Saravanan666/pypi-org huggingface dataset:
Package: wekan-logstash
wekan-logstashInstallTo install the package run the following commandpython-mpipinstallwekan-logstashAfter installed the package fromPyPi, the scriptget_cardsis available and ready to be running#get_cards --helpUsage:get_cards[OPTIONS][BOARDSFILE]ScripttoreadWekancardsbelongingtooneormoreboards,directlyfromMongodbinJSONformat.MakearequesttoLogstashconfiguredtousetheHTTPinputplugintoingestthecardsintoElasticsearch.

Options:--boardIdTEXTSingleBoardID--logstashMakeaHTTPrequesttoLogstashendpoint,definedatLOGSTASHSERVERenvironmentvariable-v,--verboseShowcardsJSONdata-h,--helpShowthismessageandexit.Warning: It is necessary before executing the above script that we define the following environment variables used for the connection to the mongodb database and the logstash URLMONGODB_USER: Database usernameMONGODB_PWD: Database passwordMONGODB_HOST: Database hostnameMONGODB_PORT: Database portMONGODB_DB: Database nameLOGSTASH_SERVER: Logstash endpoint urlData formatTo format data for logstash and ELK (Kibana) - Format below :{"id":"7WfoXMKnmbtaEwTnn","title":"Card title","storyPoint":2.0,"nbComments":1,"createdBy":"fmonthel","labels":["I-U","I-Nu"],"assignees":"fmonthel","members":["fmonthel","Olivier"],"boardSlug":"test","description":"A subtask description","startAt":"2021-06-07T20:36:00.000Z","endAt":"2021-06-07T20:36:00.000Z","requestedBy":"LR","assignedBy":"MM","receivedAt":"2021-06-07T20:36:00.000Z","archivedAt":"2021-06-07T20:36:00.000Z","createdAt":"2021-06-07T20:36:00.000Z","lastModification":"2017-02-19T03:12:13.740Z","list":"Done","dailyEvents":5,"board":"Test","isArchived":true,"dueAt":"2021-06-07T20:36:00.000Z","swimlaneTitle":"Swinline Title","customfieldName1":"value1","customfieldName2":"value2","boardId":"eJPAgty3guECZf4hs","cardUrl":"http://localhost/b/xxQ4HBqsmCuP5mYkb/semanal-te/WufsAmiKmmiSmXr9m","checklists":[{"name":"checklist1","items":[{"title":"item1","isFinished":true},{"title":"item2","isFinished":false}]},{"name":"checklist2","items":[{"title":"item1","isFinished":true},{"title":"item2","isFinished":true}]}]}Goal is to export data into Json format that we can be used as input for Logstash and ElastisSearch / Kibana (ELK)Import in logstash should be done daily basic (as we have field daily event)
Package: wekapyutils
Handy little Python utilities/classeswekasshA simple way of connecting to other hosts via ssh, executing commands, and copying files (ala scp), including parallel executions and copies similar to pdsh and pscp.sthreadsA simple thread management classwekaloggingA module for setting up logging in a standard way so all our programs look the samewekatimeTime conversion routines for dealing with Weka time values (which are a little different than Python time values)
Package: wekarestapi
&lt;div&gt;The Weka system supports a RESTful API. This is useful when automating the interaction with the Weka system and when integrating it into your workflows or monitoring systems. The API is accessible at port 14000, via the /api/v2 URL, you can explore it via /api/v2/docs when accessing from the cluster (e.g.https://weka01:14000/api/v2/docs).&lt;divstyle&#x3D;&quot;margin-top: 15px;&quot;&gt;Note: Weka uses 64bit numbers. Please take special care when interacting with the API with different program languages (In JS for example you can use &quot;json-bigint&quot;)&lt;/div&gt;&lt;/div&gt;  # noqa: E501
Package: wekatui
This library provides a framework for developing console applications using Python and curses.
Most of the code was shamelessly stolen from npyscreen (https://github.com/npcole/npyscreen), which appears to be a dead project, as the author (Nicholas Cole) has been unresponsive and has not made any changes in years.
However, the base code was useful for our purposes, and will retain the copyright info in the files.
