Saravanan666/pypi-org huggingface dataset:
Package: bnrml-usaddress
Parse and normalize unstructured US address strings!Project Featuresbnrml_usaddressa starterClickcommand-line
applicationautomated unit tests you can run withpytestaSphinxdocumentation
projectGetting StartedThe project’s documentation contains a section to help youget
startedas a developer or user of the library.Development PrerequisitesIf you’re going to be working in the code (rather than just using the
library), you’ll want a few utilities.GNU MakePandocResourcesBelow are some handy resource links.Project DocumentationClickis a Python package for
creating beautiful command line interfaces in a composable way with
as little code as necessary.Sphinxis a tool that
makes it easy to create intelligent and beautiful documentation,
written by Geog Brandl and licnsed under the BSD license.pytesthelps you write
better programs.[GNU Make] is a tool which controls the generation of executables and
other non-source files of a program from the program’s source files.AuthorsPat Daburu-Initial work-githubSee also the list ofcontributorswho participated in this project.LicenseMIT LicenseCopyright (c) patdaburuPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: bns-utils
BNS UTILSDocumentation goes here.Data ModuleIP21The IP21 class provides a Python interface for interacting with an IP.21 historian database. It uses the pyodbc and pandas libraries to connect to the database and execute SQL queries.InitializationTo use the IP21 Python Interface, you first need to initialize an instance of the IP21 class. The IP21 constructor takes the following arguments:host: The IP address or hostname of the IP21 serverport: The port number to use when connecting to the IP21 serverads: The name of the IP21 Advanced Data Server (ADS) to connect to (default: MINPRO)frombns_utils.dataimportIP21# Create an IP21 object to connect to the historian databaseip21=IP21(host="127.0.0.1",port=1234,ads="MINPRO")Retrieving Tag Names and DefinitionsThe tag_dump method can be used to retrieve a list of all IP.21 tags and their descriptions from the historian database. By default, this method saves the list to a CSV file named tag_dump.csv, but this behavior can be disabled by passing write_csv=False. For example:# Retrieve a list of all tags and save it to a CSV fileip21.tag_dump()# Retrieve a list of all tags without saving it to a CSV filetag_list=ip21.tag_dump(write_csv=False)Retrieving Tag DataYou can retrieve historical data for a list of tags using the tag_data method. This method takes the following arguments:tag_list: A list of tag names to retrieve data forstart: The start time for the data in the format "DD-Mon-YY HH:MM:SS"end: The end time for the data in the format "DD-Mon-YY HH:MM:SS"freq: The interval to retrieve data at in the format "HH:MM:SS"separate: A boolean indicating whether to save the data for each tag separately (default: True)file_type: The file format to use when saving the data (either ftr or csv, default: ftr)Here is an example of how to retrieve data for a single tag:ip21.tag_data(tag_list=["MyTag"],start="01-Jan-23 00:00:00",end="02-Jan-23 00:00:00",freq="00:01:00")This will retrieve data for the tag "MyTag" between January 1st, 2023 and January 2nd, 2023, at one minute intervals. The data will be saved to a file named MyTag.ftr in Feather format by default.If you want to retrieve data for multiple tags, simply pass a list of tag names to the tag_list argument:ip21.tag_data(tag_list=["Tag1","Tag2","Tag3"],start="01-Jan-23 00:00:00",end="02-Jan-23 00:00:00",freq="00:01:00")This will retrieve data for the tags "Tag1", "Tag2", and "Tag3" between January 1st, 2023 and January 2nd, 2023, at one minute intervals. The data for each tag will be saved to separate files by default.
