Saravanan666/pypi-org huggingface dataset:
Package: knot
Knot is a small do-it-yourself (DIY) dependency container for Python.Getting startedUnlike other existing implementations, knot does not make use of introspection.
Therefore, dependencies are manually defined in a straight forward manner. The
container acts as a central registry for providers and configuration settings.Configuration settingsThe container is just an ordinary dictionary with some additional methods. As a
result, it is very easy to assign or retrieve data from it. Probably the most
common way to assign configuration settings is passing a dict to the
constructor.fromknotimportContainerc=Container({'host':'localhost','port':6379})Obviously it is also possible to add configuration settings to an existing
container.c=Container()c['host']='localhost'c['port']=6379ProvidersA provider creates and returns a particular value or object. It has the ability
to utilize an injected container to retrieve the necessary configuration
settings and dependencies.The container expects a provider to adhere to the following rules:It must be callable.It must accept the container as the only argument.It must return anything exceptNone.Assigning a provider to a container is easy.defconnection(c):fromredisimportRedisreturnRedis(host=c['host'],port=c['port'])c.add_provider(connection,True)It is also possible to use a decorator.fromknotimportprovider@provider(c,True)defconnection(c):fromredisimportRedisreturnRedis(host=c['host'],port=c['port'])The second argument inc.add_provider(connection, True)and in@provider(c, True)indicates whether or not the return value of a provider
must be cached.Retrieve what you have defined.conn=c.provide('connection')For convenience, you can also use the shortcut.conn=c('connection')ServicesA service is just a provider with thecacheargument set toTrue.
Basically this means the return value is created only once.defconnection(c):fromredisimportRedisreturnRedis(host=c['host'],port=c['port'])c.add_service(connection)Or with a decorator.fromknotimportservice@service(c)defconnection(c):fromredisimportRedisreturnRedis(host=c['host'],port=c['port'])conn1=c('connection')conn2=c('connection')printconn1isconn2# TrueFactoriesA factory is just a provider with thecacheargument set toFalse.
Basically this means the return value is created on every call.defurgent_job(c):fromsomewhereimportJobconnection=c('connection')returnJob(connection=connection,queue='urgent')c.add_factory(urgent_job)job1=c('urgent_job')job1.enqueue('send_activation_mail',username='johndoe')job2=c('urgent_job')job2.enqueue('send_activation_mail',username='janedoe')printjob1isjob2# FalseOr with a decorator.fromknotimport@factory@factory(c)defurgent_job(c):fromsomewhereimportJobconnection=c('connection')returnJob(connection=connection,queue='urgent')InstallationInstall Knot with the following command:$pipinstallknotTestsTo run the tests, installtoxfirst:$pipinstalltoxThen, run the tests with the following command:$toxInspirationPimple (http://pimple.sensiolabs.org/)LicenseMIT, seeLICENSEfor more details.
Package: knot-floer-homology
Python wrapper for Zoltán Szabó’sHFK Calculator.InstallingTo install and test, do the following to install the current release
fromPyPI:python3 -m pip install knot_floer_homology
python3 -m knot_floer_homology.testYou should see a result such asTestResults(failed=0, attempted=24).UsageIn Python, do:>>> import knot_floer_homology
>>> PD = [(2,0,3,15),(0,6,1,5),(6,2,7,1),(3,10,4,11),(9,4,10,5),(7,12,8,13),(13,8,14,9),(11,14,12,15)]
>>> knot_floer_homology.pd_to_hfk(PD)
{'L_space_knot': False,
 'epsilon': 0,
 'fibered': True,
 'modulus': 2,
 'nu': 0,
 'ranks': {(-2, -2): 1, (-1, -1): 2, (0, 0): 3, (1, 1): 2, (2, 2): 1},
 'seifert_genus': 2,
