Saravanan666/pypi-org huggingface dataset:
Package: ado2hugo
ADO2HugoWith this program you will be able to exportAzure DevOpswikis toHugoAzure DevOps offers two kinds of wiki: Project (managed by the platform) or "Wiki as code" (publish directly markdown files from a branch in your repository).Currently, only wiki projects are supported, and it does not have sense to support the "wiki as code" option.The program uses the Azure DevOps API to iterate over an organization's projects and, in accordance with Hugo's folder structure, exports all wiki pages with their corresponding attachments.I have usedGeekdoc theme.Installationpip install ado2hugoUsageado2hugo-h
usage:ado2hugo[-h][--organizationORGANIZATION][--patPAT][--projectPROJECT][-v]site_dir

positionalarguments:site_dirSitedirectory

optionalarguments:-h,--helpshowthishelpmessageandexit--organizationORGANIZATIONOrganization--patPATPersonalaccesstoken--projectPROJECTProjectname-v,--verboseVerboseORGANIZATIONandPAToptions, can be also an environment variable.ado2hugo--organization<YOUR_ORGANIZATION>--pat<YOUR_PAT><YOUR_SITE_DIRECTORY>Be carefully because this program deletes before its execution, all files in the /static and /content folders of supplied pathDevelopmentYou must run the following commands to develop localy:Create a pipenv local environment withpipenv install --devInstall pre-commit hooks withpre-commit installFor executing__main__.py, you must usepython -m src.ado2hugo, so relative imports will work, if you usepython __main__.pyyou will receive the errorImportError: attempted relative import with no known parent package,
more information inhttps://napuzba.com/a/import-error-relative-no-parent
Package: ado-asana-sync
ado-asana-syncThis project aims to synchronize work items between Azure DevOps (ADO) and Asana. It's currently in development and not ready for use. Breaking changes will occur as needed.How to useGet the latest container image from theGithub Container Registry.Configure the environment variables with the relevant values:ADO_PAT- Your Personal Access Token for ADO to accesst the work items.ADO_URL- The full URL of your Azure DevOps instance.ASANA_TOKEN- Your Personal Access Token for Asana to access the work items.ASANA_WORKSPACE_NAME- Name of the Asana workspace to sync with.CLOSED_STATES- Comma separated list of states that will be considered closed.THREAD_COUNT- Number of projects to sync in parallel. Must be a positive integer.SLEEP_TIME- Duration in seconds to sleep between sync runs. Must be a positive integer.SYNCED_TAG_NAME- Name of the tag in Asana to append to all synced items. Must be a valid Asana tag name.Run the container with the configured environment variables.The application will start syncing work items between ADO and Asana based on the configured settings.DevelopmentCommit message styleThis repo usesConventional Commitsto ensure the build numbering is generated correctlyManual testingTo test the application manually, you can use the following steps:Create new ADO work item and ensure it is synced to Asana.Rename Asana task and ensure it is reverted back to the ADO name.Rename ADO task and ensure it is synced to Asana.Remove Synced tag from item in Asana and ensure it is replaced.Delete synced tag from Asana workspace and from appdata.json file and ensure it is re-created and assigned to all synced tasks.Mark Asana task as complete and ensure it is re-opened.Mark ADO task as complete and ensure it is marked as complete in Asana.Re-open ADO task and ensure it is re-opened in Asana.ReferenceADOazure-devops PyPiazure-devops GitHubazure-devops API referenceazure-devops samplesAsanaAsana PyPiAsana GitHubAsana API Reference
Package: adobe-aam
