Saravanan666/pypi-org huggingface dataset:
Package: oc-validator
oc_validatoroc_validatoris a Python (≥3.9) library to validate CSV documents storing citation data and bibliographic metadata.
To be processed by the validator, the tables must be built as either CITS-CSV or META-CSV tables, defined in two
specification documents[^1][^2].[^1]: Massari, Arcangelo, and Ivan Heibi. 2022. ‘How to Structure Citations Data and Bibliographic Metadata in the OpenCitations Accepted Format’.https://doi.org/10.48550/arXiv.2206.03971.[^2]: Massari, Arcangelo. 2022. ‘How to Produce Well-Formed CSV Files for OpenCitations’.https://doi.org/10.5281/zenodo.6597141.InstallationThe library can be installed frompip:pip install oc_validatorUsageThe validation process can be executed from the CLI, by running the following command:python -m oc_validator.main -i <input csv file path> -o <output dir path>An object of theValidatorclass is instantiated, passing as parameters the path to the input document to validate and the
path to the directory where to store the output. By calling thevalidate()method on the instance ofValidator,
the validation process gets executed.The process automatically detects which of the two tables has been passed as input (on condition that the input
CSV document's header is formatted correctly for at least one of them). During the process, thewholedocument is always processed:
if the document is invalid or contains anomalies, the errors/warnings are reported in detail in a JSON file
and summarized in a .txt file, which will be automatically created in the output directory.validatealso returns a list
of dictionaries corresponding to the JSON validation report (empty if the document is valid).v = Validator('path/to/table.csv', 'output/directory')
v.validate()
Package: oc-wp
Changes:0.2 (27-7-2009)Summary page viewlet now accesses WordPress by URLs that are internal to the system,
to save on bandwidth, time and other good things of the earth. Seehttp://www.coactivate.org/projects/opencore/lists/opencore-dev/archive/2009/07/1247921892729/forum_viewfor more details. (egj)
Package: ocx-client
A client library for the bitcoin exchange OCX
—# Install` pip install ocx_client `# Usage
- Rest API Client:https://github.com/ocxapi/ocx-openapi-python-client/blob/master/client_sample.py- Streaming API Client:https://github.com/ocxapi/ocx-openapi-python-client/blob/master/streaming_client_sample.py# Licence
LGPL
Package: ocx-databinding
ocx-databindingCLI python script for managing theOCX schemadatabinding.
See the documentation ofxsdatafor details on creating python databindings.Installationpip install ocx_databindingChangesCHANGELOGUsagedatabinding --help
Usage: databinding [OPTIONS] COMMAND [ARGS]...

Options:
  --install-completion  Install completion for the current shell.
  --show-completion     Show completion for the current shell, to copy it or
                        customize the installation.
  --help                Show this message and exit.

Commands:
  generate  Generate code from xml schemas, webservice definitions and...
  version   Print the version number and exit.Generate a datbinding:Usage: databinding generate [OPTIONS] SOURCE PACKAGE SCHEMA_VERSION

  Generate code from xml schemas, webservice definitions and any xml or json
  document. The input source can be either a filepath, uri or a directory
  containing  xml, json, xsd and wsdl files.

Arguments:
  SOURCE          [required]
  PACKAGE         [required]
  SCHEMA_VERSION  [required]

Options:
  --docstring-style TEXT        [default: Google]
  --structure-style TEXT        [default: single-package]
  --slots / --no-slots          [default: slots]
  --recursive / --no-recursive  [default: no-recursive]
  --help                        Show this message and exit.ExampleGenerate the databindings from the unitsML schema url:databinding generate https://3docx.org/fileadmin/ocx_schema/unitsml/unitsmlSchema_lite-0.9.18.xsd unitsml 0.9.18

2023-09-04 11:17:28.705 | INFO     | ocx_databinding.generator:generate:64 - New databinding package name is unitsml_0918 with version: 0.9.18 is created in C:\PythonDev\ocx-generator\unitsml
========= xsdata v23.8 / Python 3.11.5 / Platform win32 =========