 'tau': 0,
 'total_rank': 9}It also acceptsSpherogramknots as input:>>> import spherogram
>>> L = spherogram.Link('K10n10')
>>> ans = knot_floer_homology.pd_to_hfk(L)
>>> ans['seifert_genus']
3It can also return the simplified UV = 0 knot Floer chain complex,
do:>>> help(knot_floer_homology.pd_to_hfk)for details.LicenseCopyright Zoltán Szabó, Marc Culler, Nathan M. Dunfield, and Matthias
Goerner, 2017-present.  This code is released under theGNU General
Public License, version 2or (at your option) any later version as
published by the Free Software Foundation.
Package: knotify
KnotifyKnotify is a simple tool that helps you to push notifications and other messages to the dedicated clients
Include Telegram Bot, Webhook, WirePusher, Wechat Bot (Server Chan)Quick StartUse pip to install Knotify first:pipinstallknotifyThen inside your program:fromknotifyimportWirePusherw=WirePusher("Your_Token_From_WirePusher")w.emit("FooBar")RequirementPython 3.6+LicenseMIT License
Package: knot-injector
Knot Injector========Framework agnostic dependency injectionsThis library build as a wrapper for `Knot`_ Dependencies Container.Installation------------``pip install knot-injector``Usage-----Example:.. code:: pythonimport sqlalchemy as dbfrom sqlalchemy.orm import sessionmakerfrom knot_injector import Containerregistry = Container()settings = {'db': {'uri': 'sqlite://','echo': False,'encoding': 'utf8',},}registry.add_service(lambda _: settings, name='settings')@registry.service()def dbengine(container, settings):uri = settings['db']['uri']return create_engine(uri, **settings['db'])@registry.factory(name='dbsession')def database_session(container, dbengine):session = sessionmaker()session.configure(bind=dbengine)return session()class ItemResource(object):@registry.injectdef on_get(request, response, item_id, dbsession):response.data = dbsession.query(Item).filter(Item.id == item_id).one().. _Knot: https://github.com/jaapverloop/knot
Package: knot-keystore
knot-keystoreA tool to safely backup the knot kasp-db directory.overviewusage:knot-keystore[-h][--socketSOCKET][--plugins[{local,azure}[{local,azure}...]]][--retrieve][--config-fileCONFIG_FILE][-v]optionalarguments:-h,--helpshowthishelpmessageandexit--socketSOCKET,-sSOCKETpathtoknotccontrolsocket--plugins[{local,azure}[{local,azure}...]],-p[{local,azure}[{local,azure}...]]selectarchivalplugins--retrieve,-rretrievearchive--config-fileCONFIG_FILE,-cCONFIG_FILEpathtoaconfigurationfile-vincreaseoutputverbositytries to find the kasp-db path by readingknotdconfig over the control
socket.plugins:create an xz-compressed archive and put it somewhere, safely encrypted (default)retrieve and decrypt the stored archive, ready to restore to the kasp-db
directory (with--retrieve)available pluginslocal: create an encrypted copy of the archive and write it to disk along
with the encryption key. Mostly useful for testing.azure: write the archive to an Azure storage blob, first encrypting it using
"client-side-encryption" with a KEK stored in Azure Key Vault.
Package: knot-pull
KnotPull reduces a user provided 3D structure, to simplify it,
while preserving the topology of the chain. It has been successfully
used for knot detection in proteins and chromatine chains.It can return either a final, simplified structure, or a .pdb formatted
trajectory of consecutive simplifications performed.Finally, it can give a knot type assignment (including connected knots and links)
of the simplified structure (using the Dowker notation for topology detection).
Package: knotr
StitchAknitr-RMarkdown-like library, in Python.Note:You might want to consider Jan Schulz’sknitpyinstead. It’s probably more mature at this point. However, I wanted
