Saravanan666/pypi-org huggingface dataset:
Package: bel2scm
bel2scmThis package is for creating Structural Causal Models (SCMs) in Pyro and evaluating various conditions with those models.The causal model (example) can be created from a list of BEL statements strings (causal_graph.str_graph;http://language.bel.bio/language/structure/), a pyBEL graph (causal_graph.bel_graph;https://pypi.org/project/pybel/), or a json file created by exporting a causal graph from Causal Fusion (causal_graph.cf_graph;https://causalfusion.net/login).  Each causal model consists of nodes connected by directed edges.  Each node then has parameters defining the distribution of that node's variables conditioned on the values of the parent nodes.  These parameters are learned from data -- each graph can learn these parameters either using Maximum Likelihood Estimation (for point estimates) or Stochastic Variational Inference (for Bayesian estimates).  Currently, Bernoulli, Normal, Lognormal, Exponential, and Gamma output distributions are supported; the choice of distribution is either specified during the initialization or defaults to a hard-coded mapping from BEL object types to distributions.Once the training process is complete, the causal model can be queried in several different fashions.  The basic query is to sample all of the nodes of the model and return a dictionary of node names and samples (example.model_sample).  Using built-in Pyro functionality, the model can then leverage this to calculate conditioned samples (example.model_cond_sample), interventional samples using the do-calculus (example.model_do_sample, example.model_do_cond_sample), and counterfactual samples (example.model_counterfact).The package includes a method to calculate the Conditional Mutual Information of a target node with respect to a test node of interest (example.cond_mut_info).  This calculation relies only on the input data, not the model itself.  However, the SCM also has a built-in method to perform the G-test on a variable of interest (example.g_test) to determine if the SCM sufficiently captures the distribution represented by the provided data.  Note that performing both of these calculations requires binning the data to produce discrete distributions.With the various methods for sampling conditional, interventional, and counterfactual distributions from the model, the SCM can estimate the Controlled Direct Effect (example.cd_effect), the Natural Direct Effect (example.nd_effect), and the Natural Indirect Effect (example.ni_effect).  Finally, the SCM can write itself to a json file that can then be imported directly to Causal Fusion (example.write_to_cf).
Package: bela
👸 BELABLIPELANLanguageAnnotation - A method for creating and processing multi-lingual transcriptions usingBELA conventionandELAN software.Documentation:https://blipntu.github.io/belacon/Source code:https://github.com/letuananh/belaPyPI:https://pypi.org/project/bela/InstallationBELAis available onPyPIand can be installed using pip:pip install belaCitationTuan Anh Le, Seraphina Fong, Jinyi Wong,
Fei Ting Woon, Eshwaaree C Yogarrajah, Nur Sakinah Binte Mohd Salleh, Shaza binte Amran,
Suzy J Styles, 2021,"BELA - A Processing Pathway for Multilingual Parent-child Conversations",BMRS 2021LicenseBELApackage is licensed under MIT License.
Package: b-elasticsearch-layer
TODORelease history0.0.6Set ElasticSearch version which currently supports AWS ElasticSearch implementation.0.0.5Ensure consistent asset builds.Add ability to install boto3 dependency.0.0.4Update Docker bundling commands. Use bash to move files between directories instead of installing with-tflag. Related
issue:https://github.com/pypa/pip/issues/1030.0.1Initial build.Elasticsearch-python 7.9.1.Docker image for asset building: python:3.9.
Package: belaweb
BELA DashboardBELA Dashboard is a web-based user interface for visualising and analysing BELA transcripts.
To learn how to create BELA transcripts, please refer to theBELA convention documentation.BELA Dashboard uses thePython bela packageto process transcripts under the hood.
Users who want to perform batch processing or data extraction can usebelapackage directly using Python scripts.Installationbelawebis available onPyPIand can be installed using pip:pipinstallbelawebDevelopmentTo run belaweb locally for testing, there is a minimal WSGI application available athttps://github.com/letuananh/belaweb# activate a Python (virtual) environment with belaweb package installedcdbelaweb-wsgi-min
./manage.pyrunserverThen access belaweb locally using a browser:http://localhost:8000/bela/gui/Licensebelawebpackage is licensed under MIT License.
The following packages are included:Chart.js v2.9.4- MIT License
Package: belay
Belay is:A python library that enables the rapid development of projects that interact with hardware via a MicroPython or CircuitPython compatible board.A command-line tool for developing standalone MicroPython projects.A MicroPython package manager.Belay supports wired serial connections (USB) and wireless connections via WebREPL over WiFi.Quick Video of Belay in 22 seconds.Seethe documentationfor usage and other details.Who is Belay For?Belay is for people creating a software project that needs to interact with hardware.