Parsing schema https://3docx.org/fileadmin/ocx_schema/unitsml/unitsmlSchema_lite-0.9.18.xsd
Parsing schema file:///C:/miniconda3/envs/generator/Lib/site-packages/xsdata/schemas/xml.xsd
Compiling schema file:///C:/miniconda3/envs/generator/Lib/site-packages/xsdata/schemas/xml.xsd
Builder: 5 main and 2 inner classes
Compiling schema https://3docx.org/fileadmin/ocx_schema/unitsml/unitsmlSchema_lite-0.9.18.xsd
Builder: 38 main and 2 inner classes
Analyzer input: 43 main and 4 inner classes
Analyzer output: 35 main and 0 inner classes
Generating package: init
Generating package: unitsml_0918This has now generated a subdirectoryunitsmlwith the following structure:C:.
└───unitsml
    └───unitsml_0918and with the content:Length Name
   ------ ----
    29577 unitsml_0918.py
     2145 xsdata.xml
     1531 __init__.pyThe CLI generates a single  package, meaning that any imported schemas are ignored, seexsdataif more detailed control of the databinding is necessary.
The databindings are also versioned by creating a package name with the version string. This allows us to have multiple databindings representing different schema versions.API documentationAPI
Package: ocx-schema-parser
ocx-schema-parserA Python library for parsing an OCX schema and providing documentation of the schema gloabla elements and attributes.InstallationInstall using pip:pip install ocx_schema_parser:latestAPIThe API documentation is autogenerated usingsphinxand the Read The Docs theme:https://ocxstandard.github.io/ocx-schema-parser/ChangelogChangelog
Package: ocxtools
ocxtoolsA python CLI to work with 3Docx models.ocxtoolsis a configurable Python CLI shell with pluggable subcommands. The CLI combines features fromclick,click-shell,typerandRichwith emphasis onTyperandRichto make a good-looking CLI.Installationinstall from pypi:pip install ocxtoolsUsageThe main CLI,----..
            /   /   \    ,----..   ,--,     ,--,
           /   .     :  /   /   \  |'. \   / .`|
          .   /   ;.  \|   :     : ; \ `\ /' / ;
         .   ;   /  ` ;.   |  ;. / `. \  /  / .'          ______   ___    ___   _     _____
         ;   |  ; \ ; |.   ; /--`   \  \/  / ./          |      | /   \  /   \ | |   / ___/
         |   :  | ; | ';   | ;       \  \.'  /     _____ |      ||     ||     || |  (   \_
         .   |  ' ' ' :|   : |        \  ;  ;     |     ||_|  |_||  O  ||  O  || |___\__  |
         '   ;  \; /  |.   | '___    / \  \  \    |_____|  |  |  |     ||     ||     /  \ |
          \   \  ',  / '   ; : .'|  ;  /\  \  \            |  |  |     ||     ||     \    |
           ;   :    /  '   | '/  :./__;  \  ;  \           |__|   \___/  \___/ |_____|\___|
            \   \ .'   |   :    / |   : / \  \  ;
             `---`      \   \ .'  ;   |/   \  ' |
                         `---`    `---'     `--`

Version: 1.1.0
Copyright (c) 2024. OCX Consortium (https://3docx.org)

Starting ocxtools...
ocxtools >: help

Documented commands (type help <topic>):
========================================
clear  docker  validate  version