to see if there was a simpler way of doing things.The high-level goal of this type of library (knitr/RMarkdown, knitpy, and stitch) is to make writing
reproducible reports easier.Documentation is availablehere.ExamplesSee the project’sexamples pagefor a
side-by-side comparison of input markdown and stitched HTML.More complex examples are linked to from there as well.Installstitchsupports Python 3.5 and above.
At the momentstitchcan be installed from pip viapipinstallknotrI know, it’s confusing.
I’ve filed a claim forstitchon PyPI, but I think the people working that support queue are over-worked.
Once that gets processed, I’ll put it up on conda-forge as well.
If you need a mnemonic, it’s “I want knitr, butnotthe one written inR.”
Also I wanted to confuse R users.
And knots are kind of like a buggy version of knits.stitchrequires pandoc>=1.18. This can be installed using your
system package manager, orpypandoc.DesignThe goal was to keepstitchitself extremely simple by reusing
existing libraries. A high level overview of our tasks isCommand-line InterfaceParse markdown fileExecute code chunks, capturing the outputCollate execution output into the documentRender to final outputFortunately the building blocks are all there.We reusepandocviapypandocfor parsing
markdown and rendering the final outputjupyterfor
language kernels, executing code, and collecting the outputUsepandocfiltersto
collate the execution output into the documentSo allstitchhas to do is to provide a command-line interface, scan
the document for code chunks, manage some kernels, hand the code to the
kernels, pass the output to an appropriatepandocfilter.The biggest departure fromknitpyis the use of pandoc’s JSON AST.
This is what you get frompandoc-tjson input.mdThis saves us from having do any kind of custom parsing of the markdown.
The only drawback so far is somewhat inscrutable Haskell exceptions ifstitchhappens to produce a bad document.DocumentationStitch’s documentation has an odd build process, so standard tools like
readthedocs weren’t flexible enough.
To make the docs, install stitch and all the extra dependencies.
Clonehttps://github.com/pystitch/pystitch.github.ioCheckout thesrcbranch.Runmake html.
Package: knotty
Welcome to Knotty, the easiest way to tie down your application performance metrics!NOTE: While all the basic functionality is here, Knotty is still in Alpha development. The project aims to have
more libraries automatically tracked, as well as more export options. APIs and functionality are also apt to
change with little to no warning.Modern applications require close monitoring, and Knotty aims to provide everything you need out of the box.
Importing the library will automatically register numerous metrics regarding the performance of the application
and the system it is operating on, including hardware utilization, application logging rates, http request rates,
and many more. Beyond the build in metrics, there is also an extremely powerful meters API that allows you to
create whatever custom metrics you would like.Once you have your meters defined, Knotty also provides a simple API for exporting your metrics to a number of
commonly used metrics backends (Prometheus, InfluxDB, Graphite, etc).Here's a simple example of how you can add a Prometheus endpoint to a Flask application:fromflaskimportFlaskfromknotty.exportersimportPrometheusExporterapp=Flask(__name__)PrometheusExporter(flask_app=app)@app.route('/hello',allowed_methods=['GET'])defhello()return'Hello World'app.run()
Package: knovleks
KnovleksPersonal Search Engine for different types of resources.Knovleks can currently index websites, pdf files and text notes.InstallUsageIndexSearchTag filterTUISearchbar focusedResults focusedInstallpip install knovleksUsageUsage: knovleks [OPTIONS] COMMAND [ARGS]...