Package: bn-testing
BN testingA test framework to evaluate methods that learn Bayesian Networks from
high-dimensional observational data.SamplingSet up the graphical model and sample  datafrombn_testing.modelsimportBayesianNetworkfrombn_testing.dagsimportErdosRenyfrombn_testing.conditionalsimportPolynomialConditionalmodel=BayesianNetwork(dag=ErdosReny(p=0.01,n_nodes=100),conditionals=PolynomialConditional(max_terms=5))df=model.sample(10000,normalize=True)The observations are stored in apandas.DataFramewhere the columns
are the nodes of the DAG and each row is an observation. The
underlying DAG of the graphical model can be accessed withmodel.dag
Package: bntrans
BanglaTranslationKitBanglaTranslationKit is a open-source translation package for offline conversion between both Bengali and English languages (English to Bangla and Bangla to English)InstallationpipinstallbntransUsageYou can use this package to translate any Bangla to English or English to Bangla.frombntransimportTranslatortranslator=Translator(src="en",dest="bn")translation=translator.translate("Hello world!")#  ্যালো বিশ্ব!print(translation)Some Useful MethodsfrombntransimportTranslator# You can tell the model to use cputranslator=Translator(src="en",dest="bn",use_gpu=False)# Specify a custom model (from huggingface.co/models or local path)translator=Translator(model="mymodel")# If custom model doesn't have a maximum length, you can specify it. It is better to specify it as some models have wrong maximum length in their config.translator=Translator(model="mymodel",max_length=512)# You can also use `generate` method to get raw output from the modeltranslator.generate("Hello world!")Use Cloud Translation API (Free)If you want to test the model without installing it, you can use the cloud translation API. Get a free Token fromhere(Read Token is enough) and use it like this:frombntransimportTranslatortranslator=Translator(src="en",dest="bn",use_cloud=True,huggingface_token="YOUR_TOKEN")translation=translator.translate("Hello world!")#  ্যালো বিশ্ব!print(translation)Use in Command LineYou can also use this package in command line. Just install it and run the following command:bntrans"Hello world!"usage:cli.py[-h][-sSRC][-dDEST][-mMODEL][-gGPU][-lMAX_LENGTH][-cUSE_CLOUD][-tHUGGINGFACE_TOKEN]text

Translatetextfromonelanguagetoanother.

positionalarguments:textTexttotranslate

options:-h,--helpshowthishelpmessageandexit-sSRC,--srcSRCSourcelanguage(default:en)-dDEST,--destDESTDestinationlanguage(default:bn)-mMODEL,--modelMODELModeltousefortranslation-gGPU,--gpuGPUWhethertouseGPUfortranslation(default:True)-lMAX_LENGTH,--max-lengthMAX_LENGTHMaximumlengthofthemodel(default:None)-cUSE_CLOUD,--use-cloudUSE_CLOUDWhethertousehuggingfaceinferenceAPI(default:False)-tHUGGINGFACE_TOKEN,--huggingface-tokenHUGGINGFACE_TOKENHuggingfaceinferenceAPItoken(default:None)
Package: bntranslit
BNTRANSLITBNTRANSLITis a deep learning based transliteration app for Bangla word.Installationpip install bntranslitDependencypytorch 1.7.0 or 1.7.0+NB: NoGPUNeeded. TotallyCPUbasedPre-trained ModelDownload bntranslit_modelUsagefrombntranslitimportBNTransliterationmodel_path="bntranslit_model.pth"bntrans=BNTransliteration(model_path)word="aami"output=bntrans.predict(word,topk=10)# output: ['আমি', 'আমী', 'অ্যামি', 'আমিই', 'এমি', 'আমির', 'আমিদ', 'আমই', 'আমে', 'আমিতে']Datasets and Training DetailsWe usedGoogle Dakshina DatasetThanks toAI4Bharatfor providing training notebook with details explanationWe trained Google Bangla Dakshina lexicons train datasets for 10 epochs with batch size 128, 1e-3, embedding dim = 300, hidden dim = 512, lstm, used attentionWe evaluated our trained model with Google Bangla Dakshina lexicon test data usingAI4Bharat evaluation scriptand our evaluation results insidesdocs/evaluation_summary.txt
Package: bnum
Bounded numbers were first conceptualized by Chris Crawford, and
introduced in his book,Chris Crawford on Interactive Storytelling.The basic idea of bounded numbers is to force the real number range
into the bounded range of-1.0< b < 1.0, with limits at -1.0 and 1.0,
with the whole range observing a bell curve distribution.A real number can be converted to a bounded number like so:def _bind(unbounded_number: Union[float, int]) -> float:
    """Transform an unbounded number into an bounded number."""
