Saravanan666/pypi-org huggingface dataset:
Package: wbddh
WBDDHWBDDH is a Python wrapper for World Bank Data Catalog Web APIs. It can be used for both DDH API v1.0 and v2.0.DDH API 1.0 admin (PROD):https://ddhinboundapi.ase.worldbank.org/swagger/ui/indexDDH API 2.0 admin (UAT):https://ddhinboundopenapiuat.asestg.worldbank.org/docs/index.htmlDDH API 2.0 admin (PROD):https://ddhinboundopenapiprd.ase.worldbank.org/docs/index.htmlDDH API 2.0 public (UAT):https://ddhextopenapiuat.asestg.worldbank.org/docs/index.htmlSetupPublic users:pipinstall-UwbddhDDH administrators:pipinstall-Uwbddh[admin]UsageWBDDH handles HTTP requests and manages MSAL sessions.Users first have to set API_host before using other functions:importwbddh
wbddh.set_api_host(url)Admin users first have to create a session to further run other admin functions:importwbddhsession=wbddh.create_session(name='default_ddh_session',params=None,autoCopy=True,verify=None)Users can send a simple GET/POST request:wbddh.get(endpoint,params=None,headers=None,session=None)wbddh.post(endpoint,params=None,json=None,headers=None,session=None)Alternatively, users can repeat sending a GET/POST request until it succeeds or it tries {num_try} times:wbddh.try_get(endpoint,params=None,headers=None,session=None,num_try=3,interval=300)wbddh.try_post(endpoint,params=None,json=None,headers=None,session=None,num_try=3,interval=300)There are two helper functions that fetch all data for endpoints that have paging parameters (ex. search, datasets):wbddh.get_all_in_generator(endpoint,params=None,top_key="top",skip_key="skip",headers=None,session=None)wbddh.get_all_in_list(endpoint,params=None,top_key="top",skip_key="skip",headers=None,session=None)ExamplesJupyter Notebooks under examples folder show basic use cases utilizing wbddh.
Package: wbia-deepsense
Deepsense Plug-in - Part of the WildMe / Wildbook IA Project.A plug-in for the deepsense dolphin ID algorithm.Installation./run_developer_setup.shCode Style and Development GuidelinesContributingIt’s recommended that you usepre-committo ensure linting procedures are run
on any commit you make. (See alsopre-commit.com)Referencepre-commit’s installation instructionsfor software installation on your OS/platform. After you have the software installed, runpre-commitinstallon the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:gitadd.pre-commitrun# orpre-commitrun--all-filesBrunetteOur code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).Flake8Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.To run flake8 from the command line use:flake8This will use the flake8 configuration withinsetup.cfg,
which ignores several errors and stylistic considerations.
See thesetup.cfgfile for a full and accurate listing of stylistic codes to ignore.PyTestOur code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:pytest
Package: wbia-finfindr
FinFindR Plug-in - Part of the WildMe / Wildbook IA Project.A plug-in for the finFindR dolphin ID algorithm.Installation./run_developer_setup.shCode Style and Development GuidelinesContributingIt’s recommended that you usepre-committo ensure linting procedures are run
on any commit you make. (See alsopre-commit.com)Referencepre-commit’s installation instructionsfor software installation on your OS/platform. After you have the software installed, runpre-commitinstallon the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:gitadd.pre-commitrun# orpre-commitrun--all-filesBrunetteOur code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).Flake8Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.To run flake8 from the command line use:flake8This will use the flake8 configuration withinsetup.cfg,
which ignores several errors and stylistic considerations.
See thesetup.cfgfile for a full and accurate listing of stylistic codes to ignore.PyTestOur code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:pytest
Package: wbia-kaggle7
kaggle7 Plug-in - Part of the WildMe / Wildbook IA Project.A plug-in for the Kaggle7 humpback fluke ID algorithm.Installation./run_developer_setup.shCode Style and Development GuidelinesContributingIt’s recommended that you usepre-committo ensure linting procedures are run
on any commit you make. (See alsopre-commit.com)Referencepre-commit’s installation instructionsfor software installation on your OS/platform. After you have the software installed, runpre-commitinstallon the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:gitadd.pre-commitrun# orpre-commitrun--all-filesBrunetteOur code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).Flake8Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.To run flake8 from the command line use:flake8This will use the flake8 configuration withinsetup.cfg,
which ignores several errors and stylistic considerations.