Options:
  -h, --help  Show this message and exit.

Commands:
  index
  search      full-text search
  tag-filter  tag filter
  tui         terminal user interface (experimental)IndexUsage: knovleks index [OPTIONS] DOCUMENT

Options:
  -t, --tag TEXT
  --title TEXT
  -d, --type, --document-type TEXT
  -h, --help                      Show this message and exit.SearchUsage: knovleks search [OPTIONS] QUERY

  full-text search

Options:
  -t, --tag TEXT
  -st, --show-tags
  -l, --limit INTEGER
  -dt, --doc-type TEXT
  -ft, --full-text      display full text
  -h, --help            Show this message and exit.Tag filterUsage: knovleks tag-filter [OPTIONS] [TAG]...

  tag filter

Options:
  -st, --show-tags
  -l, --limit INTEGER
  -dt, --doc-type TEXT
  -h, --help            Show this message and exit.TUISwitch focus: TAB
Next result: ctrl+j
Previous result: ctrl+k
Open result without closing: ctrl+lSearchbar focusedExit: ESCResults focusedSwitch focus to searchbar: ESC
Open result: Enter
Package: know
knowBuild live stream toolsTo install:pip install knowThe tools are made to be able to create live streams of data
(e.g. from sensors) and funnel them into proceses with a consistent interfaces.
One important process being the process that will store all or part of
the data, through a simple storage-system-agnositic facade.proc=LiveProc(source=Sources(# make a multi-source object (which will manage buffering and timing)audio=AudioSource(...),plc=PlcSource(...),video=VideoSource(...),),services=Services(# make a multi-data service (and/or writer/transformer) objectstorage=Store(...),notifications=Notif(...),live_viz=LiveViz(...),),...# other settings for the process (logging, etc.))proc()# run the processWith a variety of sources, target storage systems, etc.ExamplesRecording Audiofromknow.audio_to_storeimport*wfs=demo_live_data_acquisition(chk_size=100_000,end_idx=300_000,logger=print)print(f"{len(wfs)=}")Theend_idx=300_000is there to automatically stop the demo after 300 thousand bytes are acquired
(at the default rates that's about 1.5 seconds).If you specifyend_idx=None, the process will run until you interrupt it.Recording Audio, with more controlHere's what's actually being fed to the demo when you don't specify any explicitlive_sourceorstore.
You can use this to try different combinations of specs out:fromknow.audio_to_storeimport*wfs=demo_live_data_acquisition(live_source=LiveWf(input_device_index=None,# if None, will try to guess the device indexsr=44100,sample_width=2,chk_size=4096,stream_buffer_size_s=60,),store=mk_session_block_wf_store(rootdir=None,# will choose one for you# template examples: '{session}_{block}.wav' '{session}/d/{block}.pcm', '{session}/{block}', 'demo/s_{session}/blocks/{block}.wav'template='{session}/d/{block}.pcm',#pattern=r'\d+',value_trans=int),chk_size=100_000,end_idx=300_000,logger=print)print(f"{len(wfs)=}")What you want to see above, is how you can easily change the folder/file template you use to store data.Below, we'll also show how you can change the data storage system backend completely,
using a mongo database instead!Here, see how you can use MongoDB to store your data.
For this, you'll need to have amongoDBserver running locally,
andmongodolinstalled (pip install mongodol).fromknow.audio_to_storeimportmk_mongo_single_datadef_cast_data_field_to_json_list(d):returnlist(map(int,d))play_nice_with_json=wrap_kvs(data_of_obj=_cast_data_field_to_json_list)mongo_store=play_nice_with_json(mk_mongo_single_data(mgc='mongodol/test',# enter here, the `db_name/collection_name` you want to use (uses local mongo client)key_fields=('session','block'),data_field='data'))wfs=demo_live_data_acquisition(live_source=LiveWf(input_device_index=None,# if None, will try to guess the device indexsr=44100,sample_width=2,chk_size=4096,stream_buffer_size_s=60,),store=mongo_store,chk_size=100_000,end_idx=300_000,logger=print)print(f"{len(wfs)=}")
Package: knowage-python