Package: welcome-overlords
Welcome, Overlords!Welcome your newfunction nameoverlords by using this module.This module introduces the@Overlord(not overload with an 'a'!)
annotation. It only welcomes your new overloards and then returns
the function/class/object it has been applied to.This module is not only funny because of the associations with the
OOP concept of overloading and the famous The Simpsons meme, but
can also be considered essential for basically any project, as
this annotation can be quite useful for certain debugging
situations.
Package: welcomepackage
Welcome PackageMinimal TUI ready for cross-platform packaging as a pure-Python zipapp.UsageDownloadwelcomepackage.pyzRunpython welcomepackage.pyzDevelopment workflowRequirespdmandpdm-packer.To run from source:pdmsync--dev
pdmrunwelcomepackageTo package for distribution:pdmpack# generates welcomepackage.pyz# that's it!To package as an executable witha portable shebang:pdmpack--exe-i"/usr/bin/env python3"# You can now drop this in ~/.local/binReferenceshttps://textual.textualize.io/guide/app/#widgetshttps://github.com/frostming/pdm-packerOther options for zipapp packaging includepexandshiv. For a tutorial, tryhttps://realpython.com/python-zipapp/Even more options for Python packaging and distribution are covered inPyOxidizer: Comparisons to Other Tools.Sharing and contributionsWelcome Package
https://lofidevops.neocities.org
Copyright 2023 David Seaward and contributors
SPDX-License-Identifier: CC0-1.0You can copy and modify this project freely and without credit. It's mostly
uncopyrightable anyway.
Package: weldbuild
FeaturesPopular platforms deployment: Windows, Linux, AndroidProject configuration is simple: One build.pyPrecompiled libraries out of the box: Windows (x64, x86), Linux (x64, x86), Android (armeabi-v7a, arm64-v8a)RequirementsAndroid deploymentAndroid SDKAndroid NDK r26JDK 21Windows deploymentMicrosoft Visual Studio Build Tools with CMake componentLinux deploymentCMakeExamplefromweldbuildimportArchTypefromweldbuild.platformsimportWindowsBuildproject=WindowsBuild(app_version=(1,0,0),app_name="Example",app_icons={"mdpi":"mdpi.png","hdpi":"hdpi.png","xhdpi":"xhdpi.png","xxhdpi":"xxhdpi.png"},app_console=True,py_version="3.11.5")project.configure(ArchType.X64)project.build()Example projectRoadmapDeploymentsWindowsLinuxAndroidCustom pre-post build commandsLicenseCheck LICENSE for additional information.
Package: weldyn
WeldynThe Weldyn package implements theYamlConfigurableModel, which is a flexible class for working with Pydantic models and YAML files.
It allows you to easily define and update configurations for your Python applications.FeaturesSub-models of a Pydantic model can be dumped to one or several YAML files for easy editing.The location of the YAML files and which sub-models are written to which file can be easily configured.When a YAML file does not exist, it is automatically created.When a YAML file exists, values in the file have priority over values defined in the Pydantic model. Changing the value in the Pydantic model won't change its value in the YAML file.If a field is added to or removed from the Pydantic model, the YAML file is automatically updated to reflect this change.InstallationpipinstallweldynUsageThe package requires Python 3.10+.frompathlibimportPathfromweldynimportget_root,field_validator,BaseModel,YamlConfigurableModel# `BaseModel` and `validator` can be imported from `weldyn` instead of `pydantic`ROOT_DIR=get_root(__file__,depth=1)# Get the absolute path to the project's root directoryDATA_DIR=ROOT_DIR/'data'# Create your Pydantic modelsclassPathConfig(BaseModel):# Use absolute path to make it work from anywhere in the projectroot:Path=ROOT_DIRdata:Path=DATA_DIRraw:Path=DATA_DIR/'raw'processed:Path=DATA_DIR/'processed'classTfIdfConfig(BaseModel):ngram_range:list[int]=[1,3]vocab_size:int=500incremented:int=1# Validators can be used: in this case `incremented` will be 1 in the YAML file, but 2 in the Pydantic model@field_validator('incremented',mode='before')defvalidate_incremented(cls,v):returnv+1classRandomForestConfig(BaseModel):n_estimators:int=200max_depth:int=3max_features:str='sqrt'classModelConfig(BaseModel):tfidf:TfIdfConfig=TfIdfConfig()# Models can be