Saravanan666/pypi-org huggingface dataset:
Package: cdk-valheim
cdk-valheimA high level CDK construct ofValheimdedicated server.FeaturesFargate cluster to run a Valheim server, with EFS for persistence (schedulable)Hourly AWS Backup with 3 days retention (customizable)lloesche/valheim-serveras the default container image (replaceable)Seeintegration testfor an example.API DocSeeAPI.mdReferencesOn-demand, serverless Valheim server setup with AWS CDK, Discord Interactions and GitLab CIGrantapher/discord-cdk-valheimExamplesThe construct is published to both npm and PyPI.TypeScriptnewValheimWorld(stack,'ValheimWorld',{cpu:2048,memoryLimitMiB:4096,schedules:[{start:{hour:'12',weekDay:'1-5'},stop:{hour:'1',weekDay:'1-5'},}],environment:{SERVER_NAME:'CDK Valheim',WORLD_NAME:'Amazon',SERVER_PASS:'fargate',BACKUPS:'false',},});PythonValheimWorld(self,'ValheimWorld',cpu=2048,memory_limit_mib=4096,schedules=[ValheimWorldScalingSchedule(start=CronOptions(hour='12',week_day='1-5'),stop=CronOptions(hour='1',week_day='1-5'),)],environment={'SERVER_NAME':'CDK Valheim','WORLD_NAME':'Amazon','SERVER_PASS':'fargate','BACKUPS':'false',})TestingUnit test and snapshot testnpxprojentestIntegration testnpxcdk-a"npx ts-node src/integ.valheim.ts"diff
npxcdk-a"npx ts-node src/integ.valheim.ts"deploy
Package: cdk-validator-checkov
CDK Checkov Validator Plugin---The APIs of higher level constructs in this module are experimental and under active development.
They are subject to non-backward compatible changes or removal in any future version. These are
not subject to theSemantic Versioningmodel and breaking changes will be
announced in the release notes. This means that while you may use them, you may need to update
your source code when upgrading to a newer version of this package.InstallationTypeScript/JavaScriptnpminstall@bridgecrew/cdk-validator-checkovPythonpipinstallcdk-validator-checkovUsageTo use this plugin in your CDK application add it to the CDK App.Pythonfromcdk_validator_checkovimportCheckovValidator...App(policy_validation_beta1=[CheckovValidator()])By default, theCheckovValidatorplugin comes with allcheckovbuilt-in checks for CloudFormation.
In order to disable any of the checks or just run a subset of them you can use thecheckorskipCheckproperty.CheckovValidator(check=['CKV_AWS_18','CKV_AWS_21'])CheckovValidator(skipCheck=['CKV_AWS_18','CKV_AWS_21']}TypeScriptnewApp({policyValidationBeta1:[newCheckovValidator(),],});Specify checks:newCheckovValidator({check:['CKV_AWS_18','CKV_AWS_21'],});Skip checks:newCheckovValidator({skipCheck:['CKV_AWS_18','CKV_AWS_21'],});
Package: cdk-versioned-stack-manager
cdk-versioned-stack-managerA CDK construct for dealing with Versioned Stacks - multiple copies of the same stack that would forever grow over time without this. This prevents hitting AWS quotas, but allows for some replication.Usagenewcdk.Stack(app,`VersionedStack-${Date.now()}`);//InsidedifferentstacknewVersionedStackManager(this,'VersionedStackManager',{dryRun:false,//Usethistotesttheconstruct,willnotactuallydeletebutwilllogwhatitwoulddeletenumberOfStacksToKeep:5,//KeepthismanystacksrequestId:newDate().toISOString(),//Shouldchangeeverytimeyouwantthistorun.sortDirection:"DESCENDING",//Optional,defaultstoDESCENDING,indicateshowyourstackNamesshouldbesortedstackNamePrefix:'VersionedStack-'//Thepertinentstacknamesshouldstartwiththis});Use CasesBlue/Green Deployments of entire stacks
Package: cdk-vscode-fargate