Examples include:Control a motor so a webcam is always pointing at a person.Turn on an LED when you receive a notification.Read a potentiometer to control system volume.The Belay Package Manager is for people that want to use public libraries, and get them on-device in
an easy, repeatable, dependable manner.What Problems Does Belay Solve?Typically, having a python script interact with hardware involves 3 major challenges:On-device firmware (usually C or MicroPython) for directly handling hardware interactions. Typically this is developed, compiled, and uploaded as a (nearly) independent project.A program on your computer that performs the tasks specified and interacts with the device.Computer-to-device communication protocol. How are commands and results transferred? How does the device execute those commands?This is lot of work if you just want your computer to do something simple like turn on an LED.
Belay simplifies all of this by merging steps 1 and 2 into the same codebase, and manages step 3 for you.
Code is automatically synced at the beginning of script execution.The Belay Package Manager makes it easy to cache, update, and deploy third party libraries with your project.InstallationBelay requires Python>=3.8and can be installed via:pipinstallbelayThe MicroPython-compatible board only needs MicroPython installed; no additional preparation is required.
If using CircuitPython, and additional modification needs to be made toboot.py. Seedocumentationfor details.ExamplesTurning on an LED with Belay takes only 6 lines of code.
Functions decorated with thetaskdecorator are sent to the device and interpreted by the MicroPython interpreter.
Calling the decorated function on-host sends a command to the device to execute the actual function.importbelaydevice=belay.Device("/dev/ttyUSB0")@device.taskdefset_led(state):print(f"Printing from device; turning LED to{state}.")Pin(25,Pin.OUT).value(state)set_led(True)Outputs fromprintcalls from on-device user-code are forwarded to hoststdout.For more examples, see the examples folder.
Package: belch
DescriptionBELCH Password List Generator is a simple tool to generate password
lists based on a given pattern. You can specify the password pattern and
generate multiple unique passwords.InstallationInstall usig PIP:pipinstallbelchUsageTo generate passwords, run the following command from anywhere on your
system:belchFollow the on-screen instructions to specify the password pattern and
the number of passwords to generate.Password PatternsYou can use the following characters in your pattern:/d : Digit/c : Lowercase/C : Uppercase/e: Special characters/? : Random charactersFor example, the pattern [/C/c-pass-/d/?] will generate passwords with a
combination of uppercase, lowercase, digits, and random characters in
specified order.LicenseThis project is licensed under the GNU-GPL License. See the LICENSE file
for more details.
Package: bel-commons
An environment for curating, validating, and exploring knowledge assemblies
encoded in Biological Expression Language (BEL) to support elucidating
disease-specific, mechanistic insight.InstallationBEL Commons can be installed easily fromPyPIwith the following code in
your favorite shell:$pipinstallbel_commonsGet the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bel-commons/bel-commons.gitIt’s also suggested to use a relational database management system like PostgreSQL
and install their corresponding connectors:$python3-mpipinstallpsycopg2-binaryUsageRun BEL CommonsA test server can be easily run with:$bel-commonsrunA more powerful server likegunicorncan also be used like:$gunicornbel_commons.wsgi:flask_appRunning with the ParserTo run the parser, you’ll need an instance of a message queue likeRabbitMQ(or
any other message queue supported byCelery), a results backend likeRedis, and a worker. It’s best to run in docker if you want to do this.Run with DockerClone this repo from GitHub$gitclonehttps://github.com/bel-commons/bel-commons.git$cdbel-commonsCreate a file called.envand generate bothSECRET_KEYandSECURITY_PASSWORD_SALT.SECRET_KEY=mypasswordSECURITY_PASSWORD_SALT=mypasswordBUTLER_NAME="BEL Commons Butler"BUTLER_EMAIL=bel@example.comBUTLER_PASSWORD=butlerpasswordRun docker compose:$docker-composeupPorts exposed:5002: BEL Commons web application5432: PostgreSQL databaseReset the DatabaseFor the times when you just have to burn it down and start over:bel-commonsmanage dropwill nuke the database and output a user listbel-commonsmanage loadwill automatically add the most recently exported
user listbel-commonsmanage examples loadwill automatically load some example
networks and data setsCitationIf you find BEL Commons useful in your work, please consider citing[Hoyt2018]and[Hoyt2017]:[Hoyt2018]Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018).BEL Commons: an environment for
exploration and analysis of networks encoded in Biological Expression Language.Database, 2018(3), 1–11.[Hoyt2017]Hoyt, C. T., Konotopez, A., & Ebeling, C., (2017).PyBEL: a computational framework for Biological
Expression Language.Bioinformatics,
34(4), 703–704.AcknowledgementsSupportersThis project has been supported by several organizations:University of BonnBonn Aachen International Center for ITFraunhofer Institute for Algorithms and Scientific ComputingFraunhofer Center for Machine LearningIMI(in theAETIONOMYproject)LogoThe BEL Commonslogowas designed byScott Colby.