Python webservice for KnowageKnowage-python is a webservice that allows users to directly embed python scripts into Knowage cockpits.
The service exposes REST APIs for retrieving python code, run it and return results as images, html or bokeh applications.Installknowage-python can be installed from pypipip install knowage-pythonContributionsBefore start to contribute, please read and sign theContributor License Agreement.DocumentationThe official documentantion is available atRead the Docs.MorePlease visitthe project websitefor information
about the Enterprise Edition.LicenseAGPLÂ© 2020 Engineering Ingegneria Informatica S.p.A.
Package: knowhere
knowhereIn memory cache, a python implementation ofzizouwith a different expiration policyFeaturesStore millions of entriesHigh concurrent thread-safe accessExpiration supportShard support to avoid locks on whole db during any concurrent read/writes/deletesInstallationpipinstallknowhereUsageInitializeshard_size should be a power of 2. This size must be set according to the number of keys you expect to be there in the cache at any given time.fromdatetimeimporttimedeltafromdecimalimportDecimalimportuuid,time,json,sys,knowherec=knowhere.Cache(shard_size=2,eviction_interval=timedelta(seconds=10))Setc.set("key","value",ttl=timedelta(seconds=5))Default expiry will be set to 5 minutes if no ttl is specifiedGetc.get("key")Deletec.delete("key")Flushdeletes all keys in the cachec.flush()Infogives all shards and number of keys in each shard as a jsonc.info()Keysgives a list of all keys in the cachec.keys()Expiration policyknowhere expires keys the same way redis does. You can find ithere. Specifically, this is what knowhere does on every eviction interval for each shardStep 1: Get 20 random keys from the shardStep 2: Delete all the keys which should be expired.Step 3: If more than 4 of the 20 keys were expired, start again from step 1
Package: knowipy
Python3 - Knowi API SDK (knowipy)This repository houses the official Python SDK for use with Knowi.Knowi is an augmented analytics platform that instantly transforms any data into insights and data driven actions.AboutThe Knowi API SDK is the simplest way to automate your Knowi instanceThe SDK streamlines the API programming experience, allowing you to significantly reduce your written codeThis SDK was written for Python3, and is not compatible with Python2This library requires Python 3.6 and above.Execute scripts viapython3FeaturesDashboard ManagementList, create, edit, clone, share, and delete dashboardsView widgets in a dashboard, export dashboards to PDFGenerate simple/secure dashboard share urlList, share, delete dashboard filter-setsWidget ManagementList, create, edit, clone, share, and delete widgetsGenerate simple/secure widget share urlDatasource ManagementList, clone, share, and delete datasourcesQuery ManagementList, clone, share, run, and delete queriesCategories ManagementList, copy, share, run, and delete categoriesAdd and remove assets to/from categoriesUser ManagementList, create, edit, and delete usersTransfer user assets to another userGroups ManagementList, create, edit, and delete groupsNatural Language Query ManagementGet NLP query suggestions on a datasetExecute NLP queries against a datasetDataset ManagementRetrieve data from a dataset or querySingle Sign On (SSO)Create and manage SSO usersUpdate SSO user attributes including user content filtersNote: The resources below requires additional configuration. Contactsupport@knowi.comfor assistance.Parent/Master Customer (Multi-tenancy)Create and manage a multi-tenancy model with a parent/master to sub-customer relationshipAdminister assets and access from the parent/master to sub-customersSystem Status Health ChecksChecks all or specified Knowi components for connectivity and status reportInstallationWe recommend usingPyPIto install the Knowi SDK for Python$pip3installknowipy--upgradeNote:You may need to usepython3before your commands to ensure you use the correct Python path. e.g.python3 --versionpython--version