Welcome to cdk-vscode-fargatecdk-vscode-fargateis a JSII construct library for AWS CDK that allows you to deployCode-serverrunning VS Code remotely, on a AWS Fargate container.By deploying theVSCodeFargateconstruct, the following resources will be created:VPC (if not passed in as a prop)ACM DNS validated certificateECS ClusterEFS file systemALB Fargate ServiceSecurity GroupsSecrets Manager secret (for login authorization)HowtoCreate a new project with AWS CDK$mkdirmy-vscode-fargate&&cdmy-vscode-fargate# initialize the AWS CDK project$cdkinit-ltypescript# install the cdk-vscode-fargate npm module$yarnaddcdk-vscode-fargateAWS CDK sampleBuilding your serverless VS Code service with theVSCodeFargateconstruct:Update./lib/my-vscode-fargate-stack.ts# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826importaws_cdk.aws_ec2asec2importaws_cdk.coreascdkfromcdk_vscode_fargateimportVSCodeFargateclassCdkStack(cdk.Stack):def__init__(self,scope,id,*,description=None,env=None,stackName=None,tags=None,synthesizer=None,terminationProtection=None,analyticsReporting=None):super().__init__(scope,id,description=description,env=env,stackName=stackName,tags=tags,synthesizer=synthesizer,terminationProtection=terminationProtection,analyticsReporting=analyticsReporting)subdomain=process.env.VSCODE_SUBDOMAIN??"vscode"domain_name=process.env.VSCODE_DOMAIN_NAME??"mydomain.com"vpc=ec2.Vpc.from_lookup(self,"Vpc",is_default=True)VSCodeFargate(self,"MyVSCodeFargate",domain_name=domain_name,subdomain=subdomain,vpc=vpc)diff the CDK stack:$cdkdeploydeploy the CDK stack:$cdkdiffOn deploy completion, the subdomain/domain name assigned to the load balancer will be returned in the Output. Click the URL and you will see the login page:
Package: cdk-watchful
✨ Have you heard ofcdk-monitoring-constructs? ✨Watchful on steroids. Check it out! 🔝cdk-watchfulWatching your CDK back since 2019Watchful is anAWS CDKconstruct library that makes it easy
to monitor CDK apps. It automatically synthesizes alarms and dashboards for supported AWS resources.# my_table: dynamodb.Table# my_function: lambda.Function# my_rest_api: apigw.RestApiwf=Watchful(self,"watchful")wf.watch_dynamo_table("My Cute Little Table",my_table)wf.watch_lambda_function("My Function",my_function)wf.watch_api_gateway("My REST API",my_rest_api)And...InitializeTo get started, just define aWatchfulconstruct in your CDK app.
You can initialize using an email address, SQS ARN or both:importaws_cdk.aws_snsassnsimportaws_cdk.aws_sqsassqsalarm_sqs=sqs.Queue.from_queue_arn(self,"AlarmQueue","arn:aws:sqs:us-east-1:444455556666:alarm-queue")alarm_sns=sns.Topic.from_topic_arn(self,"AlarmTopic","arn:aws:sns:us-east-2:444455556666:MyTopic")wf=Watchful(self,"watchful",alarm_email="your@email.com",alarm_sqs=alarm_sqs,alarm_sns=alarm_sns,alarm_action_arns=["arn:aws:sqs:us-east-1:444455556666:alarm-queue"])Add ResourcesWatchful manages a central dashboard and configures default alarming for:Amazon DynamoDB:watchful.watchDynamoTableAWS Lambda:watchful.watchLambdaFunctionAmazon API Gateway:watchful.watchApiGatewayRequest yoursWatching ScopesWatchful can also watch complete CDK construct scopes. It will automatically
discover all watchable resources within that scope (recursively), add them
to your dashboard and configure alarms for them.# storage_layer: Stack# wf: Watchfulwf.watch_scope(storage_layer)ExampleSee a more completeexample.ContributingContributions of all kinds are welcome and celebrated. Raise an issue, submit a PR, do the right thing.To set up a dev environment:Clone this repoyarnDevelopment workflow (change code and run tests automatically):yarntest:watchBuild (like CI):yarnbuildAnd then publish as a PR.LicenseApache 2.0
Package: cdk-wordpress