nestedrandom_forest:RandomForestConfig=RandomForestConfig()nn:str='RoBERTa'classTrainingConfig(BaseModel):epochs:int=10learning_rate:float=0.001batch_size:int=64classDataConfig(BaseModel):preprocess:bool=True# Create main configuration model which inherits from `YamlConfigurableModel`classMyConfig(YamlConfigurableModel):path:PathConfig=PathConfig()# Can't be dumped into YAML because `Path` objects are not serializabledata:DataConfig=DataConfig()# Will be dumped in its own YAML filemodel:ModelConfig=ModelConfig()# Will be dumped with `training` sub-modeltraining:TrainingConfig=TrainingConfig()# Use the `YamlConfig` inner class to specify the YAML directory# and which models should be dumped to which YAML filesclassYamlConfig:YAML_PATH='path/to/yaml'MODEL_MAPPING={'data_config':['data'],'ml_config':['model','training'],}# Initialize the modelcfg=MyConfig()Now you can access your configuration from anywhere in the project:fromconfigimportcfgprint(cfg.training.epochs)# 10print(cfg.model.tfidf.vocab_size)# 500If you've updatedml_config.yamlto containepochs: 20, then the next time you create an instance ofMyConfig, the updated value will be used:print(cfg.training.epochs)# 20If a field is added to or removed from the Pydantic model, then the next time you create an instance ofMyConfig, the structure of the YAML will reflect this change.
Package: weles
WelesCollection of various pattern recognition methods and experimental tools made by Machine Learning Group of Wrocław University of Science and TechnologyCurrent contentsclassifiersExposerClassifierLinearClassifierSSGNBensembleCluensGeometronGESNURSRandomSubspaceUMCEmetaPCASSEoptimizersRAOstatisticst_test_correctedt_test_13t_test_rel
Package: welford
Python (Numpy) implementation of Welford’s algorithm, which is online or
parallel algorithm for calculating variances.The algorithm is described in the followings,Wikipedia:Welford Online
AlgorithmWikipedia:Welford Parallel
AlgorithmWelford’s method is more numerically stable than the standard method.
The theoritical background of Welford’s method is mentioned in detail on
the following blog articles. Please refer them if you are interested in.http://www.johndcook.com/blog/standard_deviationhttps://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/This library is inspired by thejvf’s
implementation, which is implemented
without using numpy library.InstallDownload package viaPyPI
repository$ pip install welfordExampleFor Online CalculationimportnumpyasnpfromwelfordimportWelford# Initialize Welford objectw=Welford()# Input data samples sequentialyw.add(np.array([0,100]))w.add(np.array([1,110]))w.add(np.array([2,120]))# outputprint(w.mean)# mean --> [  1. 110.]print(w.var_s)# sample variance --> [1, 100]print(w.var_p)# population variance --> [ 0.6666 66.66]# You can add other samples after calculating variances.w.add(np.array([3,130]))w.add(np.array([4,140]))# output with added samplesprint(w.mean)# mean --> [  2. 120.]print(w.var_s)# sample variance --> [  2.5 250. ]print(w.var_p)# population variance --> [  2. 200.]Welford object supports initialization with data samples and batch
addition of samples.# Initialize Welford object with samplesini=np.array([[0,100],[1,110],[2,120]])w=Welford(ini)# outputprint(w.mean)# mean --> [  1. 110.]print(w.var_s)# sample variance --> [1, 100]print(w.var_p)# population variance --> [ 0.66666667 66.66666667]# add other samples through batch methodother_samples=np.array([[3,130],[4,140]])w.add_all(other_samples)# output with added samplesprint(w.mean)# mean --> [  2. 120.]print(w.var_s)# sample variance --> [  2.5 250. ]print(w.var_p)# population variance --> [  2. 200.]For Parallel CalculationWelford also offers parallel calculation method for variance.importnumpyasnpfromwelfordimportWelford# Initialize two Welford objectsw_1=Welford()w_2=Welford()# Each object will calculate variance of each samples in parallel.# On w_1w_1.add(np.array([0,100]))w_1.add(np.array([1,110]))w_1.add(np.array([2,120]))print(w_1.var_s)# sample variance -->[  1. 100.]print(w_1.var_p)# population variance -->[ 0.66666667 66.66666667]# On w_2w_2.add(np.array([3,130]))w_2.add(np.array([4,140]))print(w_2.var_s)# sample variance -->[ 0.5 50. ]print(w_2.var_p)# sample variance -->[ 0.25 25.  ]# You can Merge objects to get variance of WHOLE samplesw_1.merge(w_2)print(w.var_s)# sample variance --> [  2.5 250. ]print(w_1.var_p)# sample variance -->[  2. 200.]
