Saravanan666/pypi-org huggingface dataset:
Package: dsbapipy
DSBApiEine API für die DSBMobile Vertretungsplan-Lösung, welche viele Schulen benutzen.Python 3Funktioniert Stand 02.10.2020 (Jetzt via Android-API (nach Problemen in 0.0.3), seit 2015 stableAktuell in Version 0.0.13Aktuell stableUnits 2020 nicht vollständig unterstützt, PRs welcome, aber Kompatibilität berücksichtigen!Installation:pip3 install dsbapipyoder manuell vom Source Code.Datensatz:JSON Liste an Arrays. Ein Array sieht so aus:KeyValueNotiztypeVertretungArt des Eintragsclass5DKlasselesson12SchulstunderoomR404(Neuer) Raumnew_subjectM-GK1Neuer KurssubjectIF-LK4Ursprüngliches Fach / Kursnew_teacherNEUMNeuer LehrerteacherBIMMUrsprünglicher Lehrerdate01.01.2019DatumdayMontagWochentagupdated02.02.2019 12:13Letztes UpdateImplementierung:Beispiel 1importdsbapidsbclient=dsbapi.DSBApi("username","password")entries=dsbclient.fetch_entries()# Rückgabe einer JSON Liste an Arraysprint(entries[0]["date"])# Datum des ersten EintragsBeispiel 2: Anderes TabellenformatSchulen sind relativ frei in der Gestaltung Ihrer Datensätze. Daher kann der oben beschriebene Standard wiefolgt überschrieben werden:importdsbapiownFields=['class','lesson','new_subject','room','subject','new_teacher','type','text']dsbclient=dsbapi.DSBApi("username","password",tablemapper=ownFields)entries=dsbclient.fetch_entries()# Rückgabe einer JSON Liste an Arraysprint(entries[0]["date"])# Datum des ersten Eintrags
Package: dsbench
DSBenchThis small library provides a decorator function that benchmarks functions.dsbench.benchmark(*, name: str, cumulative: bool = False, range_start: int = 0, range_end: int = None) -> CallableDecorator function for benchmarking other functions.Parameters:name(str) – The name of the benchmark.cumulative(bool,optionals) – Whether to benchmark a function a number of times with different inputs. If the decorated function returns a result, the sum of the total results is displayed. Defaults to False.range_start(int,optional) – The start value of the range for cumulative benchmarking. Defaults to 0.range_end(int,optional) – The end value of the range for cumulative benchmarking. Defaults to the decorated function’s first argument.Returns:The decorated function.Return type:function
Package: dsbox-corex
Corex featurization and modeling primitives for D3M envrionmentsFeaturization:
(featurization with information regularization for continuous data (linear Gaussian model) and text (similar to LDA))
Corex continuous
Corex textRegression:
(echo provides mutual information regularization: linear regression and neural network modeling primitives provided)
EchoLinear
EchoRegression
EchoClassification
Package: dscal
dscal 3.2.5A library for all sort of calculus problems and graphing solutions.Get instant results with fewer lines of code and more readability.For any query contactzarexdhruv@gmail.comor get help fromhttps://sites.google.com/view/dscal3/homeChange logLatest release3.2.5 (24/07/2023)Get faster and smoother results than ever before.