cdk-wordpressA CDK construct library to deploy WordPressHow do useInstall your package manager:yarnaddcdk-wordpressTypeScript Sampleimport{WordPress}from"cdk-wordpress";constwordpress=newWordPress(stack,"WordPressEcs");//GetWordPressendpointnewCfnOutput(stack,"Endpoint",{value:wordpress.endpoint});To deploycdkdeployTo destroycdkdestroy
Package: cdl
DataLab is anopen-source platform for scientific and technical data processing
and visualizationwith unique features designed to meet industrial requirements.SeeDataLab websitefor more details.ℹ️ Created byCodra/Pierre Raybautin 2023, developed and maintained by DataLab open-source project team.ℹ️ DataLab is powered byPlotPyStack🚀.ℹ️ DataLab is built on Python and scientific libraries.Try DataLab online, without installing anything, using Binder:✨ Add features to DataLab by writing your ownplugin(seeplugin examples)
or macro (seemacro examples)✨ DataLab may be remotely controlled from a third-party application (such as Jupyter,
Spyder or any IDE):Using the integratedremote controlfeature (this requires to install DataLab as a Python package)Using the lightweightDataLab Simple Client(pip install cdlclient)
Package: cdl2r
# cdl2rCombination Dependent Learning to Rank (組合せ依存型ランキング学習).## requirements- Python 3.6.x ~, 3.7.x ~## dependencies- NumPy- Pandas## installation```shell$ pip install cdl2r```## usage### 1. prepare your datasetThe dataset format is like SVM-rank one.The difference is `eid` must be specified in a line.Here is a definition of a line.`|` symbol means `OR` (so `<str>|<int>` means the value must have either str or int type).```txt<line>     .=. <label> qid:<qid> eid:<eid> <features>#<comments><label>    .=. <float>|<str as a class><qid>      .=. <str>|<int><eid>      .=. <str>|<int><features> .=. <dim>:<value><dim>      .=. <0 or Natural Number><value>    .=. <float><comments> .=. <Any text will do>```Let me show you an example.```txt0.5 qid:1 eid:x 1:0.1 2:-0.2 3:0.3#comment A0.0 qid:1 eid:y 1:-0.1 2:0.2 4:0.4-0.5 qid:1 eid:z 2:-0.2 3:0.3 4:-0.4#comment C0.5 qid:2 eid:y 1:0.1 2:-0.2 3:0.30.0 qid:2 eid:z 1:-0.1 2:0.2 4:0.4-0.5 qid:2 eid:w 2:-0.2 3:0.3 4:-0.4#comment E```### 2. loading your dataset```pythonfrom cdl2r.dataset import load_data# loading dataset as a DataFrame objectdata_path = '/path/to/dataset'n_dimensions = 10train = load_data(data_path, n_dimensions)# train.columns# >>> Index(['label', 'qid', 'eid', 'features'], dtype='object')```### 3. fitting the model```pythonfrom cdl2r.models import CDFMRegressor# define your modelmodel = CDFMRegressor(n_factors=8, n_iterations=300, init_eta=1e-2)# fitting, printing out epoch losses if verbose is Truemodel.fit(train, verbose=True)```### 4. save the model```pythonimport picklewith open('/path/to/file.pkl', mode='wb') as fp:pickle.dump(model, fp)```### 5. make prediction```python# loading test datasettest = load_data(test_path, n_dimensions)pred = model.predict(test)# pred.columns# >>> Index(['pred_label', 'qid', 'eid', 'features'], dtype='object')```## development### build Cython modules```shell$ python setup.py build_ext --inplace```### profiling```shell# decorate a method with `@profile` in a script where you want to profile.$ kernprof -l -v <script>.py```### pylint- max-line-length: 130- disable snake-case### release```shell# build$ python setup.py bdist_whell# testing upload$ twine upload --repository testpypi dist/<cdl2r-version-pkg>$ pip install --index-url https://test.pypi.org/simple/<cdl2r-version-pkg># upload$ twine upload --repository pypi dist<cdl2r-version-pkg>```
Package: cdlclient