Package: welkin
welkin-healthA Python wrapper of the Welkin Health APIThis package allows Python developers to write software that makes use of the Welkin Health API. Functions available in the API are mirrored in this package as closely as possible, translating JSON responses to Python objects. You can find the current documentation for the Welkin Health API here:Welkin Health API DocumentationInstallingpip install welkinQuick StartfromwelkinimportClientwelkin=Client(tenant="gh",instance="sb-demo",api_client="VBOPNRYRWJIP",secret_key="+}B{KGTG6#zG%P;tQm0C",)### Patient methodspatient=welkin.Patient(firstName="Foo",lastName="Bar").create()# Createpatient=welkin.Patient(id="6801d498-26f4-4aee-961b-5daffcf193c8").get()# Readpatients=welkin.Patients().get()# Read all/listpatient.update(firstName="Baz")# Updatepatient.delete()# Delete### User methodsuser=client.User(username="bar",email="bar@foo.com").create()# Createuser=welkin.User(id="301b2895-cbf0-4cac-b4cf-1d082faee95c").get()# Readusers=welkin.Users().get()# Read all/listuasers=welkin.Users().get(search="lightmatter",region="east-coast",seat_assigned=True,user_state="ACTIVE")# Filtered read all/listuser.update(firstName="Baz")# Updateuser.delete()# Delete
Package: well-architected
Well ArchitectedWell-Architected CDK Patterns in Python fromhttps://cdkpatterns.com/patterns/well-architected/Available StacksApiLambdaRdsApiLambdaDynamodbApiLambdaDynamodbEventBridgeLambdaAutoscalingEcsServiceAutoscalingEcsServiceWithPlacementAutoscalingEcsClusterAlbAutoscalingEcsServiceNlbAutoscalingEcsServiceNlbFargateServiceNlbAutoscalingFargateServiceApiLambdaEventBridgeLambdaApiLambdaSqsLambdaDynamodbApiSnsLambdaEventBridgeLambdaApiSnsSqsLambdaApiStepFunctionsLambdaFatLambdaLithLambdaPowerTunerLambdaSinglePurposeRestApiDynamodbRestApiSnsS3SqsLambdaEcsEventBridgeLambdaDynamodbSagaStepFunctionSimpleGraphqlServiceSnsLambdaSnsLambdaSnsSnsLambdaDynamodbSqsLambdaSqsWafApiLambdaDynamodbAvailable ConstructsApiApiLambdaApiLambdaDynamodbDynamodbTableHttpApiStepFunctionsLambdaFunctionRestApiRestApiSnsSnsLambdaWebApplicationFirewallExamplesUsing a Well Architected Stackimportaws_cdkimportwell_architected_stacksapp=aws_cdk.App()well_architected_stacks.api_lambda_eventbridge_lambda.ApiLambdaDynamodbEventBridgeLambda(app,'ApiLambdaDynamodbEventBridgeLambda)app.synth()Creating a Stack using Well Architected Constructsimportconstructsimportwell_architected_constructs.web_application_firewallimportwell_architected_constructs.api_lambda_dynamodbimportwell_architected_stackclassWafApiLambdaDynamodb(well_architected_stack.Stack):def__init__(self,scope:constructs.Construct,id:str,partition_key='path',sort_key=None,**kwargs):super().__init__(scope,id,**kwargs)self.name=self.camel_to_snake(id)self.api_lambda_dynamodb=well_architected_constructs.api_lambda_dynamodb.ApiLambdaDynamodbConstruct(self,'ApiLambdaDynamoDb',function_name=self.name,partition_key=partition_key,error_topic=self.error_topic,)self.web_application_firewall=well_architected_constructs.web_application_firewall.WebApplicationFirewall(self,'WebApplicationFirewall',error_topic=self.error_topic,target_arn=f"arn:aws:apigateway:region::/restapis/{self.api_lambda_dynamodb.rest_api.api_id}/stages/{self.api_lambda_dynamodb.rest_api.api.deployment_stage.stage_name}",)@staticmethoddefcamel_to_snake(text):return''.join(['_'+character.lower()ifcharacter.isupper()elsecharacterforcharacterintext]).lstrip('_')
Package: well-architected-constructs