Package: dscamera
Double Sphere Camera ModelUnofficial Python library of Double Sphere Camera Model for fisheye cameras.Reference:V. Usenko, N. Demmel, and D. Cremers, "The Double Sphere Camera Model", Proc. of the Int. Conference on 3D Vision (3DV), 2018.RequirementsPython >= 3.6 with numpy and opencv.InstallationRun the following command.gitclonehttps://github.com/matsuren/dscameracddscamera
pythonsetup.pyinstallCamera calibrationPlease useBasaltfor fisheye camera calibration. The detail instruction is availablehere.ExamplePlease checkexamplefolder for fisheye image rectifications.Input fisheye image:Output perspective image:Output equirectangular image:
Package: dscan
dscandscan makes scanning a list of domains for DNS records easy!InstallationSimply run:$ pip install dscanCommand Referenceusage: dscan [-h] [--input INPUT] [--output OUTPUT] [--rrtype RRTYPE]
			[--single]
			[--provider {adguard,appliedprivacy,cleanbrowsing-family,cloudflare,commons.host,dnswarden,dnswarden-adblock,google,powerdns,quad9}]
			[--verbose]
			[domain]

positional arguments:
domain                domain name (single mode only)

optional arguments:
-h, --help            show this help message and exit
--input INPUT, -i INPUT
						path to list of domains (in plain text)
--output OUTPUT, -o OUTPUT
						path to output
--rrtype RRTYPE, -r RRTYPE
						rrtype to collate
--single, -s          single domain mode
--provider {adguard,appliedprivacy,cleanbrowsing-family,cloudflare,commons.host,dnswarden,dnswarden-adblock,google,powerdns,quad9}, -p {adguard,appliedprivacy,cleanbrowsing-family,cloudflare,commons.host,dnswarden,dnswarden-adblock,google,powerdns,quad9}
						dns provider to use
--verbose, -v         increase output verbosityLicenseThis project has been developed by Matthew Gall. This project is hereby released under the terms of the MIT License, and is included belowMIT License

Copyright (c) 2019 Matthew Gall

Permission 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.SupportHave a question? Need assistance? Don't stay stuck! If you do use this project or have any feedback we would love to hear from you, drop me ane-mail
Package: dscan-4E656F
Distributed Nmap Scanner.The main goal of this project is to provide a wrapper around nmap, and
distribute scans across several hosts.PIP package published atpip3 urlDocumentationDemo Video.Install%gitclonehttps://github.com/0x4E0x650x6F/dscan.git
%cddscan
%pipinstall.
%dscan[*]\tDistribuitedscan
usage:Distributedscanner[-h]--nameNAME{srv,agent,config}...
Distributedscanner:error:thefollowingargumentsarerequired:--name,cmdUninstall%pipuninstalldscanGenerate self-sign certificate.The following command generates a self sign certificate a private key, and two
configuration files agent.conf and dscan.conf with default settings for the
agent and for the server.dscan--name<project-name>config-emailmail@dscan.org-cndscan\-cpt-learth-stnrw-odscan-ouit-days365ls-afubar
-agent.conf-certfile.crt-dscan.conf-keyfile.keyServer output exampleThe following command starts the server, the --name is the name of the
folder where the project files will be stored, this directory should
contain the dscan.conf and the certificate private key generated from the
previous command, the last argument is a existing file with a list of
ip or networks to scan.%dscan--nameprojectnamesrv--configdscan.conftargets.txtDistributedScanStatus========================-------------------------------------NStagesNPendingTasksCompletion%-------------------------------------400.00%------------------------------------------StageNTargetsNFinishedCompletion%------------------------------------------discovery100.00%-----------------------------------------------AgentStageTaskStatusTargetIp-----------------------------------------------127.0.0.1:53281discoveryDOWNLOADING127.0.0.1/32Agent output exampleThe following starts the agent, the --name is the name of the folder were
the work files will be stores should contain a copy of the certificate, and
the agent.conf file.dscan--name<agent-project-name>agent--config<path-to-agent.conf>\-s<server-ip-address>-p<server-port>

DistributedScanStatus========================------------------------------------
TargetNªcompletedScansStatus
------------------------------------127.0.0.1/325100BTC: bc1qhzp6d5rtczyffwfaqxlca8cfqhc5m0xzarjy6ve8ujmxmgecmpeq08vppv
Package: ds-caselaw-marklogic-api-client
The National Archives: Find Case LawThis repository is part of theFind Case Lawproject atThe National Archives. For more information on the project, checkthe documentation.MarkLogic API ClientThis is an API Client for connecting to Marklogic for The National Archive's Caselaw site.This package is published on PyPI:https://pypi.org/project/ds-caselaw-marklogic-api-client/UsageYou can find documentation of the client class and available methodshere.TestingTo run the test suite:poetryinstall