DataLab Simple Clientℹ️ Created byCodra/Pierre Raybautin 2023, developed and maintained by DataLab open-source project team.ℹ️ DataLab is powered byPlotPyStack🚀.About DataLabDataLab is a generic signal and image processing software based on Python scientific
libraries (such as NumPy, SciPy or scikit-image) and Qt graphical user interfaces
(thanks to the powerfulPlotPyStack- mostly theguidataandPlotPylibraries).DataLab is available as astand-aloneapplication (see for example our all-in-one Windows installer) or as anaddon to your Python-Qt applicationthanks to advanced automation and embedding features.SeeDataLab websitefor more details.About this packageDataLab Simple Client is a Python library that can be used to interact with a DataLab application (server).
This allows to control DataLab application from a remote computer, or/and from a third-party application.DataLab Simple Client also provides ready-to-use widgets that can be used to communicate with a DataLab application:ConnectionDialog: a dialog box that allows to connect to a DataLab applicationGetObjetDialog: a dialog box that allows to retrieve an object from a DataLab applicationConnectionDialogGetObjectDialogSeedocumentationfor more details on
the library andchangelogfor recent history of changes.
Package: cd-logging
LoggerUtilityLoggerUtilityis a Python class that simplifies logging setup across your Python applications. It provides a streamlined way to configure loggers with custom formats, different logging levels, and optional file output. Whether you're debugging during development or monitoring in production,LoggerUtilitycan help you keep clear and concise logs.FeaturesEasy logger configuration with just a few lines of code.Customizable log message format.Optional date formatting in log messages.Support for logging to console or files.Convenience methods for various logging levels (debug,info,warning,error,critical).InstallationYou can simply copy theLoggerUtilityclass into your project, or if you have it packaged, you can install it viapip(assuming you have it available on PyPI):pipinstallcd-loggingUpdatepipinstall-Ucd-loggingUsageHere is a quick example of how to useLoggerUtility:fromcd_logging.cd_loggingimportLoggerUtility# Initialize the loggerlogger_util=LoggerUtility(__name__,level=logging.INFO)# Log some messageslogger_util.info("Application is starting...")logger_util.warning("An optional warning message.")logger_util.error("An error occurred.")# Log a critical message with exception tracebacktry:raiseValueError("An example exception.")exceptException:logger_util.critical("A critical error occurred.",exc_info=True)ConfigurationWhen initializing theLoggerUtility, you can customize the following:name: The logger's name, typically set to__name__to reflect the module's name.level: The threshold for the logging messages (e.g.,logging.DEBUG,logging.INFO).log_format: Custom format for the log messages.date_format: Custom date format for the log messages.filename: If set, logs will be directed to the specified file.DocumentationFor more details on how to use and configureLoggerUtility, please refer to the inline documentation within the class itself. Each method and initializer parameter is thoroughly documented with docstrings.More documentation at:Code DoctaContributingWe welcome contributions from the community! If you'd like to contribute, feel free to fork the repository, make your changes, and submit a pull request.LicenseLoggerUtilityis licensed under the MIT License. See the LICENSE file for more details.ContactIf you have any questions or feedback, please open an issue in the GitHub repository, and we will get back to you as soon as possible.