Well ArchitectedPython Well-Architected CDK Patterns fromhttps://cdkpatterns.com/patterns/well-architected/Available ConstructsApiApiLambdaApiLambdaDynamodbDynamodbTableHttpApiStepFunctionsLambdaFunctionRestApiRestApiSnsSnsLambdaWebApplicationFirewallExamplesCreating a Stack using Well Architected Constructsimportconstructsimportwell_architected_constructs.web_application_firewallimportwell_architected_constructs.api_lambda_dynamodbimportwell_architected_stackclassWafApiLambdaDynamodb(well_architected_stack.Stack):def__init__(self,scope:constructs.Construct,id:str,partition_key='path',sort_key=None,**kwargs):super().__init__(scope,id,**kwargs)self.name=self.camel_to_snake(id)self.api_lambda_dynamodb=well_architected_constructs.api_lambda_dynamodb.ApiLambdaDynamodbConstruct(self,'ApiLambdaDynamoDb',function_name=self.name,partition_key=partition_key,error_topic=self.error_topic,)self.web_application_firewall=well_architected_constructs.web_application_firewall.WebApplicationFirewall(self,'WebApplicationFirewall',error_topic=self.error_topic,target_arn=f"arn:aws:apigateway:region::/restapis/{self.api_lambda_dynamodb.rest_api.api_id}/stages/{self.api_lambda_dynamodb.rest_api.api.deployment_stage.stage_name}",)@staticmethoddefcamel_to_snake(text):return''.join(['_'+character.lower()ifcharacter.isupper()elsecharacterforcharacterintext]).lstrip('_')
Package: well-architected-stacks
Well ArchitectedPython Well-Architected CDK Patterns fromhttps://cdkpatterns.com/patterns/well-architected/Available StacksApiLambdaRdsApiLambdaDynamodbApiLambdaDynamodbEventBridgeLambdaAutoscalingEcsServiceAutoscalingEcsServiceWithPlacementAutoscalingEcsClusterAlbAutoscalingEcsServiceNlbAutoscalingEcsServiceNlbFargateServiceNlbAutoscalingFargateServiceApiLambdaEventBridgeLambdaApiLambdaSqsLambdaDynamodbApiSnsLambdaEventBridgeLambdaApiSnsSqsLambdaApiStepFunctionsLambdaFatLambdaLithLambdaPowerTunerLambdaSinglePurposeRestApiDynamodbRestApiSnsS3SqsLambdaEcsEventBridgeLambdaDynamodbSagaStepFunctionSimpleGraphqlServiceSnsLambdaSnsLambdaSnsSnsLambdaDynamodbSqsLambdaSqsWafApiLambdaDynamodbExamplesUsing a Well Architected Stackimportaws_cdkimportwell_architected_stacksapp=aws_cdk.App()well_architected_stacks.api_lambda_eventbridge_lambda.ApiLambdaDynamodbEventBridgeLambda(app,'ApiLambdaDynamodbEventBridgeLambda)app.synth()
Package: well-being-diary
Project status: Pre-alphaWell-being DiaryA happiness and well-being diary application. It's a cross-platform desktop application and is in an early development/prototype stageTable of contents:DescriptionInstallationUsageFeedbackDescriptionThe user can write questions for herself that can be used to support happiness, compassion and well-beingScreenshotsLicenseGPLv3InstallationThere are no installation packages but it's simple to install by following these steps:Download the Python 3.x installation package for your platform:https://www.python.org/downloads/Install Python 3.xOn the command line:pip3 install --upgrade pip(On Ubuntu usesudo -H)On the command line:pip3 install PyQt5(On Ubuntu usesudo -H)Download the project files from GitHub, by clicking on the "Clone or download" button and then "Download ZIP"Unzip the downloaded fileAdvanced setup (optional)Please note:This is not necessary for running the application, instead you can skip directly to theusagesectionGNU/Linux systemsFor desktop systems that are compatible with thefreedesktopstandard - for example Gnome and KDE - you can use the bwb.desktop file included in the source (please note that if using a file manager such as the Gnome file manager you may see the name displayed as "Well-being Diary" rather