See thesetup.cfgfile for a full and accurate listing of stylistic codes to ignore.PyTestOur code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:pytest
Package: wbia-lca
Local CLusters and their Alternatives (LCA) plug-in for WBIA - Part of the WildMe / Wildbook IA Project.RequirementsPython 3.5+Python dependencies listed in requirements.txtInstallation InstructionsPyPIThe WBIA software is now available onpypifor Linux systems. This means if you havePython installed. You can
simply run:pipinstallwbia_lcato install the software.We highly recommend using a Python virtual environment:https://docs.python-guide.org/dev/virtualenvs/#lower-level-virtualenvCitationIf you use this code or its models in your research, please cite:@article{berger2017wildbook,
    title={Wildbook: Crowdsourcing, computer vision, and data science for conservation},
    author={Berger-Wolf, Tanya Y and Rubenstein, Daniel I and Stewart, Charles V and Holmberg, Jason A and Parham, Jason and Menon, Sreejith and Crall, Jonathan and Van Oast, Jon and Kiciman, Emre and Joppa, Lucas},
    journal={arXiv preprint arXiv:1710.08880},
    year={2017}
}DocumentationThe WBIA API Documentation can be found here:https://wbia-lca.readthedocs.io/en/latest/Code Style and Development GuidelinesContributingIt’s recommended that you usepre-committo ensure linting procedures are run
on any commit you make. (See alsopre-commit.com)Referencepre-commit’s installation instructionsfor software installation on your OS/platform. After you have the software installed, runpre-commitinstallon the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:gitadd.pre-commitrun# orpre-commitrun--all-filesBrunetteOur code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).Flake8Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.To run flake8 from the command line use:flake8This will use the flake8 configuration withinsetup.cfg,
which ignores several errors and stylistic considerations.
See thesetup.cfgfile for a full and accurate listing of stylistic codes to ignore.PyTestOur code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:pytest
Package: wbia-lightnet
LIGHTNETBuilding blocks to recreate Darknet networks in PytorchWhy another frameworkpytorch-yolo2is working perfectly fine, but does not easily allow a user to modify an existing network.
This is why I decided to create a library, that gives the user all the necessary building blocks, to recreate any darknet network.
This library has everything you need to control your network, weight loading & saving, datasets, dataloaders and data augmentation.InstallingFirst installPyTorch and Torchvision.
Then clone this repository and run one of the following commands:# If you just want to use Lightnetpipinstall-rrequirements.txt# If you want to develop Lightnetpipinstall-rdevelop.txtThis project is python 3.6 and higher so on some systems you might want to use 'pip3.6' instead of 'pip'How to useClick Herefor the API documentation and guides on how to use this library.
Theexamplesfolder contains code snippets to train and test networks with lightnet. For examples on how to implement your own networks, you can take a look at the files inlightnet/models.If you are using a different version than the latest,
you can generate the documentation yourself by runningmake clean htmlin thedocsfolder.
This does require some dependencies, like Sphinx.
The easiest way to install them is by using the-r develop.txtoption when installing lightnet.CiteIf you use Lightnet in your research, please cite it.@misc{lightnet18,
  author = {Tanguy Ophoff},
  title = {Lightnet: Building Blocks to Recreate Darknet Networks in Pytorch},
  howpublished = {\url{https://gitlab.com/EAVISE/lightnet}},
  year = {2018}
}Main ContributorsHere is a list of people that made noteworthy contributions and helped to get this project where it stands today!Tanguy OphoffJohn Crall
Package: wbia-pyflann
FLANN - Fast Library for Approximate Nearest Neighbors! - Part of the WildMe / Wildbook IA Project.This is a Fork of the FLANN repo, under a different name for use in the Wildbook
project. The main difference is that it has a few more helper function calls
and it should be easier build wheels and to pip install.FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.