    if unbounded_number > 0.0:
        return 1.0 - (1.0 / (1.0 + unbounded_number))
    else:
        return (1.0 / (1.0 - unbounded_number)) - 1.0A bounded number may be transformed back to an unbounded number (with
rounding errors) like so:def _unbind(bounded_number: float) -> float:
  """Transform a bounded number into an unbounded number."""
  if bounded_number > 0.0:
      return (1.0 / (1.0 - bounded_number)) - 1.0
  else:
      return 1.0 - (1.0 / (1.0 + bounded_number))Note that in the world of bounded numbers, from ten on up, the number
of places beyond 1roughlycorresponds to the number of nines.  That
is:10 ~= 0.9100 ~= 0.991000 ~= 0.999etc.Note also that the journey from unbounded to bounded will result in
rounding errors.  The larger the unbounded number, the larger the
round-trip deviation.InstantiatingUse thebnum(x)constructor (or its alias,b(x)) to make a
bounded number from a float in the range-1.0< x < 1.0.Usebind(x)to bind an arbitrary real number.Blending, Amplifying, SuppressingBounded numbers may be manipulated using the uniqueblend()function, which combines two bounded numbers with an optional
weight. Without a weight,blend(x, y)finds the midpoint betweenxandy. A non-zero weight pushes the midpoint up or down
accordingly.Thebnumtype also has three blending shortcuts:x.blend(y, weight=0.0): equivalent ofblend(x, y, weight).x.amplify(weight=0.0): scalexaway from 0.x.suppress(weight=0.0): scalextowards 0.
Package: bnw-tools
BorgNetzWerk / toolsWelcome to the BorgNetzWerk tool project! This repository contains all tools in ideation, development and usage. Use, experiment and contribute for free!Ready to useSWARM-SLR, a Systematic Literature Review (SLR) workflowContributingIf you're interested in contributing to the BorgNetzWerk project, we'd love to have you! You can start by browsing through the open issues and picking one that you're interested in working on. If you have an idea for a new feature or a bug that you'd like to report, please open a new issue so we can discuss it. Once you're ready to start working on an issue, you can create a fork of the repository and submit a pull request when you're finished.IDEASThis section is dedicated to listing and discussing ideas for new features and improvements for the BorgNetzWerk project.Work in Progress: These are ideas that are currently being developed or tested and are not yet ready to be implemented as submodules.Suggestions: These are ideas that have been suggested by the community or other contributors and are being considered for implementation.Feature Requests: These are specific features that have been requested by users and are being considered for implementation.Read text from screen: a submodule that is able to extract text present in the video, not the audio trackProcess images: a submodule that is able to compare videos that look similar, use similar images or stylesIf you have an idea for a new feature or improvement, please feel free to open an issue in the repository to discuss it.Questions or FeedbackIf you have any questions or feedback about the BorgNetzWerk project, please don't hesitate to reach out to us through through the issues section or the following channels:Contact Form:https://borgnetzwerk.de/kontakt/Discord:https://discord.com/invite/gATFDczS29YouTube:https://www.youtube.com/@BorgNetzWerkTwitch:https://www.twitch.tv/borgnetzwerk
Package: bo
Bo is a chatbot written in Python. It can run either as a standalone program or easily be embedded in your own program.Bo is distributed under the BSD licence, checkLICENSEfor further information.Getting your very own copy of BoThe easiest way to get Bo running is deploying it onHeroku:$gitinitbo-heroku$cdbo-heroku/$herokucreate--stackcedar$wget"https://bitbucket.org/konikos/bo/raw/tip/requirements.txt"$echo"-e hg+http://bitbucket.org/konikos/bo#egg=bo">>requirements.txt$echo"worker: bo -c config.yaml">Procfile$wget-Oconfig.yaml"https://bitbucket.org/konikos/bo/raw/tip/conf/xmpp.yaml"Edit the createdconfig.yamlto set your password, possibly add extra plugins, and execute the following to get Bo running:$gitcommit-a-m'init'$gitpushherokumaster$herokups:scaleworker=1If you are having issues running Bo, useheroku logsto see the output from Bo andheroku restartto restart it.PluginsPlugins add extra functionality to Bo. The provided plugins can be found inbo/dist/plugins.