Adobe Audience Manager - Python ExtensionThis is a Python wrapper for the Adobe Audience Manager API.To get startedGenerate a JWT Authentication using Adobe IOThis package requires you to create a .json document with the following credential details: client ID, client secret, tech account ID, and organization ID. In a separate file, you also need generate a public/private key pair.credentials.json:{"client_id":"...","client_secret":"...","tech_acct_id":"...","org_id":"..."}Once you have these documents, you can get install the package and login:Terminal:pip install adobe_aamPython:importadobe_aamasaamaam.Login('path/to/credentials.json','path/to/private.key')Your authentication token should be tied to a Product Profile, which controls the actions you can execute and the objects on which you can act. If you are unable to perform an action supported by this package, the error is likely due to a permissions issue within the credentials setup.Here are some examples:Python:# Get traits by folder and sortaam.Traits.get_many(folderId=12345,sortBy='createTime',descending=True)# Get trait by sidaam.Traits.get_one(sid=12345)# Get traits by integration code and simplify resulting dataframeaam.Traits.get_many(ic='code',condense=True)# Get trait limits of accountaam.Traits.get_limits()# Create traits from csvaam.Traits.create_from_csv('path/to/traits_to_create.csv')If you're new to Python and want to output the results of an AAM API call, you can try something like the following:Python:importpandasaspdoutput=aam.Traits.get_one(sid=12345)output.to_csv('path/to/your_aam_output.csv')Coverage:Every standard API call for AAM can be found on SwaggerEndpointActionCoverageTraitsCreatexTraitsGetxTraitsUpdatexTraitsDeletexSegmentsCreatexSegmentsGetxSegmentsUpdatexSegmentsDeletexTrait FolderGetxSegment FolderGetxDestinationsCreate-DestinationsGet-DestinationsUpdate-DestinationsDelete-Derived SignalsCreate-Derived SignalsGet-Derived SignalsUpdate-Derived SignalsDelete-DatasourcesCreate-DatasourcesGet-DatasourcesUpdate-DatasourcesDelete-Custom reporting will be added according to roadmap. Examples:# Get traits trends for all SIDs in a folderaam.Reports.traits_trend(startDate="2021-02-21",endDate="2021-02-23",folderId=12345)# Get traits trends for one SIDaam.Reports.traits_trend(startDate="2021-02-21",endDate="2021-02-23",sid=[12345])
Package: adobe-analytics-api-20
adobe-analytics-api - An Adobe Analytics API 2.0 library for pythonInstallationYou can install this through pip: pip install adobe-analytics-api_20Usagefromadobe_analyticsimportapiimportauthenticationasauthconfig={'client_id':'CLIENT_ID','client_secret':'CLIENT_SECRET','org_id':'ORG_ID','tech_account':'TECH_ACCOUNT','keyfile_path':'KEYFILE_PATH','company_id':'COMPANY_ID'}definition={"reportsuite":"report-suite","start_date":"2019-10-01","end_date":"2019-10-01","dimensions":["Day","variables/evar50"],"metrics":[{"name":"metrics/orders"},{"name":"metrics/revenue"}],"segments":["Name|id of a Segment"]}jwt_token=auth.getToken(config["org_id"],config["tech_account"],config["client_id"],3)print(jwt_token)jwt=auth.encrypt_jwt(jwt_token,config["keyfile_path"])print(jwt)token=auth.authorize(config["client_id"],config["client_secret"],jwt)print(token)response=api.report(token['access_token'],config["client_id"],config["company_id"],definition["reportsuite"],definition["dimensions"],definition["metrics"],definition["start_date"],definition["end_date"],segments=definition["segments"])print(response)
Package: adom
adomAdomonic-likewrapper aroundselectolaxfromdomimport*print(html(body(h1('Hello, World!'))))# <html><body><h1>Hello, World!</h1></body></html>fromwindowimport*window.location="https://www.google.com"print(window.document.css('a'))# [<Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>, <Node a>]installpython3-mpipinstalladomCLITo see the version:domonic-vTo use css selectors on a website...adom-qhttps://google.coma
Package: ado-pipeline-helper
ADO Pipeline helperPython package and commandline tool for helping with writing Azure Devops pipelines.FeaturesNone of these are implemented mind you as of nowvalidate pipeline (load .azure-pipeline, resolve templates, send to run endpoint with yamlOverride and preview=True)validate library groups (see if value exists)MAYBE: validate schedule cronWarning about templating syntax errors (like missing $ before {{ }} )LimitationsCan't resolve{{ }}expressions, only simple{{ parameter.<key>}}ones.