Package: cdlpto
cdlptoThis is a command line application that will fill out CDL's PTO request form for you.cdlpto2023-06-26--n-days5--comment"Summer Vacation"After writing the PDF, the program will pop it open in Preview. It also opens an email draft in the Gmail web interface. Drag the document proxy icon from Preview's title bar to the compose window and hit send.InstallationUsepipx:pipxinstallcdlptoPlain oldpipwill work too.Or, if you want a development version, use:pipinstall--editablegit+https://github.com/baldwint/cdlpto#egg=cdlptoConfigurationOn first run, a config file is put in~/Library/Application Support/cdlpto. This is where you configure your name, email signature, and the person you send the form to. You should also put a blank,signedcopy of thePDF formin this folder for use as a template.UsageRuncdlpto --helpto see the options. It can also do sick leave, floating holidays, etc.LimitationsIt doesn't know about weekends or the CDL holiday schedule. So don't give it any multi-week spans, or spans that include days you don't need to take PTO for.
Package: cdmaoapi
cdmaoapi库查看帮助文档功能集合大量编程猫社区接口，同时简化大量爬虫解析步骤，一键式获取资料！不仅是获取，还可以与官方后台交互，如：回复评论，作品收藏，作品点赞等。新版说明经过二次送代增入了类，提高了执行效率。研发者SS_LiMgXn冷鱼闲风
Package: cdmc
====CDMC - Cinco De Mayo Calendar tools====See the chat logs. ( at https://tilde.town/~minerobber/cdmc.txt )Includes 2 scripts:* cdmdate - the date in the CDM Calendar* cdmcdateconv - A date converter to convert "plebian" normal dates into the "obviously superior" CDM format.The module, "cdmc", includes methods:* today() - get today in the CDM calendar* specific_day(y,m,d) - get day y-m-d in the CDM CalendarAs well as the class Date, which has a constructor that takes a datetime.date object (defaults to datetime.date.today()) and a datetime.date object respresenting the Cinco de Mayo you want to use as a reference point, (defaults to the Cinco de Mayo that is coming up from the datetime.date.today() date) and gives you a CDM date object.
Package: cdm-connector
skypoint-python-cdm-connectorPython Spark CDM Connector by SkyPoint.Apache Spark connector for the Microsoft Azure "Common Data Model". Reading and writing is supported and it is a work in progress. Please file issues for any bugs that you find.For more information about the Azure Common Data Model, check outthis page.We support Azure Data Lake Service (ADLS) and AWS S3 as storage, historical data preservation using snapshots of the schema & data files and usage within PySpark, Azure Functions etc.*Upcoming Support for incremental data refresh handling, [CDM 1.1](https://docs.microsoft.com/en-us/common-data-model/cdm-manifestand Google Cloud (Cloud Storage).ExamplePlease look into the sample usage file skypoint_python_cdm.pyDynamically add/remove entities, annotations and attributesPass Reader and Writer object for any storage account you like to write/read data to/from.Check out the below code for basic read and write examples.# Initialize empty modelm=Model()# Sample dataframedf={"country":["Brazil","Russia","India","China","South Africa","ParaSF"],"currentTime":[datetime.now(),datetime.now(),datetime.now(),datetime.now(),datetime.now(),datetime.now()],"area":[8.516,17.10,3.286,9.597,1.221,2.222],"capital":["Brasilia","Moscow","New Dehli","Beijing","Pretoria","ParaSF"],"population":[200.4,143.5,1252,1357,52.98,12.34]}df=pd.DataFrame(df)# Generate entity from the dataframeentity=Model.generate_entity(df,"customEntity")# Add generated entity to modelm.add_entity(entity)# Add model level annotation# Annotation can be added at entity level as well as attribute levelModel.add_annotation("modelJsonAnnotation","modelJsonAnnotationValue",m)# Create an ADLSWriter to write into ADLSwriter=ADLSWriter("ACCOUNT_NAME","ACCOUNT_KEY","CONTAINER_NAME","STORAGE_NAME","DATAFLOW_NAME")# Write data as well as model.json in ADLS storagem.write_to_storage("customEntity",df,writer)ContributingThis project welcomes contributions and suggestions.ReferencesModel.json version1 schemaA clean implementation for Python Objects from/to model.json file
Package: cdm-connector-1.1
skypoint-python-cdm-connectorThis repository is a clone of Microsoft's Python SDK for CDM which can be found athttps://github.com/microsoft/CDM/blob/master/objectModel/Python. This respository was created in order to build on top of their standards and for our custom use cases. This will be kept updated on par with the aforementioned original and users are welcome to use and contribute to it.