--or--

python3--versionUsageSeeexamples.pyfor full usage and examplesRetrieve your Client id/secret from yourKnowiaccountNavigate to Settings -> Account Settingshttps://recordit.co/HAudn0LJ2EfromknowipyimportKnowiimportos# instantiate the clientknowi=Knowi(clientId=os.environ['KNOWI_CLIENT_ID'],clientSecret=os.environ['KNOWI_CLIENT_SECRET'])# note: for on-prem deployments set the `host=<knowi_server_ip>` variableknowi=Knowi(clientId=os.environ['KNOWI_CLIENT_ID'],clientSecret=os.environ['KNOWI_CLIENT_SECRET'],host="≤knowi_server_ip>")# get a list of dashboardsdashboard_list=knowi.dashboard_list()# to refresh an existing queryquery_refresh=knowi.query_refresh(queryId=1231)DevelopmentGetting StartedAssuming that you have Python3 andvirtualenvinstalled, set up your
environment and install the required dependencies like this instead of
thepip3 install knowipydefined above:$gitclonehttps://github.com/ezeagwulae/knowi-python-sdk.git$cdknowipy$virtualenvvenv...$.venv/bin/activate$pipinstall-rrequirements.txt$pipinstall-e.
Package: know_its_ok
The purpose of Know It’s Ok is to make it as easy as possible to run your tests
while you’re debugging your code.  In particular, it’s main features are:It finds your tests from anywhere in your repository.It runs your tests with arguments tailored towards debugging.It only requires typing two letters:okInstallationKnow It’s Ok is available from PyPI:$ pip install know_its_okIt’s only dependencies are pytest and pytest-cov, which should both be
installed automatically.UsageYou can run Know It’s Ok from anywhere in your package’s repository:$ okYou can also specify individual tests to run (by default they will all be run).
You don’t need to specify the complete name of a test, any substring of a test
will be understood.  For example, if you have a test calledtest_foo.py,
you can use this command to run it:$ ok fooYou can also specify arguments that should be passed through topytest(this won’t work if the argument topytestalso matches the name of one of
your tests, but in practice this is never a problem):$ ok --pdbIn order to discover your tests simply, Know It’s Ok is very opinionated about
the organization of your code.  In particular:Your code must be version controlled usinggit.Your tests must be stored in a directory calledtests/, which itself
must be located in the top level of your repository.Inside thetests/directory, your tests must match the pattern:??_test_*.py.Your tests must be written using thepytestframework.Your package must be located in a directory containing and__init__.pyfile.If any of these expectations are not met, the code should raise an exception.
Package: knowl-apidocs
Key FeaturesFully Automated: Generates complete API documentation directly from your Django repository, requiring no manual effort.Easy to Use: Simply runknowl_apidocs <path_to_your_Django_repository>, and your API documentation will be ready in theapi-referencefolder.No Prerequisites Needed: All necessary components are installed along withknowl-apidocs.Python Compatibility: Supports Python version 3.9 and above.Getting StartedInstallknowl-apidocsvia pip:pip install knowl-apidocsGenerate your API documentation:knowl_apidocs <path_to_your_Django_repository>Check theapi-referencefolder in your project directory for the generated documentation.Support and ContributionsFor open issues or specific requests regarding API documentation, visit ourGitHub issues.Need help or have questions? Contact us atsupport@knowl.io.Start simplifying your API documentation process withknowl-apidocstoday!
Package: knowledge
Knowledge is a polymorphic vertical database based on SQLAlchemy.  It provides
a dict-like interface to the database.Knowledge is comprised of two related objects: Fact()s and Entity()s.  Entities
can be created and then have arbitrary facts applied to them.  Before Knowledge
can be used, the DB has to be set up with SQLAlchemy as follows:from sqlalchemy import create_engine
from knowledge.model import init_model, metadata
engine = create_engine('sqlite:///knowledge.db')
init_model(engine)
metadata.create_all(engine)Using knowledge is easy.  Entities are created with a key, then facts about
the entity can be applied like values in a dictionary:from knowledge.model import Entity, DBSession

monster = Entity(u'Monster')
fairy = Entity(u'Fairy')
rjbean = Entity(u'rjbean')
monster[u'color'] = u'Green'
monster[u'name'] = u'Lotharrr'
fairy[u'flies'] = True
fairy[u'name'] = u'Bell'
rjbean[u'name'] = u'ralph'
rjbean[u'flies'] = False
rjbean[u'hacks'] = True

DBSession.add(monster)
DBSession.add(fairy)
DBSession.add(rjbean)
DBSession.commit()Retrieving Entities and Facts from the DB works just like any other SQLAlchemy
application:from knowledge.model import Entity, DBSession

# Query all the Entities out of knowledge
knowledge_query = DBSession.query(Entity).all()
for entity in knowledge_query:
    print entity, entity.facts.values()