I started working on a custom resovler but it was a lot of work. You can see it on the branchexpression resolverunderado_pipeline_helper/src/ado_pipeline_helper/resolver/expression.pyUseful linksADO Yaml Reference
Package: adopt-a-doodle
adopt-a-doodleA Python package to make the creation of Doodle actors easier!Dependenciesadopt_a_doodle depends on Panda3D. If you have not installed it already, you can do so with the following command:pip install Panda3DInstallingTo install the latest version of adopt_a_doodle, open your favorite command terminal and use the following command:pip install adopt_a_doodleIf for whatever reason you are unable to install adopt_a_doodle through pip, you can also install it through thelatest source distribution released on GitHub. Download the tar.gz file and open your favorite command terminal. Navigate to wherever the file was downloaded and run the following command:pip install [file]UsageWith adopt_a_doodle, the creation of Doodle actors becomes much easier.Like with any other Panda3D Toontown project, you must first extract the Phase Files. You can do so with the following command, with [x] being replaced by the id of the phase file:multify.exe -xf phase_[x].mfWith adopt_a_doodle, you will need phase_4, phase_5, and phase_5.5. Once these files are extracted, drop them into the same directory you want to have your Python files in. Your directory should look similar to this:| phase_4
| phase_5
| phase_5.5
| example_doodle.pyNext, go into phase_4/models and find TT_pets-mod.bam. This is the model file for doodles. Drop this file into your main working directory, which should now look like this:| phase_4
| phase_5
| phase_5.5
| example_doodle.py
| TT_pets-mod.bamNow that all the necessary files are here, you can open the Python file containing your scene and start to program! Here's an example scene:fromdirect.directbase.DirectStartimportbaseimportadopt_a_doodleexample_doodle=adopt_a_doodle.adopt(adopt_a_doodle.Doodle(color=(0.546875,0.28125,0.75,1.0),eye_color=(0.242188,0.742188,0.515625,1.0),pattern=adopt_a_doodle.Pattern(ears="phase_4/maps/BeanCatEar3Yellow.jpg",body="phase_4/maps/BeanbodyLepord2.jpg",legs="phase_4/maps/BeanFootYellow1.jpg",tail="phase_4/maps/BeanLongTailLepord.jpg"),animation=adopt_a_doodle.Animation(file="phase_5/models/char/TT_pets-speak.bam",anim_loop=True,loop_from=None,loop_to=None,loop_restart=None,pose=False,pose_frame=None),eyelashes=False,hair=None,ears="catEars",nose=None,tail="longTail"))example_doodle.setPos(0,5,-1.2)example_doodle.setH(180)example_doodle.reparentTo(render)base.run()This code will produce the following doodle:DocumentationYou can find documentation for adopt_a_doodle in therustydoodle lib.rs file.LicenseCode in adopt_a_doodle is licensed under theMIT License.
Package: adopy
ADOpyADOpyis a Python implementation of Adaptive Design Optimization (ADO; Myung, Cavagnaro, & Pitt, 2013), which computes optimal designs dynamically in an experiment. Its modular structure permit easy integration into existing experimentation code.ADOpy supports Python 3.6 or above and relies on NumPy, SciPy, and Pandas.FeaturesGrid-based computation of optimal designs using only three classes:adopy.Task,adopy.Model, andadopy.Engine.Easily customizable for your own tasks and modelsPre-implemented Task and Model classes including:Psychometric function estimation for 2AFC tasks (adopy.tasks.psi)Delay discounting task (adopy.tasks.ddt)Choice under risk and ambiguity task (adopy.tasks.cra)Example code for experiments using PsychoPy(link)Installation# Install from PyPIpipinstalladopy# Install from Github (developmental version)pipinstallgit+https://github.com/adopy/adopy.git@developResourcesGetting startedDocumentationBug reportsCitationIf you use ADOpy, please cite this package along with the specific version.
It greatly encourages contributors to continue supporting ADOpy.Yang, J., Pitt, M. A., Ahn, W., & Myung, J. I. (2020).
ADOpy: A Python Package for Adaptive Design Optimization.Behavior Research Methods, 1-24.https://doi.org/10.3758/s13428-020-01386-4AcknowledgementThe research was supported by National Institute of Health Grant R01-MH093838 to Mark A. Pitt and Jay I. Myung, the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT, & Future Planning (NRF-2018R1C1B3007313 and NRF-2018R1A4A1025891), the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-01367, BabyMind), and the Creative-Pioneering Researchers Program through Seoul National University to Woo-Young Ahn.ReferencesMyung, J. I., Cavagnaro, D. R., and Pitt, M. A. (2013).
A tutorial on adaptive design optimization.Journal of Mathematical Psychology, 57, 53–67.
Package: ado-py
ado-pyDo stuff with python. Quickly access functions from the command-line.Automate stuff and save time.Inspired by make. 😂To avoid repetition of lines in the terminal, we often create a make alias and callmake funcInmakeyou write stuff in shell, inado, you write in python.Installationpipinstallado-pyUsageCreate ado.pyfile in your directory.Write functions in it like thisNote: the functions indo.pyshould not take any arguments.
For user input useinput()function.Call any function from the terminal by runningado func.Running onlyadowould print the docstring ofdo.py.