poetryrunpytestThere are also some smoketests insmoketests.pywhich run against a MarkLogic database but do not run in CI currently.To run them locally you can set the environment variables as detailed in the file in a.envfile or just hardcode them in, as long as you don't commit those changes to the repo.And then runpoetryrunpytestsmoketest.pyTo start with when running this, we have been choosing to point to the staging MarkLogic to have more confidence that the setup is a good representation of production as opposed to a local MarkLogic instance but that can work too.Eventually we will make it so that we run these tests in CI and probably point to a dedicated testing MarkLogic instance so we don't get conflicts with people using staging for manual testing.Making changesWhen making a change, update thechangelogusing theKeep a Changelog 1.0.0format. Pull
requests should not be merged before any relevant updates are made.ReleasingWhen making a new release, update thechangelogin the release
pull request.The package willonlybe released to PyPI if the branch is tagged. A merge
to main alone willnottrigger a release to PyPI.To create a release:Update the version number inpyproject.tomlCreate a branchrelease/v{major}.{minor}.{patch}UpdateCHANGELOG.mdfor the releaseCommit and pushOpen a PR from that branch to mainGet approval on the PRMerge the PR to main and pushTag the merge commit onmainwithv{major}.{minor}.{patch}and push the tagCreate a release inGithub releasesusing the created tag
Package: dschema
About dschemadschema is a small library for validating the content of python dictionary objects against a schema.The schema can be defined in code or entirely as text (parsed from json generally)dschema was mainly made for validating config files written in JSON.ExampleSee documentation link for more examplesimportreimportphonenumbersimportdschema# https://github.com/daviddrysdale/python-phonenumbers# pip install phonenumbersdefphone_type(number):# Exceptions are validation errors# Very similar design to the "argparse" modulereturnphonenumbers.parse(number)defssn_type(ssn):ifre.match('^\d{3}-?\d{2}-?\d{4}$',ssn):returnssnelse:raiseValueError('"{}" is not a valid SSN.')schema={'person':{'first_name':dschema.prop(required=True),'last_name':dschema.prop(required=True),'phone':dschema.prop(required=True,type=phone_type),'ssn':dschema.prop(required=True,type='ssn_type'),dschema.Required:True# "person" namespace is required, you must specify# even if "person" itself contains required properties},# Allow a raw dictionary value to pass through'other_info':dschema.prop(default=dict(),dict=True),# default to False if not present'subscribed':dschema.prop(default=False,type=bool)}validator=dschema.Validator(schema)# you can use this to add types that are recognized by name.# which is useful if you want your schema to be entirely textualvalidator.add_type('ssn_type',ssn_type)# you will need to define default types on your own# if you want to reference them by name# validator.add_type('int', int)data={'person':{'first_name':"John",'last_name':"Smith",'phone':'+1 234 5678 9000','ssn':'123-45-6789'},'other_info':{'website':'www.johnsmith.com',}}# If namespace is left False, a plain dictionary is returnedresult=validator.validate(data,namespace=True)print(result)# Prints: (un-indented)# Namespace(#     person=Namespace(#         first_name='John',#         last_name='Smith',#         phone=PhoneNumber(...),#         ssn='123-45-6789'),#     other_info={'website': 'www.johnsmith.com'},#     subscribed=False# )# Each Namespace is just a dynamic objectprint(result.person.first_name)# -> Johnprint(result.person.last_name)# -> Smithprint(result.person.phone)# - > Country Code: 1 National Number: 23456789000print(result.person.ssn)# -> 123-45-6789print(result.other_info)# -> {'website': 'www.johnsmith.com'}print(result.subscribed)# -> False (default)
Package: dschmidt-cdktf-provider-google
Terraform CDK google Provider ~> 4.0This repo builds and publishes the Terraform google Provider bindings forCDK for Terraform.Available PackagesNPMThe npm package is available athttps://www.npmjs.com/package/@dschmidt/provider-google.npm install @dschmidt/provider-googlePyPIThe PyPI package is available athttps://pypi.org/project/dschmidt-cdktf-provider-google.pipenv install dschmidt-cdktf-provider-googleNugetThe Nuget package is available athttps://www.nuget.org/packages/DSchmidt.Dschmidt.Providers.Google.dotnet add package DSchmidt.Dschmidt.Providers.GoogleMavenThe Maven package is available athttps://mvnrepository.com/artifact/com.danielmschmidt/dschmidt-provider-google.<dependency>