Undocumented commands:
======================
exit  help  quit


ocxtools >:Shell CompletionCommand completion is not supported on Windows installations.Sub-commandsdockervalidateChangelogChangelogAPIThe API documentation is autogenerated usingsphinxand the Read The Docs theme:https://ocxstandard.github.io/ocxtools/
Package: ocx_unitsml
UnitsML XML unit markup schemaIntroductionThis schema wasoriginally developed and published by NIST.
The schema is used by theOCX schemawhich is managed by theOCX Consortium.
The UnitsML schema is publihsed by the OCX Consortium under the originalLICENSE.ChangelogCHANGELOG.mdProject structureRoot
└──ocx_unitsml         
   └───ocx_unitsml_0918How to contributeThe schema file is maintained using an XML editor like Altova XMLSpy.Installation: Simply download the schema files from the repository or clone it using git.Create anIssuefor the proposed schema change.
Followthe recipe on the Wikion how to register an issue.Then create a branch from the issue, check out the branch and implement the proposed changes.When the implementation is completed, create apull request.The pull request will have to be approved by theOCXStandard maintainerbefore it can be included in the working draft.Schema versioningThe UnitsML schema is following the PythonPEP 440 versioning recommendations.Following PEP 440, the canonical public version identifiers MUST comply with the following scheme:[N!]N(.N)*[{a|b|rc}N][.postN][.devN]see thePEP 440for a detailed explanation.tbumpversioning toolThe Pythontbumptool is used to automate and bump schema versions.tbumpalso provides the possibility for pre- and post-commit actions.
The project config filepyproject.tomlcontains the tbump settings.UsageAssume that the current schema version is0.9.18. To bump the schema version to a new version1.0.0version, the following command can be issued:> tbump 1.0.0Naive databindingA set of Python dataclasses are autogenerated after each version release, seedatabindingsGenerating databindingsPublic LicenseThe UnitsML standard is governed by the OASIS Intellectual Property Rights Policy (the License).Read the LicenseUnless required by applicable law or agreed to in writing, the UnitsML standard and any derivative works re-distributed under the License
is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
The OCX Consortium is not liable to any use whatsoever of the distributed standard or derivative works based on the standard.CreditsNISTfor the original schema development andOASIS UnitsML TCfor publishing the schema version1.0in 2011.
Package: ocx_versioning
ocx-versioningUtility python scripts for managing OCX xsdata databinding versioning according to PEP 440.
Seexsdatafor details on python xml databindings.Installationpip install ocx_versioningUsage> python -m ocx_versioning --help
Usage: ocx_versioning "module_name" "version_string"

--help: Prints this message and exits.
--version: Prints the version number and exits.
>
> python -m ocx 1.0.0
Updating the configuration file xsdata.xml in module ocx
New package name is ocx_100 with version: 1.0.0
Package: od
Theodpackage adds a shorthand syntax to create instances ofOrderedDict>>> import od
>>>
>>> od["cat": "fish", "mouse": "cheese"]
OrderedDict([('cat', 'fish'), ('mouse', 'cheese')])
>>> od["mouse": "cheese", "cat": "fish"]
OrderedDict([('mouse', 'cheese'), ('cat', 'fish')])You can also use it like the regular constructor forOrderedDict:>>> od()