Package: adorable
adorableMake the UI of your projectadorable.Basic UsageimportadorablefromadorableimportcolorRED=color.from_name("red")print(RED.fg("Hello adorable World"))BLUE=color.from_hex(0x0AF)DARK=color.from_rgb((38,38,38))col=BLUE.on(DARK)adorable.printc("Hello","World",style=col)LinksDocumentationSource CodePyPI
Package: adoreta
adoreta_loginstalling the packagepip install adoretaExamples:Logging your logsDefault name of the log filelogs.csvfrom adoreta.log import Log

log = Log()
log.write("Please log this text to show this in the future")Custom name of the log filefrom adoreta.log import Log

log = Log("custom_filename.csv") # any custom file name you want
log.write("This text will go in the custom named file")Displaying your logsDefault logs file displaylogs.csvfrom adoreta.log import Log

log = Log()
log.show()Custom logs file displayfrom adoreta.log import Log

log = Log("custom_filename.csv")
log.show()
Package: adorn
Featuresadornis a configuration tool for python code.adorncan currentlyinstantiate an objectcheck that a config can instantiate an objectExamplefromadorn.orchestrator.baseimportBasefromadorn.paramsimportParamsfromadorn.unit.compleximportComplexfromadorn.unit.constructor_valueimportConstructorValuefromadorn.unit.parameter_valueimportParameterValuefromadorn.unit.pythonimportPython@Complex.root()classExample(Complex):pass@Example.register(None)classParent(Example):def__init__(self,parent_value:str)->None:super().__init__()self.parent_value=parent_value@Parent.register("child")classChild(Parent):def__init__(self,child_value:int,**kwargs)->None:super().__init__(**kwargs)self.child_value=child_valuebase=Base([ConstructorValue(),ParameterValue(),Example(),Python()])params=Params({"type":"child","child_value":0,"parent_value":"abc"})# well specified configuration# we can type check from any level in the# class hierarchyassertbase.type_check(Example,params)isNoneassertbase.type_check(Parent,params)isNoneassertbase.type_check(Child,params)isNone# instantiate# we can instantiate from any level in the# class hierarchyexample_obj=base.from_obj(Example,params)assertisinstance(example_obj,Child)parent_obj=base.from_obj(Parent,params)assertisinstance(parent_obj,Child)child_obj=base.from_obj(Child,params)assertisinstance(child_obj,Child)InstallationYou can installAdornviapipfromPyPI:$pipinstalladornContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theApache 2.0 license,Adornis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: adotsdot
adotsdot (Almost Surely)PurposeProvide the ability to obtain the differences between two JSON serializable Python dictionaries (e.g., configuration files) while treating lists as unordered.** Supporting this functionality requires that the dictionaries in a list of dictionaries have a a field that can be used as a unique marker across all elements in the list.UsageAssumingprev_stateandcurr_stateare JSON serializable Python dictionaries,from adotsdot import diff

node = diff(prev_state, curr_state)will generate the root of a tree of nodes that represent the changes in state.This information can be displayed in the terminal via thestrprocedure or transformed into a JSON serializable Python dictionary vianode.as_dict().
Package: adpasswd
adpasswd.py: Pure Python Command line interface to change Active Directory Passwords via LDAP.SETUP:you need a config file.config files can either be in the Current Working Directory, or in ~/config files are always named .adpasswd.cfg and are INI style.Example:[ad]host: ad.blah.comport: 636binddn: cn=Administrator,CN=Users,DC=ad,DC=blah,DC=combindpw: changemequicklysearchdn: DC=ad,DC=blah,DC=comAll of the options above MUST exist, and be configured properly for this to work.Once you have a config file setup, then it's EASY to use:adpasswd.py username [password]you can call it with a password or not, if you don't you will be prompted for one.you get NO OUTPUT (but successful return) if everything went well. (good for scripts!)If things went wrong, you will be told about it.Bug reports, etc please use launchpad: https://launchpad.net/adpasswdCREDITS:ldaplib.py originally from scmgre@users.sourceforge.netURL: http://sourceforge.net/projects/ldaplibpy/Big thanks for doing all the hard work!FYI: I no longer use this code in production, nor really maintain it.  If you love/use or care about this code, feel free to adopt it or take over ownership.
Package: adp-connectors
Connectors for Discovery Platform ApplicationsThe current version supports 3 kind of API connections: AWS S3, Box, PostgreSQL.