than the file name) to make the application visible in any start-menu-like menu (in Lubuntu this is called the "main menu" and it's shown when clicking the button in the lower left, "vanilla" (the ordinary) Ubuntu may not have a menu like thisTo use this file:Edit thewell-being-diary.desktopfile and change the paths to match the path that you are usingCopy thewell-being-diary.desktopfile to your desktop or any place where you want to be able to start the application fromCopy thewell-being-diary.desktopfile to/usr/share/applications/usingsudoHardware recommendationsWorks best on screens with a resolution of at least 1366x768No network connection is neededDoes not take much processor and memory resources, expected to run smoothly on most systemUsageChange directory to where the software files have been extractedType and runpython3 well-being-diary.pyon GNU/Linux systems orpython well-being-diary.pyon WindowsTestingAlternatively you can start the application with the--testingflag, this will make sure that the application data is stored in memory only and not saved when the application is closedFeedbackFeedback is very welcome! If you send us feedback it can help improve the softwareIdeas for improving the softwarehttps://gitter.im/well-being-diary/communityReporting bugsPlease use the GitHub issue tracker:https://github.com/SunyataZero/well-being-diary/issuesDon't hesitate to file a bug!You are helping to improve the software. Also if something is unclear in thedocumentationthat counts as a bug as well, so please report itWhat is already working wellThis is good to know so that we know what to keep, also it gives motiviation to continue working on the software <3
Package: wellcadformats
wellcadformatsRead in export file formats produced byAdvanced Logic Technology (ALT)'sWellCADsoftware, and provides a few utility functions for the imaging/waveform data formats.ASCII export formats supported so far:.waw - well logs with a single floating point number per depth.waf - full-waveform sonic logs - vector array of floating point numbers per depth.wai - image log - vector array of integers per depthThe authors of this package have no affiliation with ALT.
Package: wellcome
CardSpecial Request:Albiinstall libraryGithub$python3-mpipinstallwellcomePypi$python3-mpipinstallgit+https://github.com/krypton-byte/wellcomeLibraryfromwellcomeimportWellcomewellcome=Wellcome()im=wellcome.create('images.png','krypton-byte')# show Imageim.show()#save Imageim.save('result.png')Command LineFile$python3-mwellcome--capt="krypton-byte"--pic=image.png--out=out.pngBase64$python3-mwellcome--capt="krypton-byte"--pic=image.png--base64run as server$python3-mwellcome--serverDeployResult
Package: wellcome-aws-utils
This module is a collection of AWS-related utilities that we wrote while working on the Wellcome Digital Platform.Some of these utilities are very specific to Wellcome projects, others are more generic and should be generally useful.Documentation is available atwellcome-aws-utils.readthedocs.io.InstallationYou can install wellcome_aws_utils from PyPI:$ pip install wellcome_aws_utilsNote that this packageonly supports Python 3.6 or later.It was originally written to run inside the AWS Lambda environment, which supports 2.7 or 3.6 — and we make heavy use of f-strings, which are 3.6 only.Running TestsFirst, runexport ROOT=$(git rev-parse –show-toplevel)To run tests, runmake testAdditional requirements should be added tosetup.py. Adding requirements may require removal of the local.toxdirectory - runrm -rf .toxLicenseMIT.