Saravanan666/pypi-org huggingface dataset:
Package: person-extractor
person-extractorWork in Progress: Identify People's Names in TextusageYou initialize a PersonExtractor with a path to a CSV of names with each column a language.
You can create a csv throughWikinames.fromperson_extractorimportPersonExtractortext="Але дістатися на роботу працівникам цих бізнесів, якщо у них немає власного автомобіля або грошей на таксі чи корпоративну розвозку, стане справжньою проблемою, прогнозує політолог Микола Давидюк."extractor=PersonExtractor(data="names.csv")people=extractor.extract(text)extract returns a list of objects:[{'start':336,'end':343,'text':'Давидюк','spellings':{'en':'Davidyuk','uk':'Давидюк'}}]testTo test the package run:python -m unittest person_extractor.testcontactPost an issue athttps://github.com/Mak4Lab/person-extractor/issuesor email the package authors atdaniel@mak4lab.comandvictoria@mak4lab.com
Package: personnamenorm
Person Name NormalisationUnifying person names in different notationsdifferent sources write person names in different notations:Firstname Secondname LastnameLastname, Firstname Secondnamealso extracted are:academic degrees (e.g. 'Dr.', 'Ph.D.')name prefixes (e.g. 'van ter', 'von', 'De')included: german, french, italian, dutchmissing: spanish, portuguesemissing: double Lastnames in SpanishInstallationpipinstallpersonnamenormUsageimportpersonnamenormaspnnnameobj=pnn.namenorm('Dr. Dipl. Firstname Secondname von und zu Lastname')results innameobj.name<dict>{'raw':'Dr. Dipl. Firstname von und zu Lastname','Firstname':['Firstname','Secondname'],'Lastname':['Lastname'],'title':['Dr.','Dipl.'],'prefix':['von und zu']}nameobj.fullname<str>'von und zu Lastname, Firstname Secondname'nameobj.fullname_abbrev<str>'von und zu Lastname, F S'more examples can be found in thisfileon github.Debug-modeby default debug mode is off.activating the debug modenameobj=pnn.namenorm(<str>,True)returns additional information as logging message.used annotation dictionaryannotated input string as list of tuplesLogginglogging is implementedwrites to std-out if logging IS NOT enabled beforewrites to the existing logging handler if other logging IS enabled beforeTestsee folder 'tests' ongithub.pythontest_personnamenorm.py
Package: personnummer
personnummerValidate Swedish personal identity numbers. Version 3+ only supports Python 3.Installationpip install personnummerorpip3 install personnummerExamplesAll examples that usespersonnummer.Personnumer([params]), can be replaced withpersonnummer.parse([params]).Validationfrompersonnummerimportpersonnummerpersonnummer.valid("8507099805")# => Truepersonnummer.valid("198507099805")# => TrueFormatfrompersonnummerimportpersonnummer# Short formatpn=personnummer.Personnummer(8507099805)pn.format()# => '850709-9805'# Long formatpn=personnummer.Personnummer('8507099805')pn.format(True)# => '198507099805'Get DateNew in version 3.2.0frompersonnummerimportpersonnummerpn=personnummer.Personnummer('19121212+1212')pn.get_date()# => datetime.date(1912, 12, 12)Get Agefrompersonnummerimportpersonnummerpn=personnummer.Personnummer("19121212+1212")pn.get_age()# => 106Seepersonnummer/tests/test_personnummer.pyfor more examples.LicenseMIT
Package: persoonallisuus
PersoonallisuusPersonality (Finnish: persoonallisuus) - who am I currently and how should I behave?License: MIT|Country of origin: Switzerland|Export control classification number(ECCN): EAR99Third party dependencies are documented in the folderthird-party.DocumentationUser and developerdocumentation of persoonallisuus.Bug TrackerAny feature requests or bug reports shall go to thetodos of persoonallisuus.Primary Source repositoryThe main source ofpersoonallisuusis on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clones ofpersoonallisuusare:on codeberg- a democratic community-driven, non-profit software development platform operated by Codeberg e.V.at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.SupportPlease kindly submit issues athttps://todo.sr.ht/~sthagen/persoonallisuusor write plain text email to ~sthagen/persoonallisuus@lists.sr.htto submit patches and request support. Thanks.StatusExperimental.Note: The default branch isdefault.