The required secret file formats are as follows.S3 Connector{
  "aws_access_key_id": "",
  "aws_secret_access_key": ""
}Box Connector{
    "boxAppSettings": {
      "clientID": "",
      "clientSecret": "",
      "appAuth": {
        "publicKeyID": "",
        "privateKey": "",
        "passphrase": ""
        }
      },
      "enterpriseID": ""
    }Postgresql Connector{
  "host": "",
  "port": 5432,
  "database": "",
  "user": "",
  "password": ""
}
Package: adpred
ADpredA tool for prediction of Transcription Activation Domains from protein sequences.GoalsDocumentationContributingAuthorsLicenceGoalsThe main goal is to identify regions with high AD function probability in protein sequences. Moreover, at these observed regions, a saturated mutagenesis study can reveal insights into the important residues that confer the AD function to that region.ContributingContributions are welcome and encouraged.DocumentationAuthorsAriel ErijmanLicenceADpredis an open source software released under theMIT licence
Package: adpushup-adstxt
This library provides adpushup API for handling ads.txt management.Django:add it to installed apps:INSTALLED_APPS=(...,'adpushup_adstxt',...,)add your user_id and key to YOUR settings.py:ADPUSHUP_API_USER_ID='test@example.com'ADPUSHUP_API_KEY='1234'OPTIONALLY add different WWW_DIR:ADPUSHUP_WWW_DIR='/some/dir/to/put/ads.txt/in/it/'by default it is DjangosROOT_DIR + '/www'ADD it to your urlconf:fromadpushup_adstxt.django_viewsimporthandleurlpatterns+=patterns('',url(r'^adsTxtManagementApiByAdpushup.php',handle)),Testing:fromadpushup_adstxt.utilsimportencode_uri_componentimporttimeimporthmacimporthashlibimportrequestsuser_id='your user id'key='your key'req_time=int(time.time())hash_params="email={}&ts={}".format(encode_uri_component(user_id.encode("UTF-8")),req_time)hash=hmac.new(key,hash_params,hashlib.sha256).hexdigest()res=requests.post('http://localhost:8000/adsTxtManagementApiByAdpushup.php',dict(data='test content',ts=req_time,hash=hash))printres.status_codeprintres.content
Package: adp-webscrape
A Selenium-based python script for logging into ADP Resource and downloading reports.UsageInstallationWith Python 3.6 or greater installed, in a command prompt: enterpip installadp-webscrape. You’ll also need a recent edition of Firefox and its respectiveGeckoDriver. The GeckoDriver must be added to PATH, or to the root folder of the project.CodeUse the following code, replacing my_username, my_password, my_download_path, and my_isi_client_id with relevant information.my_username: Your ADP Resource usernamemy_password: Your ADP Resource passwordmy_download_path: (optional) The path that Selenium’s browser will download reports to (e.gC:\adp-reports). Ommiting defaults to the user’s download folder.my_isi_client_id: This can be found at the end of the url for any ezLaborManager page. Most likely, it’s going to be your company name (probably spaced out by hyphens if the name is multiple words).my_report_index: On the ezLaborManager “My Reports” page, this will be the index of the report you want to download (with the first report starting at index 0)https://i.imgur.com/Tg7kPQV.pngmy_file_prefix: (optional) If you’d like to prefix the name of your files with some word so as to not mix report names, you may do so here.importatexitfromadpwebscrapeimportADPResourceresource=ADPResource('my_username','my_password',isi_client_id='my_isi_client_id',download_path=r'my_download_path')resource.download_my_report(my_report_index,prefix='my_file_prefix')#returns Filenameatexit.register(resource.quit)OtherWhy no official API?There isn’t one. ADP Marketplace has an API, though it is very separate from the reports I’ve attempted to generate here.Why Selenium and not regular schmegular requests?Requests to ADP Resource require hidden fields whose contents seem like a pain to generate programatically. Selenium was chosen because it handles all of that at the cost of a little performance. Please let me know if you find a better way to do this.
Package: adqol-persist
# ADQ Persistence Framework for Dynamo DBThis project was made to optimize the Dynamo DB Persistence implementation code to a basic Entity CRUD operations for Python ADQ projects[The source for this project is available here][src].Most of the configuration for a Python project is done in thesetup.pyfile,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.This is the README file for the project.The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, 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.[packaging guide]:https://packaging.python.org[distribution tutorial]:https://packaging.python.org/tutorials/packaging-projects/[src]:https://github.com/jhmjesus/adqol-persist[rst]:http://docutils.sourceforge.net/rst.html[md]:https://tools.ietf.org/html/rfc7764#section-3.5“CommonMark variant”
[md use]:https://packaging.python.org/specifications/core-metadata/#description-content-type-optional