OrderedDict()
>>> an_iterable = [("cat", "fish"), ("mouse", "cheese")]
>>> od(an_iterable)
OrderedDict([('cat', 'fish'), ('mouse', 'cheese')])Install this package usingpip:$ pip install --user od
Package: odach
ODAch, An Object Detection TTA tool for PytorchODA is a test-time-augmentation (TTA) tool for 2d object detectors.For use in Kaggle object detection competitions.:star: if it helps you! ;)Installpip install odachUsageSeeExample.ipynb.The setup is very simple, similar tottach.Singlescale TTAimportodachasoda# Declare TTA variationstta=[oda.HorizontalFlip(),oda.VerticalFlip(),oda.Rotate90(),oda.Multiply(0.9),oda.Multiply(1.1)]# load imageimg=loadimg(impath)# wrap model and ttatta_model=oda.TTAWrapper(model,tta)# Execute TTA!boxes,scores,labels=tta_model(img)Multiscale TTAimportodachasoda# Declare TTA variationstta=[oda.HorizontalFlip(),oda.VerticalFlip(),oda.Rotate90(),oda.Multiply(0.9),oda.Multiply(1.1)]# Declare scales to ttascale=[0.8,0.9,1,1.1,1.2]# load imageimg=loadimg(impath)# wrap model and ttatta_model=oda.TTAWrapper(model,tta,scale)# Execute TTA!boxes,scores,labels=tta_model(img)The boxes are also filtered by nms(wbf default).The image size should be square.model output wrappingWrap your detection model so that the output is similar to torchvision frcnn format:
[["box":[[x,y,x2,y2], [], ..], "labels": [0,1,..], "scores": [1.0, 0.8, ..]]Example for EfficientDetshttps://www.kaggle.com/kyoshioka47/example-of-2d-single-scale-tta-with-odach/# wrap effdetoda_effdet=oda.wrap_effdet(effdet)# Declare TTA variationstta=[oda.HorizontalFlip(),oda.VerticalFlip(),oda.Rotate90()]# Declare scales to ttascale=[1]# wrap model and ttatta_model=oda.TTAWrapper(oda_effdet,tta,scale)ExampleGlobal Wheat DetectionExample notebookThanksnms, wbf are fromhttps://kaggle.com/zfturbotta is based onhttps://github.com/qubvel/ttach,https://github.com/andrewekhalel/edafa/tree/master/edafaandhttps://www.kaggle.com/shonenkov/wbf-over-tta-single-model-efficientdet
Package: odap
ODAP Use Case Builder FrameworkOverviewODAP is a lightweight framework for creating use cases, writing features and exporting
segments to various destinations (e.g. Facebook, Salesforce, etc.)Both SQL and Pyspark syntax is supported.It's build on top of the Databricks platform.You can try the framework right now by cloningdemo projectto your Databricks Workspace.DocumentationFor documentation seeODAP Documentation.DevelopmentThere are two main components (sub-packages)feature_factory- responsible for features development and orchestrationsegment_factory- responsible for segments creation and exportsDBR & PythonDBR 10.4+ with python 3.8+ are supportedDependency managementUsepoetryas main dependency management toolLinting & Formattingpylintpyre-checkblackCode stylefunctions-only python (no dependency injection)try to avoid classes as much as possibledata classes are OKno__init__.pyfileskeep thesrcdirectory in rootproject config is raw YAMLuse type hinting as much as possible
Package: odap-adform-downloader
DataSentics Python package for downloading adform dataThis package is distributed under the "DataSentics SW packages Terms of Use." SeelicenseDownloads data from adform in zip filesYou need to specify:Credentials (name, setup_id, client_id, secret)Optionally you can specify scope, which data from Adform you would like to download, dates and file directory$pipinstalladform-downloader

