cautodoc CLI tool written in Python with :heart:Why Hapdoc? 💁‍♀️Easy to use: Hapdoc is designed to be simple and user-friendly, making it easy for programmers to create documentation without having to spend a lot of time learning how to use it.Markdown support: Hapdoc supports Markdown syntax, which is a popular markup language used by many developers. This means that programmers can write documentation using the same tools they use to write code, making the process more familiar and efficient.Customizable themes: Hapdoc supports different themes, allowing programmers to choose a look and feel that matches their project or organization's branding. Additionally, programmers can create their own themes if they want to customize the look of their documentation further.Features :sparkles:Supported projects:PythonFastAPIJavaScriptGenerate Markdown docs viagencommand.Build docs into HTML viabuildcommand.Build Markdown docs into JSON viamd2jsoncommand.Serve generated docs at your server viaservecommand.Create your own templates viatmpl-new.See your saved templates viatmpl-list.Prepare your docs withproject.hapdocconfig file.Installing 📥viapypi📦pipinstallhapdoc--upgradeviagit💾pipinstallhttps://github.com/hapticx/hapdocUsage ⚡Help messagehapdoc--helpGenerate and serve docs via FastAPI:hapdocgenpath/to/project
hapdocservedocs/hapdoc-h127.0.0.1-p5000Build HTML docs:hapdocbuildpath/to/projectWhat's Next? 💡We planned to add some other project types! Check more inissues:doc_rulesContributing :dizzy:You can help us and create PR with project type ✌
Package: hapflk
hapflk is a software implementing the hapFLK [1] and FLK [2] tests for
the detection of selection signatures based on multiple population
genotyping data.[1] Fariello et al., 2013, Detecting Signatures of Selection Through
Haplotype Differentiation Among Hierarchically Structured
Populations. Genetics
193(3):929-941.http://www.genetics.org/content/193/3/929.abstract[2] Bonhomme et al., 2010, Detecting selection in population trees:
The Lewontin and Krakauer test extended. Genetics 186(1) 241-262http://www.genetics.org/content/186/1/241.abstract
Package: ha-philipsjs
ha-philipsjsWrapper library to integrate Philips TVs with jointSPACE API into Home-AssistantThis is just being used to wrap the API into a nice object. Nothing fancy.Support version 1 and 6 of the jointspace APISupport pairing processPython typing information on endpointsNotifyChange support for supported tv'sPrint system datafromhaphilipsjsimportPhilipsTVtv=PhilipsTV("tv2.example.com",6)tv.getSystem()print(tv.system)Pairing with TVfromhaphilipsjsimportPhilipsTVtv=PhilipsTV("tv2.example.com",6)tv.getSystem()state=tv.pairRequest("my_test_app","My Test App","mox","OSX","native")pin=input("Enter pin:")username,password=tv.pairGrant(state,pin)print(username)print(password)Command line utility:usage: python -m haphilipsjs [-h] [-d] -i HOST -a API [-u USERNAME] [-p PASSWORD] {status,monitor,ambilight,pair} ...

positional arguments:
  {status,monitor,ambilight,pair}
                        commands
    status              Show current tv status
    monitor             Monitor current tv status
    ambilight           Control ambilight
    pair                Pair with tv

optional arguments:
  -h, --help            show this help message and exit
  -d, --debug           Debug output
  -i HOST, --host HOST
  -a API, --api API
  -u USERNAME, --username USERNAME
                        Username
  -p PASSWORD, --password PASSWORD
                        Password
Package: hapic_apispec
Friendly fork of apispec waiting for merginginstance supportA pluggable API specification generator. Currently supports theOpenAPI Specification(f.k.a. the Swagger specification).FeaturesSupports OpenAPI Specification version 2 (f.k.a. the Swagger specification) with limited support for version 3.Framework-agnosticIncludes plugins formarshmallow,Flask,Tornado, andbottle.Utilities for parsing docstringsExample ApplicationfromapispecimportAPISpecfromflaskimportFlask,jsonifyfrommarshmallowimportSchema,fields# Create an APISpecspec=APISpec(title='Swagger Petstore',version='1.0.0',plugins=['apispec.ext.flask','apispec.ext.marshmallow',],)# Optional marshmallow supportclassCategorySchema(Schema):id=fields.Int()name=fields.Str(required=True)classPetSchema(Schema):category=fields.Nested(CategorySchema,many=True)name=fields.Str()# Optional Flask supportapp=Flask(__name__)@app.route('/random')defrandom_pet():"""A cute furry animal endpoint.