Package: bo4e
🇩🇪 Dies ist eine BO4E Referenz-Implementierung in Python.
Gleichzeitig ist dieses Repository der Ort, um Fragen und Erweiterungen des BO4E-Standards zu diskutieren.🇬🇧 This is a Python library that implements Business Objects for EnergyBO4E.Grundsätzliche ÜberlegungenPflichtfelder (nicht nullable Attributes)Der BO4E Standard soll (in Zukunft, Stand 2023-10-05) keine fachlichen Pflichtfelder mehr enthalten.
Die Entscheidung, was in welchen Fällen ein Pflichtfeld ist, obliegt den Anwendungen, die den Standard nutzen.
Entsprechend ist auch die Validierung der Pflichtfelder nicht Teil des BO4E Standards sondern anwendungsspezifisch.
Davon sind auch technische Pflichtfelder, namentlich_typund_versionnicht ausgenommen.
Ob sie anzugeben sind, entscheidet die Anwendung.
BO4E gibt sinnvolle Datenstrukturen vor, wie diese in der Praxis genutzt werden können und wollen wir aber nicht vorschreiben.Verweise zwischen ObjektenViele Objekte verweisen aufeinander und sind miteinander verknüpft.
Dabei sind unsere Designentscheidungen:Alle Verweise sind optional.Verweise sind, wo sie naheliegend sind, im BO4E Standard vordefiniert (z.B. 1 Messlokation hat n Zähler).Aber wir haben nicht jede theoretisch denkbare Verweise implementiert (z.B. allein dass User Zähler unter Angabe einer Email-Adresse suchen können, heißt nicht, dass der Zähler eine optionale EigenschaftemailAdressehaben muss.)Generell sollen Verweise zwischen zwei BOs bi-direktional sein, zwischen BOs und COMs aber nur unidirektional (z.B. soll jeder Zähler wissen zu welcher Messlokation er gehört aber eine Adresse muss nicht wissen, welchem Geschäftspartner, welcher Messlokation oder welcher Rechnung sie zugeordnet ist).COMs können zwar weitere COMs beinhalten, jedoch sollte dies nicht dafür genutzt werden von einem COM eines BOs auf das COM eines anderen BOs zu verweisen.
Bsp.: Die Adresse in Ansprechpartner ist identisch zur Lokationsadresse in der Marktlokation. Dann sollen beide Adressen als vollständiges COM dargestellt werden, statt nur als Verweis von einer Adresse auf die andere.Oder anders formuliert: wir können aus einem BO oder einem COM auf ein anderes BO verweisen.Dokumentation / Fragen und Anregungen zum BO4E DatenmodellEine Dokumentation des Datenmodells und JSON Schemata zur Erzeugung von Beispieldaten finden sich aufread the docs.Bei Fragen oder Anregungen, bitteeinfach ein Issue in diesem Repo aufmachen.Code BeiträgeÄnderungsvorschläge (sowohl an das Datenmodell als auch an die Implementierung in Python) können direkt als Code in Form von Pull Requests eingereicht werden.