Package: bel-digital-handwriting-py
bel-digital-handwriting-pybel-digital-handwriting-py- гэта Python бібліятэка, з дапамогай якой вы можаце хутка
аналізаваць беларускія тэксты па спецыяльных крытэрыях.importBelDigitalHandwriting.BelDHAnalyserasBelDHAnalysertext_file=open("text.txt","r",encoding="utf-8")text=text_file.read()analyze=BelDHAnalyser.analyse_text(text)Што можа гэта бібліятэкаАналізавацьтэксты:analyze = BelDHAnalyser.analyse_text(text)Сумяшчацьаналізы тэкстаўcommom_analyze = analyze_1 + analyze_2Як карыстацца гэтай бібліятэкайКаб пачаць выкарыстоўвацьbel-digital-handwriting-py, вы павінны ўсталяваць бібліятэку
праз PIP:pip install bel-digital-handwriting-pyЯк працуе гэты пакетУ гэтым раздзеле будуць апісаны асноўныя прынцыпы работыbel-digital-handwriting-pyКрытыэрыі аналізуДля выяўлення лічбавага почырку пісьменніка быў складзены спіс крытыэрыяў, па якім будзе праходзіць
аналіз тэксту:Частата з'яўлення ў тэксцекожнагасімвала:$$ \nu_{сімвала} = {колькасць\ паўтарэнняў\ сімвала \over колькасць\ сімвалаў}$$Частата з'яўлення ў тэксцегалосныхізычныхлітар:$$ \nu_{галосных} = {колькасць\ галосных \over колькасць\ літар}\qquad \nu_{зычных} = {колькасць\ зычных \over колькасць\ літар}$$Сярэдняя даўжыняслова:$$ \langle l_{слова} \rangle = {сума\ даўжынь\ усіх\ слоў \over колькасць\ слоў}$$Сярэдняя даўжыня сказа (па колькасьцісімвалаўі колькасьціслоў):$$ \langle l_{сказа:\ сімвалы} \rangle = {колькасць\ усіх\ сімвалаў \over колькасць\ сказаў} \qquad \langle l_{сказа:\ словы} \rangle = {колькасць\ усіх\ слоў \over колькасць\ сказаў}$$Сярэдняя колькасць косак(,;)на сказ:$$ \langle N_{косак} \rangle = {колькасць\ усіх\ косак \over колькасць\ сказаў}$$Працэнтклічныхіпытальныхсказаў:$$ \nu_{кліч} = {колькасць\ клічнікаў \over колькасць\ сказаў}\qquad \nu_{пыт} = {колькасць\ пытальнікаў \over колькасць\ сказаў}$$Частата выкарыстання слоў кожнай часціны мовы (больш падрабязна пра гэта напісана ўнаступным раздзеле):$$ \nu_x = {колькасць\ слоў\ гэтай\ часціны\ мовы \over колькасць\ слоў}$$Статыстыка па парам слоў, якія пачынаюцца/заканчваюцца на галосны/зычны(ГГ, ГЗ, ЗГ, ЗЗ):$$ \nu_{тыпа\ пар} = {колькасць\ пар\ аднаго\ тыпа \over колькасць\ ўсіх\ пар}$$Выкарыстанне Граматычнай базыДля вызначэння часціны мовы, да якой адносіцца канкрэтнае слова была выкарыстанаГраматычная базабеларускай мовы. З дапамогай распрацаванага мною Python скрыпта ўсе словаформы кожнай часціны мовы былі сабраныя па асобных файлах ў фармаце:слова#слова#слова#слова#слова#...Спасылкі на выкарыстоўваемые матэрыялыУ праекце выкарыстоўваюцца слоўнікі Беларускага N-корпусу.
Спасылка на іх рэпазітар Github -GrammarDB.
Граматычная база распаўсюджвацца па ліцэнзііCC BY-SA 4.0.