Package: perspect
Perspective is aninteractiveanalytics and data visualization component,
which is especially well-suited forlargeand/orstreamingdatasets. Use it to create user-configurable reports, dashboards, notebooks and
applications, then deploy stand-alone in the browser, or in concert with Python
and/orJupyterlab.FeaturesA fast, memory efficient streaming query engine, written in
C++ and compiled for bothWebAssemblyandPython, with read/write/streaming forApache Arrow, and a high-performance columnar
expression language based onExprTK.A framework-agnostic User Interface packaged as aCustom Element,
powered either in-browser via WebAssembly or virtually via
WebSocket server (Python/Node).AJupyterLabwidget and Python client library, for
interactive data analysis in a notebook, as well asscalableproductionVoilaapplications.ExampleseditablefilefractalmarketraycastingevictionsnypdmagicstreamingcovidmoviessuperstorecitibikeolympicsjupyterlabDocumentationProject SiteUser GuidesJavascript User GuidePython User GuideDeveloper GuideConceptsTableViewExpression ColumnsData BindingAPIPerspective APIPerspective Viewer APIPerspective Python APICommunity / MediaStreaming, cross-sectional data visualization in JupyterLab | Junyuan Tan, JupyterCon 2020Perspective in 3D | Andrew Stein, Open Source in Finance Forum NYC 2022Build an order book simulation with Perspective | Andrew Stein, FINOS Open Source in Fintech Meetup 2021Perspective project case study | FINOS
Package: perspectiveapi
💖 perspectivea strongly typed wrapper for google's perspective api🪴 examplefromperspectiveimportPerspective,Attributefromasyncioimportget_event_loopp=Perspective(key="...")asyncdefmain():s=awaitp.score("your message here",attributes=(Attribute.flirtation,Attribute.sexually_explicit))print(s.flirtation)print(s.sexually_explicit)
Package: perspective-python
Perspective is aninteractiveanalytics and data visualization component,
which is especially well-suited forlargeand/orstreamingdatasets. Use it to create user-configurable reports, dashboards, notebooks and
applications, then deploy stand-alone in the browser, or in concert with Python
and/orJupyterlab.FeaturesA fast, memory efficient streaming query engine, written in
C++ and compiled for bothWebAssemblyandPython, with read/write/streaming forApache Arrow, and a high-performance columnar
expression language based onExprTK.A framework-agnostic User Interface packaged as aCustom Element,
powered either in-browser via WebAssembly or virtually via
WebSocket server (Python/Node).AJupyterLabwidget and Python client library, for
interactive data analysis in a notebook, as well asscalableproductionVoilaapplications.ExampleseditablefilefractalmarketraycastingevictionsnypdmagicstreamingcovidwebcammoviessuperstorecitibikeolympicsjupyterlabDocumentationProject SiteUser GuidesJavascript User GuidePython User GuideDeveloper GuideConceptsTableViewExpression ColumnsData BindingAPIPerspective APIPerspective Viewer APIPerspective Python APICommunity / MediaStreaming, cross-sectional data visualization in JupyterLab | Junyuan Tan, JupyterCon 2020Perspective in 3D | Andrew Stein, Open Source in Finance Forum NYC 2022Build an order book simulation with Perspective | Andrew Stein, FINOS Open Source in Fintech Meetup 2021Perspective project case study | FINOS
Package: perspectives
Perspectives: Pandas-based library for emotion graphing and semantic search with LMsOverviewThe Perspectives library offers an easy way to extract perspectives (emotion events with a speaker, emotion, object, and reason) at scale with itscutting-edge emotion extraction model. It is built on top of the powerful pandas DataFrame functionality, with added support for semantic search. The library introduces several novel methods for text analytics,perfect for dealing with customer feedback, analyzing semantic trends, or profiling entities within a text.Graph generated from extracted perspectivesSemantic search dashboard built on top of pandasMain FeaturesEasily extract perspectives from text: Theget_perspectives()function allows you toextract the speaker's identity, emotions, and the object of these emotions, giving you useful insights about the emotions in your text.Powerful search capabilities: You can perform semantic search on the dataset based onany column or combination of columnsin the dataset (including columns generated from perspective extraction) . The search method leverages the sentence transformer models for semantic search functionality, providing you with outputs that are spot-on.Improved machine learning models: The library efficiently interfaces with specialized modelbart-perspectivesfor extraction and the mpnet-base model for search.Structured emotional outcomes: All outputs are neatly structured in DataFrame format, allowing for easy downstream analysis and visualizations.Installationpip install perspectivesUsagefromperspectivesimportDataFrame# Load DataFramedf=DataFrame(texts=[listofsentences])# Get perspectivesdf.get_perspectives()# Semantic search on any combination of columnsdf.search(speaker='...',emotion='...')# Profiledf.graph(speaker='...')DemoVideo demoColab demo for profilingColab demo for analyzing customer reviewsAbout meI'm a recent grad of Ohio State University where I did an undergraduate thesis on Synthetic Data Augmentation using LLMs. I've worked as an NLP consultant for a couple awesome startups, and now I'm looking for a role with an inspiring company who is as interested in the untapped potential of LMs as I am!Here's my LinkedIn.Contributing and SupportContributions are welcome! Please raise a GitHub issue for any problems you encounter.Buy me a coffee!LicenceThe library is open source, free to use under the MIT license.Please note that this library is still under active development, hence you may see regular updates and improvements. Feel free to contribute!