FLANN is written in C++ and contains bindings for the following languages: C, MATLAB, Python, and Ruby.DocumentationCheck FLANN web page [here](http://www.cs.ubc.ca/research/flann).Documentation on how to use the library can be found in the doc/manual.pdf file included in the release archives.More information and experimental results can be found in the following paper:Marius Muja and David G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”, in International Conference on Computer Vision Theory and Applications (VISAPP’09), 2009 [(PDF)](http://people.cs.ubc.ca/~mariusm/uploads/FLANN/flann_visapp09.pdf) [(BibTex)](http://people.cs.ubc.ca/~mariusm/index.php/FLANN/BibTex)Getting FLANNIf you want to try out the latest changes or contribute to FLANN, then it’s recommended that you checkout the git source repository:git clone git://github.com/mariusmuja/flann.gitIf you just want to browse the repository, you can do so by going [here](https://github.com/mariusmuja/flann).Build and InstallationThis package requires the following system dependencies:lz4 (in debian as liblz4)pkg-config (in debian as pkg-config)gcc (use build-essential in debian)For development use therun_develop_setup.shscript.Conditions of useFLANN is distributed under the terms of the [BSD License](https://github.com/mariusmuja/flann/blob/master/COPYING).
Package: wbia-pyhesaff
Hessian Affine + SIFT keypoints in Python! - Part of the WildMe / Wildbook IA Project.This is an implementation of Hessian-Affine detector.The implementation uses a Lowe’s (Lowe 1999, Lowe 2004) like pyramid
to sample Gaussian scale-space and localizes local extrema of the
Detetminant of Hessian Matrix operator computed on normalized
derivatives. Then a Baumberg-Lindeberg discovery of a local affine
shape is employed (Lindeberg 1998, Baumberg 2000, Mikolajzyk 2002) to
compute affine shape of each det of Hessian extrema. Finally a local
neighbourhood is normalized to a fixed size patch and SIFT
descriptor(Lowe 1999, Lowe 2004) computed.IMPLEMENTATIONImplementation depends on OpenCV (2.3.1+). Although, the code is
original, the affine iteration and normalization was derived from the
code of Krystian Mikolajczyk.The SIFT descriptor code was patented under a US Patent 6,711,293, which
expired on March 7th 2019, so the license is no longer required for use.OUTPUTNOTE THIS IS NO LONGER THE CASE. WE MAY REINSTATE THIS.The built binary rewrites output file: <input_image_name>.hesaff.siftThe output format is compatible with the binaries available from the
page “Affine Covariant Features”. The geometry of an affine region is
specified by: u,v,a,b,c in a(x-u)(x-u)+2b(x-u)(y-v)+c(y-v)(y-v)=1. The
top left corner of the image is at (u,v)=(0,0). The geometry of an
affine region is followed by N descriptor values (N = 128).File format:N
m
u1 v1 a1 b1 c1 d1(1) d1(2) d1(3) ... d1(N)
      :
      :
um vm am bm cm dm(1) dm(2) dm(3) ... dm(N)PROPER USEIf you use this code, please refer toPerdoch, M. and Chum, O. and Matas, J.: Efficient Representation of
Local Geometry for Large Scale Object Retrieval. In proceedings of
CVPR09. June 2009.TBD: A reference to technical report describing the details and some
retrieval results will be placed here.NOTESRequires opencv. On ubuntu you can:sudoapt-getinstalllibopencv-dev. You can also build / use wheels. If cmake is unable to find OpenCV, manually set the location of OpenCV to the OpenCV_DIR environment variable
Package: wbia-utool
Useful Utility Tools For You! - Part of the WildMe / Wildbook IA Project.Notice: This is a “kitchen sink” library. While it is still somewhat maintained, it should be considered “end-of-life”. Please seehttps://github.com/Erotemic/ubeltandhttps://github.com/Erotemic/xdevfora well-maintained curated collection of utilities.Theutoollibrary is a collection of tools that I’ve found useful. I’ve
written most of them from scratch, but there are a few I’ve taken or partially