    ---
    get:
        description: Get a random pet
        responses:
            200:
                description: A pet to be returned
                schema: PetSchema
    """pet=get_random_pet()returnjsonify(PetSchema().dump(pet).data)# Register entities and pathsspec.definition('Category',schema=CategorySchema)spec.definition('Pet',schema=PetSchema)withapp.test_request_context():spec.add_path(view=random_pet)Generated OpenAPI Specspec.to_dict()# {#   "info": {#     "title": "Swagger Petstore",#     "version": "1.0.0"#   },#   "swagger": "2.0",#   "paths": {#     "/random": {#       "get": {#         "description": "A cute furry animal endpoint.",#         "responses": {#           "200": {#             "schema": {#               "$ref": "#/definitions/Pet"#             },#             "description": "A pet to be returned"#           }#         },#       }#     }#   },#   "definitions": {#     "Pet": {#       "properties": {#         "category": {#           "type": "array",#           "items": {#             "$ref": "#/definitions/Category"#           }#         },#         "name": {#           "type": "string"#         }#       }#     },#     "Category": {#       "required": [#         "name"#       ],#       "properties": {#         "name": {#           "type": "string"#         },#         "id": {#           "type": "integer",#           "format": "int32"#         }#       }#     }#   },# }spec.to_yaml()# definitions:#   Pet:#     enum: [name, photoUrls]#     properties:#       id: {format: int64, type: integer}#       name: {example: doggie, type: string}# info: {description: 'This is a sample Petstore server.  You can find out more ', title: Swagger Petstore, version: 1.0.0}# parameters: {}# paths: {}# security:# - apiKey: []# swagger: '2.0'# tags: []DocumentationDocumentation is available athttp://apispec.readthedocs.io/.EcosystemA list of apispec-related libraries can be found at the GitHub wiki here:https://github.com/marshmallow-code/apispec/wiki/EcosystemLicenseMIT licensed. See the bundledLICENSEfile for more details.
Package: hapi-nn
Application of HAPI for Neural Networks in PythonHAPI-NN allows interfacing of HAPI with TensorFlow and PyTorch to rapidly create deep neural network models for predicting and forecasting.See Test Examples for toy examples that test different functionalties within HAPI-NN.Real Data Examples are still work-in-progress.FeaturesA quick conversion from PySpedas plot data to HAPI-Data (as of now, metadata is not included)TrainerCan take data from different HAPI sources and so long as the time column is exactly the same (including time gaps) can be combined.Columns within HAPI data can be selected for input or output or both for the model.Columns with 1D-vectors are supported and elements within the vectors can be subselected as input or output for the model.Preprocessing helper functions that help handle time gaps by either ignoring them or treating each split of data separately.Data proportions for splitting train, validation, and tests set is easily specifiable. (Test splits are still work-in-progress)Can be used to train PyTorch or TensorFlow models with a train method.TesterHas similar capabilities to the Trainer, but does not handle training or time gaps.Predicts all possible predictions from data given some stride.Plotting of forecasts and predictions.TODOImprove/Fix Train/Val/Test SplitSplits should be made from at least several different random locations in the time seriesCreate Backend ML class that replaces current method for TensorFlow and PyTorchUpdate DocumentationComplete setup.py requirements and release on PyPI©️ 2022 The Johns Hopkins University Applied Physics Laboratory LLC.
Package: hapiplot
HAPI plotting client for Python 3.6+Installationpipinstallhapiplot--upgradeUseSee the examples athttps://github.com/hapi-server/client-pythonand the unit tests athttps://github.com/hapi-server/plot-python/blob/main/test/test_hapiplot.pyhttps://github.com/hapi-server/plot-python/blob/main/test/test_hapiplot_visual.pyContactSubmit bug reports and feature requests on therepository issue
tracker.
Package: hapiplotserver
HAPI Plot Server for Python 3.6+Serve plots from a HAPI server using thehapiplotfunction inhapiplot package.Live DemoInstallation and Startuppipinstallhapiplotserver--upgrade
hapiplotserver--port5999--workers4then seehttp://localhost:5999/for API documentation.Script UsageSeetest_hapiplotserver.py.Developmentgitclonehttps://github.com/hapi-server/plotserver-pythoncdplotserver-python;pipinstall-e.To run tests before a commit, executemaketest-repositoryContactSubmit bug reports and feature requests on therepository issue
tracker.