    <groupId>com.danielmschmidt</groupId>
    <artifactId>dschmidt-provider-google</artifactId>
    <version>[REPLACE WITH DESIRED VERSION]</version>
</dependency>GoThe go package is generated into thegithub.com/hashicorp/cdktf-provider-google-gopackage.go get github.com/hashicorp/cdktf-provider-google-go/googleDocsFind auto-generated docs for this provider here:./API.mdYou can also visit a hosted version of the documentation onconstructs.dev.VersioningThis project is explicitly not tracking the Terraform google Provider version 1:1. In fact, it always trackslatestof~> 4.0with every release. If there are scenarios where you explicitly have to pin your provider version, you can do so by generating theprovider constructs manually.These are the upstream dependencies:Terraform CDKTerraform google ProviderTerraform EngineIf there are breaking changes (backward incompatible) in any of the above, the major version of this project will be bumped.Features / Issues / BugsPlease report bugs and issues to theterraform cdkproject:Create bug reportCreate feature requestContributingprojenThis is mostly based onprojen, which takes care of generating the entire repository.cdktf-provider-project based on projenThere's a customproject builderwhich encapsulate the common settings for allcdktfproviders.Provider VersionThe provider version can be adjusted in./.projenrc.js.Repository ManagementThe repository is managed byRepository Manager
Package: dschmidt-cdktf-provider-google-beta
Terraform CDK google-beta Provider ~> 4.36This repo builds and publishes the Terraform google-beta Provider bindings forCDK for Terraform.Available PackagesNPMThe npm package is available athttps://www.npmjs.com/package/@dschmidt/provider-google-beta.npm install @dschmidt/provider-google-betaPyPIThe PyPI package is available athttps://pypi.org/project/dschmidt-cdktf-provider-google_beta.pipenv install dschmidt-cdktf-provider-google_betaNugetThe Nuget package is available athttps://www.nuget.org/packages/DSchmidt.Dschmidt.Providers.GoogleBeta.dotnet add package DSchmidt.Dschmidt.Providers.GoogleBetaMavenThe Maven package is available athttps://mvnrepository.com/artifact/com.danielmschmidt/dschmidt-provider-google-beta.<dependency>
    <groupId>com.danielmschmidt</groupId>
    <artifactId>dschmidt-provider-google-beta</artifactId>
    <version>[REPLACE WITH DESIRED VERSION]</version>
</dependency>GoThe go package is generated into thegithub.com/hashicorp/cdktf-provider-google-beta-gopackage.go get github.com/hashicorp/cdktf-provider-google-beta-go/google-betaDocsFind auto-generated docs for this provider here:./API.mdYou can also visit a hosted version of the documentation onconstructs.dev.VersioningThis project is explicitly not tracking the Terraform google-beta Provider version 1:1. In fact, it always trackslatestof~> 4.36with every release. If there are scenarios where you explicitly have to pin your provider version, you can do so by generating theprovider constructs manually.These are the upstream dependencies:Terraform CDKTerraform google-beta ProviderTerraform EngineIf there are breaking changes (backward incompatible) in any of the above, the major version of this project will be bumped.Features / Issues / BugsPlease report bugs and issues to theterraform cdkproject:Create bug reportCreate feature requestContributingprojenThis is mostly based onprojen, which takes care of generating the entire repository.cdktf-provider-project based on projenThere's a customproject builderwhich encapsulate the common settings for allcdktfproviders.Provider VersionThe provider version can be adjusted in./.projenrc.js.Repository ManagementThe repository is managed byRepository Manager
Package: dsci310-g7-zoo
zooPackage created for dsci_310 zoo analysisInstallation$pipinstallzooUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensezoowas created by Group_7. It is licensed under the terms of the MIT license.Creditszoowas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: dsci-310-group-10-package
DSCI-310-Group-10-PackageDSCI_310_Group_10_Package is a Python package for the investment income outcome analysis done for DSCI310. This package is largely dependent on the scikit-learn and matplotlib packages, and is distributed under the MIT License.The functions in this package have roles related to hyperparamater optimizations for specific machine learning models, and plot the results obtained.The main project where this package is used can be found in the following GitHub Repository:https://github.com/DSCI-310/DSCI-310-Group-10Installation$pipinstallDSCI-310-Group-10-PackageUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.LicenseDSCI-310-Group-10-Packagewas created by Harry Zhang Ahmed Rizk Mahdi Heydar Nikko Dumrique. It is licensed under the terms of the MIT license.CreditsDSCI-310-Group-10-Packagewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: dsci-310-group-11-pkg
dsci_310_group_11_pkgThis package contains functions used for generating dsci_310_group_11's report. The main functions inside this package is to
split the data, generate pipeline for predictive models, conduct hyperparameter tunning and make graphs.The main difference between our package to other similar functions is that the functions in our package is designed to improve the efficiency for analyzing the wine data. Using our pipeline generator as example, while directly using functions in sklearns can generate the same model, we simplifies this process by integrating repetitive steps into our functions.Code Coverage BadgeInstallationClone the git repository onto your local machine using:git clone https://github.com/DSCI-310/dsci-310-group-11-pkg.gitNavigate to the local repository through the terminal (this may differ based on your home directory setups):cd dsci-310-group-11-pkgRun pip to install the package locally (frompypi):pip install dsci-310-group-11-pkgYou should now be able to import the package into relevant projects and notebooks.UsageThe most basic usage of this package is to split the data into training or testing data:fromdsci_310_group_11_pkg.preprocessimportpreprocessordf# target dataframetraining_data=preprocessor(df,0)testing_data=preprocessor(df,1)One of the other usages ofdsci_310_group_11_pkgis to generate pipeline for classification models as follows:fromdsci_310_group_11_pkg.pipelineimportpipe_buildx=X_training_data# training features datay=Y_trainingdata# training label datamodel_type='lr'# types of modelmodel=pipe_build(model_type,x,y)Another usage is to conduct hyperparameter tuning and return it as dataframe for different model as follows:fromdsci_310_group_11_pkg.optimizeimporthy_optimizerx=X_training_data# training features datay=Y_trainingdata# training label datamodel_name='lr'# types of modeltuning_result=hp_optimize(model_name,x,y)This package can also be used to generate different graphs for the analysis report, for example:fromdsci_310_group_11_pkg.grapherimportcorrelation_tablectb=correlation_table(df)# this generate the correlation table for dataframe dfOther functions and more detailed usage can be found in docs/example or in thewebsite documentation of the package.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensedsci_310_group_11_pkgwas created by DSCI_310_Group_11. It is licensed under the terms of the MIT license.Creditsdsci_310_group_11_pkgwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: dsci-310-group-1-package
DSCI_310_group_1_packageA package containing the necessary functions to smoothly run the analysis in DSCI-310-Group-1Installation$pipinstallDSCI_310_group_1_packageUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.LicenseDSCI_310_group_1_packagewas created by Andres Perez, Daniel Hou, Zizhen Guo, Timothy Zhou. It is licensed under the terms of the MIT license.CreditsDSCI_310_group_1_packagewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: dscience
dscienceA lightweight Python 3 library for everyday coding, data science, and bioinformatics.
Integrated with Numpy and Pandas 1.0+, with few other dependencies.Provides:Many utility functions, data types, and decoratorsExceptions that store useful attributes to help calling code recoverPandas DataFrames with required columns / enforced structure and extra methodsSimple utilities for Jupyter notebooksAlgorithms, bioinformatics resources, and machine learning supportcontributingThis project is under active development.New issuesand pull requests are welcome.
We use tabs for indentation and will eventually useconventional commits.licenseThe authors release these contents and documentation files under the terms of theApache License, version 2.0.
The project was developed to support research at the Kokel Lab, fulfill requirements forUCSF QBCPhD programs, and be useful to the public.Authors and contributors:Douglas Myers-Turnbull (primary)Chris Ki (contributor)Cole Helsell (contributor)
Package: dsciencelib
This is the description file for the project.The file should use UTF-8 encoding and be written using ReStructured Text. It
will be used to generate the project webpage on PyPI, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.