taken from StackOverflow. References are given in most locations.In my experience the most useful functions in this library are:utool.flattenutool.takeutool.take_columnutool.compressutool.ichunksutool.itertwoutool.isiterableutool.group_itemsutool.dict_subsetutool.dict_histutool.map_dict_valsutool.map_dict_keysutool.memoizeutool.get_argflagutool.get_argvalutool.ProgIterutool.Timerutool.Timeritutool.MemoryTrackerutool.InteractiveIterutool.color_printutool.ensuredirutool.globutool.greputool.sedutool.lsutool.repr2InstallationInstallation can now be done via pypipipinstallwbia-utool
Package: wbia-vtool
Vision Tools - tools for computer vision. Part of the WildMe / Wildbook IA Project.Repos relevant to the ibeis project:https://github.com/WildMeOrg/wbia-vtoolhttps://github.com/WildMeOrg/wbia-pyflannhttps://github.com/WildMeOrg/wbia-pyhesaffhttps://github.com/WildMeOrg/wbia-utoolhttps://github.com/WildMeOrg/wildbook-ia
Package: wbia-whaleridgefindr
whaleridgefindr Plug-in - Part of the WildMe / Wildbook IA Project.A plug-in for using the containerized version of thewhaleridgefindr gray whale ID algorithm.Running the whaleridgefindr ContainerThis plugin assumes that:You have separately started the whaleridgefindr container with the command:dockerrun-p8004:8004/tcp--nameflukebook_whaleridgefindr--networkflukebookhaimeh/whaleridgefindr:latestYour WBIA container also has the same network (“flukebook” above) defined for it.Development Setup./run_developer_setup.shCode Style and Development GuidelinesContributingIt’s recommended that you usepre-committo ensure linting procedures are run
on any commit you make. (See alsopre-commit.com)Referencepre-commit’s installation instructionsfor software installation on your OS/platform. After you have the software installed, runpre-commitinstallon the command line. Now every time you commit to this project’s code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:gitadd.pre-commitrun# orpre-commitrun--all-filesBrunetteOur code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).Flake8Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.To run flake8 from the command line use:flake8This will use the flake8 configuration withinsetup.cfg,
which ignores several errors and stylistic considerations.
See thesetup.cfgfile for a full and accurate listing of stylistic codes to ignore.PyTestOur code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:pytest
Package: wbib
Wikidata BibA helper for building Wikidata-based literature dashboards via SPARQL queries.Free software: MIT licenseDocumentation:https://lubianat.github.io/wbib.CreditsDevelopment LeadTiago Lubiana (lubianat)ContributorsJoão Vitor Ferreira Cavalcante (jvef)AcknowledgementsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template.CookiecutterHistory0.3.12 (2024-02-21)Fix the Laura/Lauren typo.0.3.11 (2024-02-21)Changed the default titles and subtitles of the basic display.0.3.6 (2021-05-30)-Query: Changed "articles" query to select articles with at least 1 author that matches all restrictions.0.3.4 (2021-05-12)Patch: Load Jinja2 template and add Jinja2 as requirement0.3.0 (2021-05-11)Change code to use jinja2 templating for rendering html (contribution by @jvfe).0.2.0 (2021-05-07)Set up "advanced" mode, with yaml template for restricting the query.0.1.0 (2021-04-19)First release on PyPI.
Package: wbjieba
jieba“结巴”中文分词：做最好的 Python 中文分词组件“Jieba” (Chinese for “to stutter”) Chinese text segmentation: built to
be the best Python Chinese word segmentation module.完整文档见README.mdGitHub:https://github.com/fxsjy/jieba特点支持三种分词模式：精确模式，试图将句子最精确地切开，适合文本分析；全模式，把句子中所有的可以成词的词语都扫描出来,
速度非常快，但是不能解决歧义；搜索引擎模式，在精确模式的基础上，对长词再次切分，提高召回率，适合用于搜索引擎分词。支持繁体分词支持自定义词典MIT 授权协议在线演示：http://jiebademo.ap01.aws.af.cm/安装说明代码对 Python 2/3 均兼容全自动安装：easy_install jieba或者pip install jieba/pip3 install jieba半自动安装：先下载https://pypi.python.org/pypi/jieba/，解压后运行
python setup.py install手动安装：将 jieba 目录放置于当前目录或者 site-packages 目录通过import jieba来引用
Package: wbl