fromadform.ClientimportClient,FilePersistingHandler
fromadform.CredentialsimportCredentialssetup_id=""client_id=""client_secret=""# optionally can be added into credentials. Written with default valuesfile="/tmp/"# for state filescopes=["https://api.adform.com/scope/buyer.masterdata","https://api.adform.com/scope/eapi","https://api.adform.com/scope/buyer.rtb.lineitem",]tables=["Click","Event","Impression","Trackingpoint","meta"]start_day=today-8days,# format dd-mm-yyyyend_day=today,landing_file="/tmp/"# for data, can be same as filepersisting_handler=FilePersistingHandler(location=file)credentials=Credentials(setup_id=setup_id,client_id=client_id,client_secret=client_secret)client=Client(credentials,persisting_handler)client.download_masterdata_files_by_id()
Package: odap-adfrom-downloader
DataSentics Python package for downloading adform dataThis package is distributed under the "DataSentics SW packages Terms of Use." SeelicenseDownloads data from adform in zip filesYou need to specify:Credentials (name, setup_id, client_id, secret)Optionally you can specify scope, which data from Adform you would like to download, dates and file directory$pipinstalladform-downloader

fromadform.ClientimportClient,FilePersistingHandler
fromadform.CredentialsimportCredentialssetup_id=""client_id=""client_secret=""# optionally can be added into credentials. Written with default valuesfile="/tmp/"# for state filescopes=["https://api.adform.com/scope/buyer.masterdata","https://api.adform.com/scope/eapi","https://api.adform.com/scope/buyer.rtb.lineitem",]tables=["Click","Event","Impression","Trackingpoint","meta"]start_day=today-8days,# format dd-mm-yyyyend_day=today,landing_file="/tmp/"# for data, can be same as filepersisting_handler=FilePersistingHandler(location=file)credentials=Credentials(setup_id=setup_id,client_id=client_id,client_secret=client_secret)client=Client(credentials,persisting_handler)client.download_masterdata_files_by_id()
Package: odapdbxbundle
ODAP Databricks BundleOverviewODAP Databricks Bundle is a bundle containing connectors to various data sources to make importing and exporting of data easier.ODAP Databricks Bundle supports metadata driven ingestion and exports of tables and filesIt's build on top of the Databricks platform.DocumentationTODODBR & PythonDBR 10.4+ with python 3.8+ are supportedDependency managementUsepoetryas main dependency management toolLinting & Formattingpylintpyre-checkblackCode stylefunctions-only python (no dependency injection)try to avoid classes as much as possibledata classes are OKno__init__.pyfileskeep thesrcdirectory in rootproject config is raw YAMLuse type hinting as much as possible
Package: odap-export-orchestrator
ODAP EXPORT ORCHESTRATORThis package is distributed under the "DataSentics SW packages Terms of Use." SeelicenseThis module exports data based on config sent from Persona application backend. It’s part of the application, it exports data from featurestore, etc to inmemory dataset of application. It’s scheduled from backend of the application and also all information needed to make correct export get from backend in form of json.
Package: odap-ga-downloader
Persona 360 Google Analytics 360This package is distributed under the "DataSentics SW packages Terms of Use." SeelicenseComponent, using Google Analytics API, allows you to download raw data used in Persona360 service.Long Description: This component uses the Google Analytics API to extract raw data via BigQuery and Google Cloud Platfom storage to your define storage. These data are used in Persona360 service.
Data are extracted incrementally.
To configure this extractor, you need to have Google Cloud Platform storage and  appropriate credentials.
Package: odapt
odaptThe odapt library is a bridge between columnar file formatsROOT, Parquet, Feather, and HDF5.It aims to simplify file conversions in Python, replacing what is usually a multi-step process with one line of code, with builtin features for managing large datasets and choosing compression levels.Installationodapt can be installed fromPyPIusing pip:pipinstallodaptGetting startedimportodaptasod# To merge two or more root files with TTrees,# and add together any histograms:od.hadd_and_merge("destination.root",["ttree_file1.root","ttree_file2.root"])# To add merge files with only histograms:od.hadd("destination.root",["hist_file1.root","hist_file2.root"])
Package: odata-pretty-printer
odata-pretty-printerPretty printer for OData filter expressions.This is an example of usingoppen-pretty-printto create a pretty-printer for
OData filter expressions.This is not a robust program, just a bench tool that's used occasionally to pretty-print
complex OData filters that would otherwise be unreadable. The input filter is expected to be
grammatically correct, just untidy. Pretty error reporting is non-existent!Usagepip install odata-pretty-printer

odata-printer  "ShowOnSite eq true and PublicationDate le 2020-04-29T08:49:33Z \
                and PublicationDate ge 2019-04-29T08:49:33Z and ChannelId eq 23 \
                and (ExpiryDate eq null or ExpiryDate ge 2020-04-29T08:49:33Z) and \
                IsAdvisor eq true and (FundCodeList/any(fundcode: fundcode eq 'ITSMT') \
                or SectorCodeList/any(sectorcode: sectorcode eq 'T:IG') \
                or GroupCodeList/any(groupcode: groupcode eq 'BAIL'))"