Package: hapipy
A python wrapper around HubSpot’s APIs.  Docs for this wrapper:
<a href=”https://github.com/HubSpot/hapipy/wiki/hapipy-documentation”>https://github.com/HubSpot/hapipy/wiki/hapipy-documentation</a>.General API reference documentation: <a href=”http://docs.hubapi.com”>http://docs.hubapi.com</a>.
Package: hapis
Overviewhapis is a collection of packages that help with creating plugins for Pyramid
web framework.Each subpackage aims to make work on a certain aspect of an application more
convenient, some also serve as mediators between plugins and Pyramid.The hapis package itself does not provide lots of useful functionality but
includes helper functions that could be used by subpackages. At the time of this
writing, these helper functions only include those for parsing complex settings
values.Changelog0.1Version that definitely works as intended.
Package: hapless
haplesshapless(adjective) - (especially of a person) unfortunate. A developer who accidentally launched long-running process in the foreground.Simplest way of running and tracking processes in the background.Installation$pipinstallhapless# or to make sure proper pip is used for the given python executable$python-mpipinstallhaplessInstall into user-specific directory in case of any permissions-related issues.$pipinstall--userhapless
$python-mpipinstall--userhaplessUsage# Run arbitrary script$haprun--pythonlong_running.py# Show summary table$hap# Display status of the specific process$hapstatus1SeeUSAGE.mdfor the complete list of commands and available parameters.ContributeSeeDEVELOP.mdto setup your local development environment and feel free to create a pull request with a new feature.ReleasesSeeCHANGELOG.mdfor the new features included within each release.See alsoRichconsole UI library.Supervisorfull-fledged process manager.podmenaprovides nice emoji icons to commit messages.Support 🇺🇦 Ukraine in the war!🇺🇦 Donate tothis foundationin case you want to help. Every donation matter!
Package: haploblock-shuffler
Haploblock-shufflerCreate all possible combinations of phased and unphased blocks in a vcfBackgroundThis tool takes a phase, unphased or partially phased VCF file, and generates
all possible combinations of phase blocks that are consistent with the phasing
that is present in the VCF file.DetailsFirst, this tool reads all variants from a VCF file, and groups variants
together if they are compatible.If a variant is phased (using thePStag), it is only compatible with
other phased variants that have the same phase ID.Homozygous variants are always compatible with other variants, since they
are part of every phase groupHeterozygous variants are only compatible when they are phased, and the
phase ID matches.To produce all possible combinations of grouped variants, haplotype-suffler
uses a counter to produce a binary pattern that determines which calls should
be modified. To modify a variant, we simply invert the order of theGTfield,
so that0/1becomes1/0, or vice versa.Since there are two alleles for every variant, we only have to produce half of
the possible VCF file, since the other half are mirror images (e.g.0101and1010).Usagehaploblock-shufflertest.vcfoutputTo generate consensus fasta files from the output vcf files, bgzip and index
the output vcf filescdoutputforiinout_*.vcf;dobgzip$itabix${i}.gzdoneThen, generate the consensus usingsamtoolsfaidx$REFERENCE$REGION|bcftoolsconsensus-H1out_0.vcf.gz>out_0_1.fa
samtoolsfaidx$REFERENCE$REGION|bcftoolsconsensus-H2out_0.vcf.gz>out_0_2.faLimitationsThis tool will generate2^(n-1)VCF files in the specifiedoutputfolder,
wherenis the number of phase blocks in the input VCF (see above). By
default, this is limited to 11 blocks, which means that at most 1024 files will
be created. This limit can be increased by using--max-blocks, but use with
caution.
Package: haplotype-plot
Haplotype plotGenerates haplotype plots for VCF files.Requires Python 3 to run.PypiYou may install haplotype plot from pip:pipinstallhaplotype-plotUsageusage:main.py[-h]-vVCF[-oOUTPUT]-cCHR-pPARENTAL[--phase][--version][-z{HOM,HET}][--confKEY=VALUE[KEY=VALUE...]]CreatesahaplotypeplotfromaVCFfile

optionalarguments:-h,--helpshowthishelpmessageandexit-vVCF,--vcfVCFpathtotheinputsortedVCFfile-oOUTPUT,--outputOUTPUTpathtotheoutputPNGhaplotypeplotfile-cCHR,--chrCHRchromosometoplotfromtheVCF-pPARENTAL,--parentalPARENTALsamplenamefromtheVCFusedasparentalhaplotype--phaseifspecified,itwillphasethegenotypes.AnoutputVCFwiththephasedgenotypesiscreated--versionshowprogram's version number and exit-z {HOM,HET}, --zygosis {HOM,HET}zygosis of the input VCF file--conf KEY=VALUE [KEY=VALUE ...]set a number of key-value pairs to modify the defaultplot configuration (do not put spaces before or afterthe = sign). If a value contains spaces, you shoulddefine it with double quotes: foo="this is asentence". Possible values are: 'start=integer','end=integer', 'show=boolean', 'xtickslabels=boolean','size_y=float'For examples of use please go to thedocumentation.