commondivide_into_equal_partsNewThreadFilesave_to_fileload_from_filesave_to_jsonsave_to_pickleload_from_jsonload_from_picklesave_to_exceladjust_excel_col_widthEncryptb64_encodeb64_decodemd5spiderSpiderrequest_headers_string_to_dictsearch_query_joinsearch_query_splitcookie_dict_to_strcookie_string_to_dictget_chinese_charresponse_to_xpath
Package: wbn
wbnWeighted Bayesian Network Text ClassificationFree software: MIT licenseDocumentation:https://wbn.readthedocs.io.InstallationFrom source$gitclonehttps://github.com/leonkozlowski/wbn.git$cdwbn$python3.8-mvenvvenv$pipinstall-e.UsageBuilding, training, and testingWBNfromsklearn.model_selectionimporttrain_test_split# Import WBNfromwbn.classifierimportWBNfromwbn.sample.datasetsimportload_pr_newswire# Build the modelwbn=WBN()# Load a sample datasetpr_newswire=load_pr_newswire()# Train/test splitx_train,x_test,y_train,y_test=train_test_split(pr_newswire.data,pr_newswire.target,test_size=0.2)# Fit the modelwbn.fit(x_train,y_train)# Testing the modelpred=wbn.predict(x_test)# Reverse encode the labelsy_pred=wbn.reverse_encode(target=pred)Constructing a new dataset:importpickle# Import data structures for dataset creationfromwbn.objectimportDocument,DocumentData,Documents# Load your dataset from csv or picklewithopen("dataset.pickle"),"rb")asinfile:raw_data=pickle.load(infile)# De-structure 'data' and 'target'data=raw_data.get("data")target=raw_data.get("target")# Construct Document's for each data/target entrydocuments=Documents([Document(DocumentData(paragraphs,keywords),target[idx])foridx,(paragraphs,keywords)inenumerate(data)])CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-11-03)First release on PyPI.
Package: wbo
WBOScrape box office mojo's Worldwide Box Office data. Download as csv. Plot using plotly.jsInstallationpip install wboUsageTypewbo get-dataorwbo display-plotin the command line. These will run the two commands with their default values.
You can also specify the earliest year and the latest year like this:wbo get-data -min=2005 -max=2020
Package: wbpFilebrowser
wbpFilebrowserFilebrowser plugin forWorkbenchapplicationsInstallationpipinstallwbpFilebrowserInstalling this plugin registers an entry point
in the group "wbbase.plugin" named "filebrowser".To use the plugin in your application,
add it to yourapplication.ymlfile as follows:AppName:myAppPlugins:-Name:filebrowserDocumentationFor details read theDocumentation.
Package: wbpFonttools
wbpFonttoolsFontTools plugin forWorkbenchapplications.This plugin provides Workbench document templates for the following font formats:OpenType-TTF FontOpenType-OTF FontOpenType-WOFF WebFontOpenType-WOFF-2 WebFontAs well as a document view to view and edit fonts in these formats.InstallationpipinstallwbpFonttoolsInstalling this plugin registers an entry point
in the group "wbbase.plugin" named "fonttools".To use the plugin in your application,
add it to yourapplication.ymlfile as follows:AppName:myAppPlugins:-Name:fonttoolsDocumentationFor details read theDocumentation.
Package: wbpHTMLpanel
wbpHTMLpanelHTML panel forWorkbenchapplications.InstallationpipinstallwbpHTMLpanelInstalling this plugin registers an entry point
in the group "wbbase.plugin" named "htmlpanel".To use the plugin in your application,
add it to yourapplication.ymlfile as follows:AppName:myAppPlugins:-Name:htmlpanelDocumentationFor details read theDocumentation.
Package: wbpLogConfig
wbpLogConfigLogging Configuration forWorkbenchapplicationsInstallationpipinstallwbpLogConfigInstalling this plugin registers an entry point
in the group "wbbase.plugin" named "logconfig".To use the plugin in your application,
add it to yourapplication.ymlfile as follows:AppName:myAppPlugins:-Name:logconfigDocumentationFor details read theDocumentation.
Package: wbpLoglist
wbpLoglistLoglist plugin forWorkbenchapplicationsInstallationpipinstallwbpLoglistInstalling this plugin registers an entry point
in the group "wbbase.plugin" named "loglist".To use the plugin in your application,
add it to yourapplication.ymlfile as follows:AppName:myAppPlugins:-Name:loglistDocumentationFor details read theDocumentation.