Package: cdmgcplugin
cdm-gc-pluginThe project is aCodimension Python IDEgarbage collector plugin.The idea of the plugin is simple. The embedded Python garbage collector
triggers objects collection at pretty much unknown moments and the
plugin will make it more predictable. The plugin will call the``collect()``method of the Python gc module when:a tab is closeda project is changednew files appeared in a projectsome files are deleted from a projectThere is pretty much no user interface for the plugin. It does its work
in the background.InstallationThe plugin is pip installable:pipinstallcdmgcpluginThere is a little value (if any) to install it without theCodimension
IDE. It however can be a good start point in
developing a new plugin for Codimension. The overall structure could be
copied as well as the way the plugin is installed.
Package: cdmi_client
An interactive command line client for CDMI.InstallationTo install cdmi-cli, simply:$sudopipinstallcdmi_clientTo install cdmi-cli from source:$sudopythonsetup.pyinstallor alternatively (using virtualenv):(env)$pythonsetup.pyinstallGetting StartedTo use (with caution), simply do:$cdmi-clicdmi@>?availablecommands:quit-quitclientqos-manageQoSforCDMIobjecthelp-showhelpforavailablecommandsauth-authenticationtotheCDMIserverexit-exitclientquery-makeCDMIqueryclose-closeconnectiontoCDMIserveropen-openconnectiontoCDMIserver?-showavailablecommandsMore ExamplesSimple QueryTo perform a simple query you need to open a connection first and provide some
authentication, for example:$cdmi-clicdmi@>opencdmi.exmpale.com443cdmi@cdmi.example.com>authbasicEnterusername:cdmi_userEnterpassword:cdmi@cdmi.example.com>query/
Package: cdmn
cDMNWelcome to the cDMN solver's code repository.cDMN stands for Constraint Decision and Model Notation.
It is an extension to theDMNstandard, managed by the Object Management Group (OMG).
cDMN combines the readability and straighforwardness of DMN and the expressiveness and flexibility of constraint reasoning.
For more specific details, please visit ourcDMN documentation.ExamplesExample implementations can also be found in thecDMN documentation.Installation and usageThe full installation and usage guide for the cDMN solver can be foundhere.
In short for Linux: after cloning this repo, install the Python dependencies.git clone https://gitlab.com/EAVISE/cdmn/cdmn-solver
cd cdmn-solver
pip3 install -r requirements.txtAfter this, you can run the solver. Example usage is as follows:python3 -O solver.py Name_Of_XLSX.xlsx -n "Name_Of_Sheet" -o output_name.idpReferenceIf you used cDMN in a publication or in other works, reference us as follows:BibTeX:@article{cDMN,
  title = {Tackling the {{DM}} Challenges with {{cDMN}}: {{A}} Tight Integration of {{DMN}} and Constraint Reasoning},
  author = {Vandevelde, Simon and Aerts, Bram and Vennekens, Joost},
  year = {2021},
  journal = {Theory and Practice of Logic Programming},
  pages = {1--24},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/S1471068421000491}
}or direct cite:Vandevelde, S., Aerts, B., & Vennekens, J. (2021). Tackling the DM challenges with cDMN: A tight integration of DMN and constraint reasoning. Theory and Practice of Logic Programming, 1–24. https://doi.org/10.1017/S1471068421000491ContributorsWe would also like to thank all contributors who have developed code or given suggestions to us, such as Marjolein Deryck, Pierre Carbonnelle, Jo Devriendt, and many more!