Package: hapm
Home Assistant Package ManagerPackage manager for Home Assistant which allows you to prepare components for a Stateless Docker image.InstallationpipinstallhapmConfigurationA manifest (hapm.yaml) is a list of links, divided into several categories. The links are written in a special format and necessarily have a version with@in it.For example:github.com/user/integration@v1.0.0.The version specifies an existing tag or branch in the specified repository.A link may not have an https prefix, in which case it will be inserted automatically during the reading phase.integrations:-github.com/mishamyrt/dohome_rgb@v0.3.0-github.com/mishamyrt/myrt_desk_hass@masterInitialize empty confighapminitSync remote packageshapmsyncExporthapmexport<path>ListhapmlistAdd new packagehapminstall-tintegrationsmishamyrt/assisted_pol@v0.2.4Update packagehapminstallmishamyrt/assisted_pol@v0.2.4Updates# Prints updateshapmupdates
Package: hapool
pyhapoolA handy, customizable python library that provides high availability(including failover etc.) for multiple node connections.InstallpipinstallhapoolQuickStartFor example, we need to connect to multiple kubernetes apiserver.frompyhapoolimportHAEndpointPoolfromkubernetes.clientimportApiClient# got configuration list which contain multiple apiserversconfiguration_list=[...]ha_pool=HAEndpointPool.from_list(configuration_list)classHAApiClient(ApiClient):defcall_api(self,*args,**kwargs):whileha_pool.data:withha_pool.get_endpoint()asconfiguration:self.configuration=configurationreturnsuper().call_api(*args,**kwargs)If any apiserver became unavailable (may raise some exceptions),ha_poolwould switch to next endpoint until any available one been chosen(work like upstream innginx).more documentation is coming.
Package: happenings
happenings - convenient timeseries creationThehappeningsmodudule is lightweight utility package built on Numpy/Pandas for creating complex event based time-series dataInstallationhappeningscan be installed from PyPi using pip:pipinstallhappeningsTestingAshappeningsis intended be lightweight, testing packages are not installed unless specified as extras which can also be installed from PyPi using pip:pipinstall-ehappenings.[test]pytestCode, Issues, and ContributionThe code and issue tracker are hosted on GitHub:https://github.com/TNonet/happenings/Basic ExampleLicenseThehappeningspackage and all related documentation, examples, and code is released under theMIT LicenseAuthorContact
Package: happer
happerDaniel Standage, 2018https://github.com/bioforensics/happerhapperis a minimal Python library for generating complete haplotype sequences.
Given a reference sequence and haplotype alleles annotated in BED format, happer will mutate the reference and produce sequences to match the specified haplotypes.InstallationTo install:pip3installhapperTo make sure the package installed correctly:pip3installpytest
py.test--pyargshapperhapper requires Python version 3.UsageReference sequences must be provided in Fasta format, and haplotype alleles must be specified in BED format as follows.
Alleles corresponding to different haplotypes at the locus are separated by a|character, so for example a diploid individual should have 2|-separated alleles annotated at each locus, while a tetraploid would have 4 alleles.
In the example below, theCCGAalleles are phased and represent one haplotype, while theTATGalleles are phased and represent another haplotype.#SeqID    Start  End     Alleles
chr1     38827  38828   C|T
chr1     59288  59289   C|A
chr2     24771  24772   G|T
chr4     201191 201192  A|GTo invoke happer from the command line:[standage@lappy ~]$ happer --out haploseqs.fasta refr.fasta alleles.bedTo invoke happer directly in Python:>>>importhapper>>>seqfile=open('refr.fasta','r')>>>alleles=open('alleles.bed','r')>>>forlabel,haploseqinhapper.mutate.mutate(seqfile,alleles):...# do whatever you'd like with the sequences
Package: happi
HAPPIHeuristic Access to Positioning of Photon InstrumentationMotivation•Features•Installation•Basic Usage•DocumentationMotivationLCLS endstations deal with dynamic sets of instrumentation. Information like
ports, triggers and aliases are all important for operation, but hard to manage
when spread across a multitude of applications.Happisolves this problem
by creating a single access point for all the metadata required to interface
with LCLS instrumentation. Using a flexiblecontainerbased system Happi
allows the enforcement of specific conventions while still permitting flexible
data entry where required.FeaturesManage information for specific device types using containersInput arbitrary metadata associated with a specific deviceFlexible backend support for multiple types of databases; MongoDB, JSON e.t.cEasily search database entries for device/s that match a set of keysInstallationInstall the most recent tagged build:conda install happi -c pcds-tag -c conda-forgeInstall the most recent development build:conda install happi -c pcds-dev -c conda-forgeBasic UsageThehappi.Clientis your main interface to the underlying device database.