Details dazu finden sich imContribution Guide.Nutzung als Python LibraryIn Python kann diese Library als Paket installiert werden:pip install bo4eAndere nennenswerte BO4E ImplementierungenC#/.NETGolangKotlinTypeScript (handcrafted)TypeScript (autogenerated, inherently consistent with the .NET library)PHP(undSchemas)
Package: boa-api
About Boa Python Client APIThe Boa Python Client API provides programmatic access to the Boa language and infrastructure from Python.About BoaFor more information about Boa, please see the main website:https://boa.cs.iastate.edu/Creating a clientThe main entry point for the API is aBoaClientobject.  You use this to log in, submit queries, find datasets, log out, etc.  To instantiate this object, you must provide the API endpoint's URL.  The API has several constants for common endpoints:BOA_API_ENDPOINT- for the Boa MSR endpointBOAC_API_ENDPOINT- for the Boa CORD-19 endpointFor example if you want a client for the CORD-19 endpoint, you do the following:client = BoaClient(endpoint=BOAC_API_ENDPOINT)If you don't specify an endpoint, it will default to the MSR endpoint.Example Use (using MSR endpoint)importgetpassimporttimefromboaapi.boa_clientimportBoaClient,BOA_API_ENDPOINTfromboaapi.statusimportCompilerStatus,ExecutionStatusclient=BoaClient(endpoint=BOA_API_ENDPOINT)user=input("Username [%s]: "%getpass.getuser())ifnotuser:user=getpass.getuser()client.login(user,getpass.getpass())print('successfully logged in to Boa API')query="""# Counting the 10 most used programming languagesp: Project = input;counts: output top(10) of string weight int;foreach (i: int; def(p.programming_languages[i]))counts << p.programming_languages[i] weight 1;"""# query using a specific datasetjob=client.query(query,client.get_dataset('2019 October/GitHub'))print('query submitted')whilejob.is_running():job.refresh()print('job '+str(job.id)+' still running, waiting 10s...')time.sleep(10)ifjob.compiler_statusisCompilerStatus.ERROR:print('job '+str(job.id)+' had compile error')elifjob.exec_statusisExecutionStatus.ERROR:print('job '+str(job.id)+' had exec error')else:try:print('output:')print(job.output())except:passclient.close()print('client closed')
Package: boaconstrictor
BoaConstrictorSimplicity rocks!Why!?!?:To make a New and Improved coding language.
You see, whenever I look up Python on Reddit, people are always saying
that they wish there was a language which combines the simple syntax of
Python with the simple grammar of C++. Intruiged by the idea, I did a some
looking around to see if such a language exists. Unfortunately, however,
I quickly found that are no such languages. As a result, I decided to make a
new programming language, which would combine these features. In
addition, I wanted to still be able to use ANY python module in your code. So,
after several months of work, here is the end product: BoaConstrictor. In
addition, you can use ANY python module in your code.Installation using PIP:pip install boaconstrictorUsage:C:\Users\foo> boaconstrictorYou will then be prompted to enter a filename. This must be a valid path to
a *.boa file. Enter a file-path, and then press <>.Example code:usingtimefunctionmain(){cout"Hello, world!"x=3.5}If you have any questions, contact me via email. My address isthescribe11@gmail.com.CHANGELOG:V 1.2.2: Fixed escape characters.V 1.2.3: Fixed problems with the Print and Input functions.V 1.2.4: Fixed the output filename problem with V 1.2.3.V 1.2.9: Addedendkeyword, fixed the glitch which was causing always to output to 'output.exe'.V 1.2.10: Fixedendkeyword implementation problem.V 1.2.11: Got everything up and working, releasing to PyPI.V 1.3.3: Reworked compilation algorithm to be faster, fixed 'cin' statement, changed command-line operation.