Package: persty
persty- Minibox and Delaunay Edges AlgorithmsThis package provides an implementation of algorithms for finding theMiniboxandDelaunayedges on a finite set of points in d-dimensional
space withChebyshev distance.InstallationThesetuptools,numpyandscipyPython packages are prerequisites to using
this package.With these installed, theperstypackage can be installed running the following command.>>> pip install perstyNote.To use thepersty.util.make_gudhi_simplex_treefunction, it is necessary to
install thegudhiPython package.>>> conda install -c conda-forge gudhiCompilation.Pre-compiledwheelsare not available for all platforms.So runningpip install perstymight require compiling the C++ code in thepersty.cpp.bindingsubmodule. For this to complete successfully you need some
additional components installed: a C++ compiler,cmakeversion3.11or
greater, and thepybind11Python
package.To obtain these we recommend installingcondafirst. Then
installbothcmakeandpybind11withconda.>>> conda install -c anaconda cmake
>>> conda install -c conda-forge pybind11Windows.After installingconda, run the above commands within anAnaconda prompt. For the C++ compiler installVisual Studio community.Basic usageimportnumpyasnpimportpersty.miniboximportpersty.delaunaynp.random.seed(0)points=np.random.rand(20,2)minibox_edges=persty.minibox.edges(points)delaunay_edges=persty.delaunay.edges(points)Computing Persistent HomologyMinibox and Delaunay edges can be used to compute persistent homology in
homological dimensions zero and one.Theperstypackage provides a wrapper function to generate agudhi.SimplexTree()object that can be used to compute persistence diagrams of Minibox and Alpha flag
filtrations.The following code computes the zero and one dimensional persistence diagrams
of 100 three-dimensional randomly sampled points in the unit cube.importnumpyasnpimportpersty.miniboximportpersty.utilfromscipy.spatial.distanceimportchebyshevnp.random.seed(0)points=np.random.rand(100,3)minibox_edges=persty.minibox.edges(points)simplex_tree=persty.util.make_gudhi_simplex_tree(points,minibox_edges,max_simplex_dim=2,metric=chebyshev)persistence_diagrams=simplex_tree.persistence(homology_coeff_field=2,persistence_dim_max=False)TestsTo check that this package has been installed correctly you can run the tests in
thetest/directory of this repository.Download this repository on you computer by running the following command in
a terminal window.>>> git clone https://github.com/gbeltramo/persty.gitNote.On Windows you can obtaingitby installing thechocolateypackage manager, and running>>> choco install gitIn a terminal window move to theperstydirectory you just downloaded.If you do not have thepytestpackage installed run>>> pip install pytestFinally run>>> pytestor>>> pytest -qThe second option decreases the verbosity of the output of this command.