odata-printer -f tmp/filter_example.txtOutput:ShowOnSite eq true
  and PublicationDate le 2020-04-29T08:49:33Z
  and PublicationDate ge 2019-04-29T08:49:33Z
  and ChannelId eq 23
  and (ExpiryDate eq null or ExpiryDate ge 2020-04-29T08:49:33Z)
  and IsAdvisor eq true
  and (FundCodeList/any(fundcode: fundcode eq 'ITSMT')
        or SectorCodeList/any(sectorcode: sectorcode eq 'T:IG')
        or GroupCodeList/any(groupcode: groupcode eq 'BAIL'))DocumentationHead over to theREADMEfor more details.ContributingThe source code forodata-pretty-printeris availableon GitHub. If you find a bug or something is unclear, we encourage
you to raise an issue. We also welcome contributions, to contribute, fork the
repository and open apull request.
Package: odata-query
odata-queryis a library that parsesOData v4filter strings, and can
convert them to other forms such asDjango Queries,SQLAlchemy Queries,
or just plain SQL.Installationodata-queryis available on pypi, so can be installed with the package manager
of your choice:pipinstallodata-query# ORpoetryaddodata-query# ORpipenvinstallodata-queryThe package defines the following optionalextra’s:django: If you want to pin a compatible Django version.sqlalchemy: If you want to pin a compatible SQLAlchemy version.The followingextra’s relate to the development of this library:linting: The linting and code style tools.testing: Packages for running the tests.docs: For building the project documentation.You can installextra’s by adding them between square brackets during
installation:pipinstallodata-query[sqlalchemy]QuickstartThe most common use case is probably parsing an OData query string, and applying
it to a query your ORM understands. For this purpose there is an all-in-one function:apply_odata_query.Example for Django:fromodata_query.djangoimportapply_odata_queryorm_query=MyModel.objects# This can be a Manager or a QuerySet.odata_query="name eq 'test'"# This will usually come from a query string parameter.query=apply_odata_query(orm_query,odata_query)results=query.all()Example for SQLAlchemy ORM:fromodata_query.sqlalchemyimportapply_odata_queryorm_query=select(MyModel)# This is any form of Query or Selectable.odata_query="name eq 'test'"# This will usually come from a query string parameter.query=apply_odata_query(orm_query,odata_query)results=session.execute(query).scalars().all()Example for SQLAlchemy Core:fromodata_query.sqlalchemyimportapply_odata_corecore_query=select(MyTable)# This is any form of Query or Selectable.odata_query="name eq 'test'"# This will usually come from a query string parameter.query=apply_odata_core(core_query,odata_query)results=session.execute(query).scalars().all()Advanced UsageNot all use cases are as simple as that. Luckily,odata-queryis modular
and extendable. See thedocumentationfor advanced usage or extending the
library for other cases.ContactGot any questions or ideas? We’d love to hear from you. Check out ourcontributing guidelinesfor ways to offer feedback and
contribute.LicenseCopyright ©Gorillini NV.
All rights reserved.Licensed under the MIT License.
Package: odb2vtk
odb2vtkA python wrapper package for theODB2VTKlibrary.Installationpipinstallodb2vtkUsageUse theodb2vtkcommand to convert an ODB file to a VTK file.$odb2vtk--help

usage:odb2vtk.py[-h][--headerHEADER][--instance[INSTANCE[INSTANCE...]]][--step[STEP[STEP...]]][--writeHistoryWRITEHISTORY]--odbFileODBFILE[--writePVDWRITEPVD][--suffixSUFFIX]optionalarguments:-h,--helpshowthishelpmessageandexit--headerHEADERif1,extractheaderinformationandgeneratea.jsonfile.Otherwise,generate.vtufile.Bydefault0--instance[INSTANCE[INSTANCE...]]selectedinstancenameswhichareseparatedbywhitespace,e.g.'instanceName1''instanceName2'--step[STEP[STEP...]]selectedstepnamesandframeswhichareseparatedbywhitespace,e.g.,'step1:1,2,3''step2:2,3,4'.TheframescanbeanyvalidPythonexpressionthatcanbeevaluatedtoanintegeriterable,e.g.,'range(1, 10)'--writeHistoryWRITEHISTORYif1,writehistoryoutput.--odbFileODBFILEselectedodbfile(fullpathname)--writePVDWRITEPVDif1writeapvdfile--suffixSUFFIXstringappendedtothefileUse theodb2vtk-multiprocesscommand for the multiprocessing version.$odb2vtk-multiprocess--help

usage:odb2vtk-multiprocess[-h][--headerHEADER][--instance[INSTANCE...]][--step[STEP...]][--writeHistoryWRITEHISTORY]--odbFileODBFILE[--suffixSUFFIX]options:-h,--helpshowthishelpmessageandexit--headerHEADERif1,extractheaderinformationandgeneratea.jsonfile.Otherwise,generate.vtufile.Bydefault0--instance[INSTANCE...]selectedinstancenameswhichareseparatedbywhitespace,e.g.'instanceName1''instanceName2'--step[STEP...]selectedstepnamesandframeswhichareseparatedbywhitespace,e.g.,'step1:1,2,3''step2:2,3,4'.TheframescanbeanyvalidPythonexpressionthatcanbeevaluatedtoanintegeriterable,e.g.,'range(1, 10)'--writeHistoryWRITEHISTORYif1,writehistoryoutput.--odbFileODBFILEselectedodbfile(fullpathname)--suffixSUFFIXstringappendedtothefile