Package: boaconstructor
The boacontructor is a templating library fordata.It allows you to construct Python dictionaries from other templates,
dictionaries or instances.Source code is available on github:https://github.com/oisinmulvihill/boaconstructorDocumentation is available here:http://packages.python.org/boaconstructorAn ExampleI’ll let the code and comments do the talking:from boaconstructor import Template

# Some shared information in a dict. This could also be a class instance
# or something else that supports getattr and hasattr.
#
common = dict(
    timeout = 30,
    email = "admin@example.com"
)

# This is a template created in a module. You need one of these. I pass in
# references that are available at this stage. The 'host.$.name' I will pass
# in at render time.
#
webserver_data = Template('webserver',
    dict(
        interface = 'host.$.name',
        port = 32189,
        timeout = 'common.$.timeout',
        alert_email = 'common.$.email',
    ),
    # This is uses common as an 'internal' reference
    references={'common':common}
)

# At run time I can pass 'external' references to resolve the hostnames.
# Maybe I got these from a database or some other source.
#
machine_1 = webserver_data.render({'host': {'name': 'myserver1'}}),
# {'alert_email': 'admin@example.com', 'interface': 'myserver1', 'port': 32189, 'timeout': 30}

machine_2 = webserver_data.render({'host': {'name': 'myserver2'}}),
# {'alert_email': 'admin@example.com', 'interface': 'myserver2', 'port': 32189, 'timeout': 30}


# Now I can pass these to Cheetah/Mako/etc to render a specific type of
# XML/INI/Text configuration files.
Package: boa-contrast
BOA::ContrastPackage to compute contrast information from a CT image, part of theBOA(soon online!). The package uses the open-source softwareTotalSegmentatorto compute segmentations of important anatomical landmarks, which are then used to create features for a machine learning model to predict the contrast information.Installpipinstallboa-contrastwill install only the basic package (without the TotalSegmentator), if you also want to install the TotalSegmentator you canpipinstall"boa-contrast[totalsegmentator]"However, the TotalSegmentator can also be used together with docker, and in such case it is not needed to install it.Command Lineconstrast-recognition --helpOnce a CT and a folder where to store the TotalSegmentator segmentations is given, you can run it using the following commandcontrast-recognition [-h] --ct-path CT_PATH --segmentation-folder SEGMENTATION_FOLDER [--docker] [--user-id USER_ID] [--device-id DEVICE_ID] [-v]You can run it using docker by using the--dockerflag. If you are using docker, you need to specify your user ID using the--user-idflag, otherwise you will have to change the ownership of the segmentations afterwards.If you are using a GPU, you can specify the device ID using the--device-idflag.You can enable verbosity with the-vflag.To not download the TotalSegmentator weights all the time, you can specify their location using theTOTALSEG_WEIGHTS_PATHenvironment variable.A sample output looks as follows:IV Phase: NON_CONTRAST
Contrast in GIT: NO_CONTRAST_IN_GI_TRACTFrom PythonCompute the segmentation with the TotalSegmentator with dockerfromboa_contrastimportcompute_segmentationcompute_segmentation(ct_path=...,# The path to the CTsegmentation_folder=...,# The root where the segmentation should be storeddevice_id=...,# The ID of the GPU device or -1user_id=...,# Your user ID for docker to run in user modecompute_with_docker=False,# Whether to use docker or not)Once the segmentation is computedfromboa_contrastimportpredictpredict(ct_path=...,# path to the CTsegmentation_folder=...,# path to this CT's segmentation)Output:{
    "phase_ensemble_prediction": 0,
    "phase_ensemble_predicted_class": "NON_CONTRAST",
    "phase_ensemble_probas": array(
        [
            9.89733540e-01,
            3.60637282e-04,
            4.79974664e-04,
            5.55973168e-04,
            8.86987492e-03,
        ]
    ),
    "git_ensemble_prediction": 0,
    "git_ensemble_predicted_class": "NO_CONTRAST_IN_GI_TRACT",
    "git_ensemble_probas": array(
        [
            9.99951577e-01,
            4.84187825e-05,
        ]
    ),
}