Package: persuader-technology-automata-api
Automata APIPackagingpython3 -m buildDevelopmentView API end-pointshttp://127.0.0.1:8000/docshttp://127.0.0.1:8000/redocStart Web ServerInstead of usinguvicornwith--reload(as this does not wire-in dependencies as per this projects' conventions)Usesimulations/serveIssuesBeware of the Zombiesps aux | grep defunct
Package: persuader-technology-automata-meta-info
Automata Meta InfoPackagingpython3 -m buildMeta Info in difference "environment" contextsDevelopment obtains meta info from setup.cfg, while in live (deployed) environment obtains info from installed package Meta Info.DevelopmentDevelopment (Deployment) Prerequisitespython3 -m pip install --upgrade pippip3 install virtualenvDevelopment (Deployment)pb(efficient mode) orpython3 -m buildpython3 -m venv /tmp/automata/meta-info-testcd /tmp/automata/meta-info-testpa(efficient mode @ dir) orsource bin/activatepip3 install ~/projects/code/automata-projects/automata-meta-info/dist/*.tar.gzDevelopment (Deployment) Iteratingcd /tmp/automata/meta-info-testpip3 install --upgrade ~/projects/code/automata-projects/automata-meta-info/dist/*.tar.gzDevelopment (Deployment) Cleandeactivaterm -fr /tmp/automata/meta-info-test
Package: persuader-technology-automata-redis
Automata Redisboth for conventional key-value (including hashset) data.Packagingpython3 -m buildLXD ContainerCreate LXD containercd ~/projects/scripts/bash-scripts/lxc/./lxc-create-basic-ubuntu-container.sh automata-all 10.104.71.60 /projects/code/automata-projects/automata-deploylxc.listAdd these aliases tovi ~/bash/bash-profile-aliases/aliases/bash-projects# automata all
alias automata-all.lxc.start="lxc.start-container automata-all"
alias automata-all.lxc.stop="lxc.stop-container automata-all"
alias automata-all.lxc.run-in="lxc.run-in.container automata-all"
alias automata-all.project="cd ~/projects/code/automata-projects/automata-deploy"Remember to runsource ~/.bashrcContainer Infolxc image list images: ubuntu/22.04 amd64Container Manipulationlxc stop automata-alllxc delete automata-allAccessing Containerautomata-all.lxc.run-inRedis (Container)Redis Installsudo apt updatesudo apt install redisRedis Configsudo vi /etc/redis/redis.confChange tobind 10.104.71.60 127.0.0.1(allow second IP for accessing on host by default)sudo systemctl restart redis-serverRedis Port (outside container)nc -zv 10.104.71.60 6379Stop and start the container to ensure redis, has installed correctly.Backup (Redis)CONFIG get dir(inredis-cli) Tells where the dump file is locatedSAVE/var/lib/redis(should bedump.rdb)sudo systemctl status redis-serversudo systemctl stop redis-serversudo cp /var/lib/redis/dump.rdb BACKUP-DIRRestore (Redis)sudo systemctl stop redis-serversudo cp BACKUP-DIR /var/lib/redis/dump.rdbsudo systemctl start redis-server
Package: persyn
persyn: Personality SyndromePersyn makes it easy to integrate cutting-edge machine learning research projects into a single microservices framework. Each project implements a component of a greater personality (language model, long-term memory, visualizations, logic) that is tied together into an interactive group chat interface.The resulting gestalt can be executed on any combination of local compute resources (CPU + GPU) and third-party APIs (OpenAI, Wikipedia, etc.)The name "Personality Syndrome" was chosen by the first proto-Persyn instance itself,https://mas.to/@annathebotMajor features:Chat with any large language model over Slack, Discord, or MastodonMaintain a consistent and arbitrarily long train of thoughtShort-term and long-term memoryKnowledge graph generationOpinionsAuto-wikipediaAuto-summarization of previous conversations over time or as the prompt size grows too longGenerate images on demandIdentify images dropped into the chat with CLIP InterrogatorOptional automatic image prompt enhancement with Prompt ParrotInstallationPersyn in intended to run from inside a python virtualenv.On first launch it will download required models and install necessary packages inside the virtualenv.$ virtualenv --python=python3.8 env
$ . env/bin/activate
(env) $ pip install --upgrade pip # best practice