You have the choice of either creating your database backend by hand or using
the environment variable$HAPPI_BACKENDto create a persistent reference to
your database type. By default, theClientassumes aJSONfile database:importhappiclient=happi.Client(path='path/to/my_db.json')If your database has entries, you should either be able to search by key
variables for individual or multiple devices.client.find_device(name="My Device")client.search(stand='DG2')Once you have the device you want, you can edit the information just as you
would any other Python object. View the device information in a
convenient table using.show_info:dev=client.find_device(name="My Device")dev.z=432.1dev.show_info()Output+--------------+----------------------+
| EntryInfo    | Value                |
+--------------+----------------------+
| active       | True                 |
| beamline     | LCLS                 |
| name         | My Device            |
| parent       | None                 |
| prefix       | MY:DEV:01            |
| stand        | None                 |
| system       | None                 |
| z            | 432.10000            |
+--------------+----------------------+After you are satisfied with your changes, push the information back to the
database using the.savemethod. If this is a new device, you will have to
callClient.add_device. Before the entry is modified in the database, thehappi.Clientconfirms that the new changes meet all the requirements
specified by the container.dev.save()
Package: happier
A Python development tool that makes you Happier.Happier formats, lints and sorts your imports. Happier is opinionated,
simple and just works, making you hopefully happier.To run it just typehappierand your code will be formatted.What Happier doesHappier does a number of things with your code. First it runsisortto sort your imports and properly format them.After having sorted them it runsautoflaketo remove unused things.Finally having done all of that it runsblackto format the code nicely.Why HappierHappier was developed by myself,William Rudenmalm (https://whn.se)because I felt that Python was falling behind on automatically
formatting and fixing my code. All the parts where already there I
just needed those 50 lines of code to tie it all together.With well-wishes and hopes for brigther days ahead in these troubled
times.~William RudenmalmStockholm, 22 June, 2020
Package: happiestfuntokenizing
Happiest Fun Tokenizer for python3This code is modified in order to adapt to Python3. And according to my own requirements, auther added some functions or params to preserve the specific tokens like nicknames, urls, and hashtags.
The former version of code implements a basic, Twitter-aware tokenizer. Originally developed byChristopher Potts(Happy Fun Tokenizer) and updated byH. Andrew Schwartz. Shared with Christopher's permission. Further updated byCaspar Chan.Usagefromhappiestfuntokenizing.happiestfuntokenizingimportTokenizertokenizer=Tokenizer()message="""OMG!!!! :) I looooooove this tokenizer lololol"""tokens=tokenizer.tokenize(message)print(tokens)['omg','!','!','!','!',':)','i','looooooove','this','tokenizer','lololol']message="""OMG!!!! :) I looooooove this tokenizer LoLoLoLoLooOOOOL"""tokenizer=Tokenizer(preserve_case=True)tokens=tokenizer.tokenize(message)print(tokens)['OMG','!','!','!','!',':)','I','looooooove','this','tokenizer','LoLoLoLoLooOOOOL']message="""RT @cahnnle #happyfuncoding: this is a typical Twitter tweet :-) https://twiter/test.phd"""tokenizer=Tokenizer(preserve_keywords=True)tokens=tokenizer.tokenize(message)print(tokens)['rt','@cahnnle','#happyfuncoding',':','this','is','a','typical','twitter','tweet',':-)']print(tokenizer..get_preserve_dict()){'username':['@cahnnle'],'url':['https://twiter/test.phd'],'hashtag':['#happyfuncoding']}InstallationThis is available throughpippipinstallhappiestfuntokenizingIf you do not have sudo privileges you can use the--userflagpipinstall--userhappiestfuntokenizingRequirementsThis uses Python 3.* Package dependencies includereandhtml.LicenseLicensed under aGNU General Public License v3 (GPLv3)BackgroundAdapted by theWorld Well-Being Projectbased out of the University of Pennsylvania
and Stony Brook University. Originally developed byChristopher Potts.