Спасылка на ліцэнзію Граматычнай базы -CC BY-SA 4.0
Package: bel-enrichment
A package for generating curation sheets for rationally enriching a BEL graph.If you findbel_enrichmentuseful in your work, please consider citing[1]:[1]Hoyt, C. T.,et al(2019).Re-curation and Rational Enrichment of Knowledge Graphs in
Biological Expression Language.Database, Volume 2019, 2019, baz068.Additionally, this package also heavily builds on INDRA[2]and PyBEL[3].Installationbel_enrichmentcan be installed from PyPI with the following command:$pipinstallbel_enrichmentThe latest version can be installed from GitHub with:$pipinstallgit+https://github.com/bel-enrichment/bel-enrichment.gitYou’ll need to set theINDRA_DB_REST_URLandINDRA_DB_REST_API_KEYin the~/.config/indra/config.inifile. Please contact the INDRA team
for credentials.Rational EnrichmentGenerate a folder full of curation sheets based on the given BEL graph that has been pre-compiled by PyBEL.
Use--info-cutoffto specify the minimum information density cutoff. 1.0 means that the node has no edges, .5 means
one edge, and so on. Use--belief-cutoffto specify the minimum belief score from INDRA for adding the statement
to the sheet. Higher belief means the more chance a statement is already right.$bel-enrichmentfrom-graphzhang2011.bel--directory~/Desktop/zhang-enrichmentGenerate a ranking for genes based on the information content in a given BEL graph that has been pre-compiled by PyBEL.$bel-enrichmentrankszhang2011.belDocument-Based CurationIf you want to make a curation sheet based on a PubMed identifier (or list of them) do this:$bel-enrichmentfrom-pmids2058558720585588>~/Desktop/document_based.tsvTopic-Based CurationIf you want to make a curation sheet based on an entity, do this:$bel-enrichmentfrom-agentsMAPTGSK3B>~/Desktop/topic_based.tsvReferences[2]Gyori, B. M.,et al.(2017).From word models to executable models of signaling networks using automated
assembly. Molecular Systems Biology, 13(11), 954.[3]Hoyt, C. T., Konotopez, A., Ebeling, C., (2017).PyBEL: a computational framework for Biological Expression
Language. Bioinformatics (Oxford, England), 34(4), 703–704.
Package: belfry_magicbell
MagicBell-Python SDKThis SDK provides convenient access to theMagicBell REST APIfrom applications written in Python. 
It includes helpers for creating notifications, managing users, managing projects, and executing GraphQL.This is an unofficial Python SDK forMagicBellopen sourced with ❤️ byNoteable, a collaborative notebook platform that enables teams to use and visualize data, together.Install|Getting Started|Examples|License|Code of Conduct|ContributingRequirementsPython 3.8+InstallationPoetrypoetryaddbelfry_magicbellThen import the package:importbelfry_magicbellPippipinstallbelfry_magicbellThen import the package:importbelfry_magicbellGetting Startedimportbelfry_magicbellfrombelfry_magicbell.configurationimportConfigurationconfig=Configuration(api_key="YOUR_API_KEY",api_secret="YOUR_API_SECRET",)asyncwithbelfry_magicbell.MagicBell(config)asmb:# Send a notificationawaitmb.realtime.create_notification(belfry_magicbell.WrappedNotification(notification=belfry_magicbell.Notification(title="My first notification from python!",recipients=[belfry_magicbell.Recipient(email="dan@example.com")],)))AuthenticationMost API calls require your belfry_magicbell project API Key and API Secret.
Some API calls (i.e. projects) require your belfry_magicbell user JWT (enterprise only).See theMagicBell API documentationfor more information.ConfigurationConfiguration can be done explicitly using thebelfry_magicbell.Configurationclass,
or implicitly by setting environment variables with theMAGICBELL_prefix.Explicit Configurationfrombelfry_magicbell.configurationimportConfiguration# Create a configuration object with the required parametersconfig=Configuration(api_key="YOUR_API_KEY",api_secret="YOUR_API_SECRET",)Implicit ConfigurationexportMAGICBELL_API_KEY="YOUR_API_KEY"exportMAGICBELL_API_SECRET="YOUR_API_SECRET"frombelfry_magicbell.configurationimportConfigurationconfig=Configuration()ExamplesFor more examples see theexamplesdirectory.ContributingSeeCONTRIBUTING.md.Open sourced with ❤️ byNoteablefor the community.
Package: belfry_python_anvil
Anvil API LibraryThis is a library that provides an interface to access theAnvil APIfrom applications
written in the Python programming language.Anvilprovides easy APIs for all things paperwork.PDF filling API- fill out a PDF template with a web request and structured JSON data.PDF generation API- send markdown or HTML and Anvil will render it to a PDF.Etch e-sign with API- customizable, embeddable, e-signature platform with an API to control the signing process end-to-end.Anvil Workflows (w/ API)- Webforms + PDF + e-sign with a powerful no-code builder. Easily collect structured data, generate PDFs, and request signatures.Learn more on ourAnvil developer page. See theAPI guideand theGraphQL referencefor full documentation.DocumentationGeneral API documentation:Anvil API docsSetupRequirementsPython 3.7.2+InstallationInstall it directly into an activated virtual environment:$pipinstallpython-anvilor add it to yourPoetryproject:$poetryaddpython-anvil
Package: belief-propagation-ldpc
Belief PropagationThis repo is aimed at providing an example implementation of belief propagation on Tanner graphs as used by a standard
LDPC decoder.You can also view my associated Mediumpost.If you see bug, wish suggest something or contribute please open an issue.
Alternatively you can also contact me viaemail.For more information on LDPC codes and belief propagation on Tanner graphs visit my GitHub pages site:https://yairmz.github.io/LDPC/.Installationpipinstallbelief-propagation-ldpcAPINodesOne can create nodes (either variable or check nodes) via:frombelief_propagationimportVNode,CNode,bsc_llrv=VNode(name="v0",channel_model=bsc_llr(0.1))c=CNode(name="c0")GraphsCreating a graph:frombelief_propagationimportTannerGraph,bsc_llrimportnumpyasnptg=TannerGraph()# add 10 variable nodesforiinrange(10):tg.add_v_node(name="v"+str(i),channel_model=bsc_llr(0.1),ordering_key=i)# add 5 check nodesforjinrange(5):tg.add_c_node(name="c"+str(j),ordering_key=j)edges={("v0","c0"),("v0","c1"),("v1","c0"),("v1","c2"),("v2","c0"),("v2","c3"),("v3","c0"),("v3","c4"),("v4","c1"),("v4","c2"),("v5","c1"),("v5","c3"),("v6","c1"),("v6","c4"),("v7","c2"),("v7","c3"),("v8","c2"),("v8","c4"),("v9","c3"),("v9","c4")}tg.add_edges_by_name(edges)# Alternatively given a biadjacency matrix (parity check matrix) a graph can be constructed from itH=np.array([[1,1,1,1,0,0,0,0,0,0],[1,0,0,0,1,1,1,0,0,0],[0,1,0,0,1,0,0,1,1,0],[0,0,1,0,0,1,0,1,0,1],[0,0,0,1,0,0,1,0,1,1]])tg=TannerGraph.from_biadjacency_matrix(H,channel_model=bsc_llr(0.1))A graph may also be converted into aNetworkXGraphobject. It can then be easily plotted usingNetworkX Draw, orPyVis.frombelief_propagationimportTannerGraph,bsc_llrimportnumpyasnpH=np.array([[1,1,1,1,0,0,0,0,0,0],[1,0,0,0,1,1,1,0,0,0],[0,1,0,0,1,0,0,1,1,0],[0,0,1,0,0,1,0,1,0,1],[0,0,0,1,0,0,1,0,1,1]])tg=TannerGraph.from_biadjacency_matrix(H,channel_model=bsc_llr(0.1))g=tg.to_nx()importnetworkxasnximportmatplotlib.pyplotaspltfig=plt.figure()top=nx.bipartite.sets(g)[0]labels={node:d["label"]fornode,ding.nodes(data=True)}nx.draw_networkx(g,with_labels=True,node_color=[d["color"]fording.nodes.values()],pos=nx.bipartite_layout(g,top),labels=labels)fig.show()Belief Propagationfrombelief_propagationimportBeliefPropagation,TannerGraph,bsc_llrimportnumpyasnp# consider a parity check matrixH=np.array([[1,1,1,1,0,0,0,0,0,0],[1,0,0,0,1,1,1,0,0,0],[0,1,0,0,1,0,0,1,1,0],[0,0,1,0,0,1,0,1,0,1],[0,0,0,1,0,0,1,0,1,1]])# Use it to construct a Tanner graph. Assume a BSC channel model with probability p=0.1  ofr bit flipmodel=bsc_llr(0.1)tg=TannerGraph.from_biadjacency_matrix(H,channel_model=model)# let us assume the codeword [1,1,0,0,1,0,0,0,0,0] was sent, but due to a channel error the last bit got flippedc=np.array([1,1,0,0,1,0,0,0,0,1])# consequently we get initially H.dot(c) % 2# array([0, 0, 0, 1, 1])# let us try to correct the errorbp=BeliefPropagation(tg,H,max_iter=10)estimate,llr,decode_success=bp.decode(c)# You can see that the error is corrected