(env) $ pip install persynThe default install only includes chat support. If you'd also like to generate and post images:(env) $ pip install persyn[all]Image posting now requires Schuyler Erle'shttps://github.com/schuyler/autobus/which must be installed manually (for now).Tmux is also highly recommended, and is required for using the bot launcher.RedisRedis is used for long-term memory and pub/sub. It also requires the following modules:RedisSearchRedisJSONBest to build from scratch. TODO: Instructions needed.Neo4j (optional but recommended)The knowledge graph is kept in Neo4j. Some day this will use RedisGraph instead, but not until it supports first-class updates of existing graphs. Until then, you'll need a separate Neo4j instance.Configure a new botCreate a new config file, usingconfig/example.yamlas a template.Every bot should have a unique guid. Runuuidgenand paste it into your config under id > guid.You will need a language model for prompt completion. OpenAI is currently the most reliable and best tested. Follow one of the URLs in the example to obtain an API key.At least one chat module (Slack, Discord, or Mastodon) should be enabled so you can interact with the bot.Image generation requires several additional components mentioned in the example config. Install docs coming soon.Running a bot(env) $ launch_bot config/my_config.yamlList running bots(env) $ tmux lsTerminate a bot(env) $ kill_bot mybotsession
Package: pertchart
pertchartThe purpose of his application is to automatically generate pert chart from tasks in json file.Installationpip install pertchartUsagefrom pertchart import PertChart

pc = PertChart()
tasks = pc.getInput(path_to_inputfile)
pc.create_pert_chart(pc.calculate_values(tasks))Example Input Data FileThe data file contains task tuples one per line as in the following sample:{
  "T1.1": {
    "Tid": "T1.1",
    "start": 0,
    "duration": 1,
    "end": 0,
    "responsible": "Responsible1",
    "pred": ["START"]
  },
  "T1.2": {
    "Tid": "T1.2",
    "start": 0,
    "duration": 3,
    "end": 0,
    "responsible": "Responsible2",
    "pred": ["T1.1"]
  },
  "T1.3": {
    "Tid": "T1.3",
    "start": 0,
    "duration": 3,
    "end": 0,
    "responsible": "Responsible3",
    "pred": ["T1.1"]
  },
  "T1.4": {
    "Tid": "T1.4",
    "start": 0,
    "duration": 2,
    "end": 0,
    "responsible": "Responsible4",
    "pred": ["T1.2"]
  },
  "T1.5": {
    "Tid": "T1.5",
    "start": 0,
    "duration": 2,
    "end": 0,
    "responsible": "Responsible5",
    "pred": ["T1.3"]
  },
  "T1.6": {
    "Tid": "T1.6",
    "start": 0,
    "duration": 1,
    "end": 0,
    "responsible": "Responsible6",
    "pred": ["T1.4"]
  },
  "T1.7": {
    "Tid": "T1.7",
    "start": 0,
    "duration": 3,
    "end": 0,
    "responsible": "Responsible7",
    "pred": ["START"]
  },
  "T1.8": {
    "Tid": "T1.8",
    "start": 0,
    "duration": 0,
    "end": 0,
    "responsible": "Responsible8",
    "pred": ["T1.5","T1.6","T1.7"]
  },
  "END": {
    "Tid": "END",
    "start": 0,
    "duration": 0,
    "end": 0,
    "responsible": "Responsible",
    "pred": ["T1.8"]
  }
}Exacmple Output PERT chart for th eabove input dataCredits and Referenceshttp://www.graphviz.org/pdf/dotguide.pdfbtree.pyFigure13https://stackoverflow.com/questions/35064304/runtimeerror-make-sure-the-graphviz-executables-are-on-your-systems-path-afthttps://www.programcreek.com/python/example/104476/graphviz.Digraphhttps://graphviz.readthedocs.io/en/stable/examples.html#structs-py
Package: pertdist
PertDistA scipy-like implementation of thePERT distribution.MotivationIn my current job I work a fair amount with the PERT (also known as Beta-PERT) distribution, but there's currently no implementation of this in scipy. To make up the deficency I crafted up my own PERT distribution class, leveraging numpy and scipy to properly flesh out the functionality. The API is heavily modeled after the scipy.stats methods API's.Build StatusTODO: when I figure out how in blazes to add these ;-)InstallationInstallation is straightforward:pip install pertdistCode ExampleUsage is very similar to what you would find in a scipy.stats class as well:from pert import PERT
import seaborn as sns

pert = PERT(10, 190, 200)
sns.kdeplot(pert.rvs(10000))On running this you should see a chart of a heavily low/left skewed distribution (recommended running in Jupyter or Spyder).RoadmapDevelop unit testsEspecially around flexible identification of various data types, eg: accepting DataFrames, Series, lists, etc.Build out the following scipy function analogues:sflogsfppfisfmomententropyfitexpectmedianmeanvarstdstats (implemented, but needs refinement)A version history is locatedhereContributingSince this is my first published project, I'm pretty relaxed about contributions. Feel free to send me a pull request with any updates/changes/etc you have in mind!Note that I do followVincent Driessen's Git Branching Modelrather rigorously. If you do contribute, it'll most likely be pulled into thedevelopbranch.Also, I'm rather fond ofSemantic Commit Messages, but I'm only picky about those for my own contributions, feel free to use wahtever commit message style you'd like.LicenseThis project uses theGNU General Public License.Short version: Have fun and use it for whatever, just make sure to attribute me for it (-: