Saravanan666/pypi-org huggingface dataset:
Package: wmt
# Where is my time? ⌚
Find out where is your time. Simple time management CLI.## Installation`bash pip install wmt `On first usage you will be asked to configure your setup. Supported configuration are using local sqlite database file or hosting it automatically on OneDrive.## Usage`bash $ wmt start-nproject_a,task_b,initial_work $ wmt end ``bash $ wmt start-nproject_b,task_c-t"yesterday at 10:00" $ wmt end-t"yesterday at 12:15" `or you can just usewmt, it would start a session and end it if it found a running session:` $ wmt Please type session name:project x, task 2 project x, task 22018-12-2113:05:42 $ wmt project x, task 2 ended (45 minutes) `Time arguments may be relative time in minutes (e.g.-4) or human readable time (and date) which will be parsed by [dateparser](https://dateparser.readthedocs.io).For more information see help.
Package: wmtile
INSTALLATION

    $ pip3 install wmtile

HELP

    $ wmtile -h

USAGE

    $ wmtile [-h] [-H] [-V] [-i] [-k] [-m] [-t] [-p] [-l] [-s] [-b] [-c]


window tiler for XFCE desktop environment

Always give to wmtile one and only one argument.

wmtile reshapes in 7 different ways all windows in the current workspace.

wmtile is a CLI program, but you should use it either by mouse (via wmtile -i)...

    $ wmtile -i
    installing 7 wmtile panel launchers
        panel launcher --> wmtile -m # Minimize
        panel launcher --> wmtile -t # reshape as Tiles
        panel launcher --> wmtile -p # reshape as Portraits
        panel launcher --> wmtile -l # reshape as Landscapes
        panel launcher --> wmtile -s # reshape as a Stack
        panel launcher --> wmtile -b # reshape as Big = maximize
        panel launcher --> wmtile -c # gracefully Close

...or by keyboard (via wmtile -k)

    $ wmtile -k
    installing 7 wmtile keyboard shortcuts
        Alt+Shift+M --> wmtile -m # Minimize
        Alt+Shift+T --> wmtile -t # reshape as Tiles
        Alt+Shift+P --> wmtile -p # reshape as Portraits
        Alt+Shift+L --> wmtile -l # reshape as Landscapes
        Alt+Shift+S --> wmtile -s # reshape as a Stack
        Alt+Shift+B --> wmtile -b # reshape as Big = maximize
        Alt+Shift+C --> wmtile -c # gracefully Close
    please reboot in order to make wmtile keyboard shortcuts effective

for further details, type:

    $ wmtile -H

OPTIONAL ARGUMENTS

  -h, --help        show this help message and exit
  -H, --user-guide  open User Guide in PDF format and exit
  -V, --version     show program's version number and exit
  -i, --launchers   Install 7 panel launchers (XFCE only)
  -k, --shortcuts   install 7 Keyboard shortcuts (XFCE only)
  -m, --minimize    Minimize
  -t, --tiles       reshape as Tiles
  -p, --portraits   reshape as Portraits
  -l, --landscapes  reshape as Landscapes
  -s, --stack       reshape as a Stack
  -b, --big         reshape as Big = maximize
  -c, --close       gracefully Close
Package: wmul_emailer
wmul_emailerUtility to send an email to multiple addresses. Primarily intended for cli applications. The various fields such as server host, username, etc. are entered at the command line, used to construct a wmul_emailer.EmailSender object, and that object is passed into the main part of the script. The main part of the script can use it as needed to report errors or results without having to worry about the e-mail details.A cli is provided for testing purposes.class EmailSender(server_host, port, user_name, password, from_email_address=None, destination_email_addresses=None)server_hostThe hostname or ip address of the smtp server.portThe port number on which the smtp server resides.user_nameThe username to authenticate with the smtp server.passwordThe password to authenticate with the smtp server.from_email_address=NoneThe 'from' e-mail address.destination_email_addresses=NoneThe e-mail address to which the results should be sent. Must be a list or tuple for multiple addresses, or a str for a single address.send_email(self, email_body, email_subject, from_email_address=None, destination_email_addresses=None)email_bodyThe body of the e-mail to be sent.email_subjectThe subject line of the e-mail to be sent.from_email_address=NoneThe 'from' e-mail address, if provided here. If afrom_email_addressis not included when calling this method, the one provided to the constructor will be used.destination_email_addresses=NoneThe e-mail address to which the results should be sent, if provided here. Ifdestination_email_addressesare not included when calling this method, the ones provided to the constructor will be used. Must be a list or tuple for multiple addresses, or a str for a single address.RaisesValueErrorif eitherdestination_email_addressesorfrom_email_addressare omitted from both the constructor and the call to send_email.Command-Line InterfaceA cli is provided to test that the credentials are functional and that the system can send e-mails.send_test_email --email destination@example.com --server smtp.example.com --port 25 --username myusername --password mypassword --from_address source@example.comIf everything works, thendestination@example.comwill receive an e-mail fromsource@example.comwith the subjectTest e-mail from wmul_emailerand bodyThis is the test e-mail from wmul_emailer.py. If you are reading this, the software is configured correctly.
Package: wncfe-query-inspector
SQL ToolsSimple middleware tool for analyzing Django ORM SQL execution.UsageAdd the follow line of code to your middleware in your project settings.py file'wncfe-sql-tools.middleware.new_middleware'How it worksThe tool uses Django built in features to inspect the SQL generated from queries executed by the application. 3rd party tools are used to format and highlight the SQL presented in the terminal window.
Package: wn-editor
wn-editorA extension for the popular wn package, making wordnets editable from python!MotivationCurrently thewn python packagedoes not support editing the imported wordnets. For some usecases editing the wordnets or creating new ones on the fly is very important. This package aims to be an extension of wn and provides an API for editing wordnets.DocumentationThis package integrates directly into the wordnet database maintained by wn, and thus allowes to change data and not only provide a changed view.
This is done by using different editor classes for the various wordnet components.QuickstartInstall this package usingpip install wn-editorimport the package (and wn) like soimportwnfromwn_editor.editorimportLexiconEditor# Get and editor for an installed lexiconlex_edit=LexiconEditor('odenet')# Use differnet methods to create and edit synsetslex_edit.create_synset().add_word('auto').set_hypernym_of(wn.synsets('mercedes')[0])Read the wiki for more detailed info about the available methods and classes.
Package: wnetron
Netron is a viewer for neural network, deep learning, and machine learning models.Netron supports ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn, MNN, PaddlePaddle, Core ML, MXNet, RKNN, MindSpore Lite, TNN, Barracuda, Tengine, TensorFlow.js, Caffe2 and UFF. Netron has experimental support for PyTorch, TensorFlow, TorchScript, OpenVINO, Torch, Vitis AI, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET and scikit-learn.
Package: wnhelloworldpackage
helloworldIt's a simple python package that prints hello world!PIP installTest\prereleasehttps://test.pypi.org/project/wnhelloworldpackage/pip install -i https://test.pypi.org/simple/ wnhelloworldpackageProductionhttps://pypi.org/project/wnhelloworldpackage/pip install wnhelloworldpackageTest locally from source codepip install .python test.pyCreate local packageMake sure you have the latest versions of setuptools, wheel installed:pip install --user --upgrade setuptools wheelpython setup.py sdist bdist_wheelunderdistthere will be one or many*.whlfile you can install it locallypip install C:/some-dir/some-file.whlorpip install /dist/some-file.whlSteps to upload your package to the Python Package Index under your account:get user accounthttps://test.pypi.orgget user accounthttps://pypi.orgMake sure you have the latest versions of setuptools, wheel and twine installed:pip install --user --upgrade setuptools wheelpip install --user --upgrade twineRename azhelloworldpackage, your package name can contains letters, numbers, _ , and -. It also must not already taken on pypi.org
you need to renamenameinsetup.pyfile and rename the folder structure
`/yourpackagenameinit.py`python setup.py sdist bdist_wheelUpload it to test\prerelease environmenttwine upload --repository-url https://test.pypi.org/legacy/ dist/*Test newly uploaded packagepip install -i https://test.pypi.org/simple/ yourpackagenameUpload it to production environmenttwine upload dist/*Test your packagepip install yourpackagenameResourcehttps://packaging.python.org/tutorials/packaging-projects/#uploading-your-project-to-pypi
Package: wNMF
wNMF: Weighted Non-Negative Matrix FactorizationAboutwNMFimplements a simple version of Non-Negative Matrix Factorization (NMF) that utilizes a weight matrix to weight the importance of each feature in each sample of the data matrix to be factorized.wNMFis easy to use, because it behaves like ansklearn.decompositionmodel, but also allows for multiple fitting attempts.More information about the modified multiplicative update algorithim utilized can be found here:Blondel, Vincent & Ho, Ngoc-Diep & Van Dooren, Paul. (2007). Weighted Nonnegative Matrix Factorization and Face Feature ExtractionwNMFspecifically implements solutions for determining the decomposed matrices U and V when minimizing the Frobenius Norm or the Kullback-Leibler Divergence:Useful LinksSource on GithubPackage on PyPIInstallationThis package is available on PyPI and can be installed withpip:$pipinstallwNMFAlternatively, download the source fromgithuband install:$gitclonehttps://github.com/asn32/weighted-nmf.git
$cdweighted-nmf
$python3setup.pyinstall--userUsagewNMFis a python library that can be imported.fromwNMFimportwNMFAnd it can be used like ansklearn.decompositionmodel.First create an instance of thewNMFmodel by setting the number of components.Other parameters can be set too, such as the loss function, maximum number of iterations, and whether or not to track the decreasing error over every single run.## Mock data, a 100x100 data matrix, reduce to 25 dimensionsn=100features=100components=25X=100*np.random.uniform(size=n*features).reshape(features,n)W=np.ones_like(X)## Define the model / fitmodel=wNMF(n_components=25,beta_loss='kullback-leibler',max_iter=1000,track_error=True)Then, fit the model to the data using the instance methodswNMF().fitorwNMF().fit_transform.fit=model.fit(X=X,W=W,n_run=5)After the fit is complete, explore the fit quality by examining the decomposed matrices and / or overall error.## Get the best solutionslowest_error=fit.errbest_V=fit.Vbest_U=fit.U## Or look at all the solutions from the 5 runs in this exampleall_Vs=fit.V_allLicensewNMF is MIT-licensedDisclaimerwNMFis provided with no guarantees
Package: wnnet
wnnet is a python package for handlingwebnucleoreaction networks.InstallationInstall fromPyPIwith pip by
typing in your favorite terminal:$ pip install wnnetAuthorsBradley S. Meyer <mbradle@g.clemson.edu>Mengke Li <mengkel@g.clemson.edu>ContributeIssue Tracker:https://github.com/mbradle/wnnet/issues/Source Code:https://github.com/mbradle/wnnet/LicenseThe project is licensed under the GNU Public License v3 (or later).DocumentationThe project documentation is available athttps://wnnet.readthedocs.io.UsageThe best way to get started using wnnet is to follow thetutorial.
Package: wnpoly
The module computes various polynomials useful forwebnucleoprojects.  In particular, the package has modules to compute symmetric and Bell polynomials for input sets of variables.  For the complete Bell polynomials, the package also has a routine to invert an appropriate set of Bell polynomials to find the variables that would give rise to those polynomials.InstallationInstall fromPyPIwith pip by
typing in your favorite terminal:$ pip install wnpolyUsageTo learn how to use the package, follow the Jupyter notebooktutorial.AuthorsBradley S. Meyer <mbradle@g.clemson.edu>Ian Reistroffer <ireistr@g.clemson.edu>ContributeIssue Tracker:https://github.com/mbradle/wnpoly/issues/Source Code:https://github.com/mbradle/wnpoly/LicenseThe project is licensed under the GNU Public License v3 (or later).DocumentationThe project documentation is available athttps://wnpoly.readthedocs.io.
Package: wnutils
wnutils is a python package for reading and plottingwebnucleofiles.InstallationInstall fromPyPIwith pip by
typing in your favorite terminal:$ pip install wnutilsAuthorsBradley S. Meyer <mbradle@g.clemson.edu>Norberto Davila <ndavila@g.clemson.edu>ContributeIssue Tracker:https://github.com/mbradle/wnutils/issues/Source Code:https://github.com/mbradle/wnutils/LicenseThe project is licensed under the GNU Public License v3 (or later).DocumentationThe project documentation is available athttps://wnutils.readthedocs.io.UsageThe best way to get started using wnutils is to follow thetutorials.
You may also want to visit ourgalleriesor check out ourJupyter Notebooksor ourCode Samples.
Package: woah
WoahWoah is a script that you can wrap commands with, and it will wait until the
load average is reasonable before running them.UsageSimilar tonice, you just start your command withwoahand everything after
that will be run unchanged.Say, if you have a lot of other things going on, and want your backup to wait
for things to settle down...woah tar -xvzf /tmp/foo.tar users/timOr you're running multiple things with xargs, but some are more expensive than
others and you want to keep your machine somewhat responsive...(cd /users; ls -d *) | xargs -P32 -n1 --no-run-if-empty woah tar /tmp/{} /users/{}Bugs and suchhttps://github.com/thatch/woahLicenseApache 2.0
Package: woahdiscord
A python library allowing discord to be used unlimited cloud storage.What's the purpose?Cloud storage is expensive even if you have a 10 USD and 20 USD laying around. Social media platforms like Instagram, YouTube, Discord offers unlimited storage to upload attachments and files. Though being limited to certain file extensions in some instances.With careful planning, we can take advantage of that and upload data from our local storage to those platforms and use them as cloud storages. In this library, I have implemented the required functions to achieve that.ExamplesHow to split a file into small chunksfromwoahdiscordimportsplit_filechunk_size_mb=20Â# Set your desired chunk size in megabytesfile_or_folder_path='woahdiscord\Wireshark-4.2.2-x64.exe'ifos.path.isfile(file_or_folder_path):ÂÂsplit_file(file_or_folder_path,chunk_size_mb,add_metadata=True)elifos.path.isdir(file_or_folder_path):ÂÂsplit_files_in_folder(file_or_folder_path,chunk_size_mb,add_metadata=True)else:ÂÂprint('Invalid file or folder path.')How to merge the chunksfromwoahdiscordimportmerge_filesfolder_path="Wireshark-4.2.2-x64_chunks"merge_files(folder_path)How to convert a file into jpg/jpegfromwoahdiscordimportfile_jpginput_path='woahdiscord\Wireshark-4.2.2-x64.exe'Â# Can be a file or a directoryfile_jpg(input_path)Reconstructing jpg into originalfromwoahdiscordimportosmosisfolder_path='CXh2bgPY'Â# Provide the folder path hereosmosis(folder_path)Setting up discord bot to upload files automaticallyfromwoahdiscordimportauto_uploadauto_upload_files("your/folder/path","<Your Bot Token>")
Package: wobble
wobble======Deployment tool for deploying to Marathon running in DCOS, easily pluggable for servers requiring authentication and custom confirmation logic.This is a deployment solution for deployments to Marathon orchestrated clusters running in DCOS. If what youneed is a *fire-and-forget* deployment, you should probably use `dcos/dcos-cli`_... _usage:Usage-----Current support is only for a cluster that is protected with Auth0_, however this will be abstracted into a :ref:`Plugin <plugins>` shortly... code-block::pip install wobblewobble --helpMost options listed will respect an environmental variable, where the leading hyphens are stripped and the remaining characters are capitalized and converted to snake case:.. code-block::dcos-url -> DCOS_URLauth0-url -> AUTH0_URL.. _plugins:Plugins-------TODO: This system is not currently definedDeveloping==========# Run all the testspython setup.py test# Developpip install -e ... _dcos/dcos-cli: https://github.com/dcos/dcos-cli.. _Auth0: https://auth0.com/
Package: woc
🛠 Awesome Command toolkit you are waiting for 🛠Installation🎉 ⚙ 🔑 🎛️ 🙈 🎠 ❌ 🎉pipinstallwochahahahaha... now,wocis on your environment, if you want to know what woc
can do, type commandwoc -hIf you could not figure out how to use it's function, such astree, just
type this command:woctree-hSometimes it is useful to be allowed to invoke more than one subcommand in one go.
For instance:wocpypi-pcREFERENCESclickDocuments
Package: woebin-python
woebinA python library powered by Rust to perform binning procedure based on WOE.Installationpip install woebin-pythonNOTE: It works for Windows 11 only. If you want to use it for another platform, please, see below how to build it from source.Simple examplefromwoebinimportWoeBinningProcwbp=WoeBinningProc()# Process categorial binningwbp.process(df['series'],df['target'],bins=5,is_numeric=False,smooth=1.0)# series as integers, target as 0-1 or boolean# Process numeric binning (values in series are considered as numeric)wbp.process(df['series'],df['target'],bins=5,is_numeric=True,smooth=1.0)# Final IVprint(wbp.get_iv_total())# Information about found binsbins_info=wbp.get_bins_info()print(bins_info)# Mapping value->WOEwoe_map=wbp.get_woe_map()print(woe_map)Build and install from sourceMake sure Rust nightly is installed (Installation:https://www.rust-lang.org/tools/install, turn on nightly mode:rustup default nightly).Clone the repository:git clone --depth=1 https://github.com/fomalhaut88/woebin.gitGo towoebindirectory:cd woebinBuild the project:python setup.py sdistAfter that the package can be installed with the command:pip install path/to/woebin-python-<VERSION>.tar.gz
Package: woe-conversion
woewoe is a Python library designed to convert categorical and continuous variables into weight of evidence. Weight of evidence is a statistical technique used in information theory to measure the "strength" of a relationship between a binary target variable and a predictor variable. The library can be used for data preprocessing in predictive modeling or machine learning projects.installationpip3 install woe-conversionusagefrom woe_conversion.woe import *

# create an instance of the WoeConversion class
woemodel = WoeConversion(binarytarget='survived', features=['categorical_variable_1','categorical_variable_2','continuous_variable_3'])

# fit the model using training data (train is a pandas dataframe)
woemodel.fit(train)

# transform the training and test data using the fitted model (train and test are pandas dataframes)
transformedtrain = woemodel.transform(train)
transformedtest = woemodel.transform(test)In the above code, WoeConversion is the class that is used to perform the conversion of variables to weight of evidence. The binarytarget parameter is the name of the binary target column in your dataset, and features is a list of the columns that you want to convert to weight of evidence.Once the model has been created, it is fit to the training data using the fit method. This method calculates the weight of evidence for each category in the specified columns, and stores the results in the model object.The transform method is then used to transform the training and test data using the fitted model. This method replaces the original columns with their weight of evidence equivalents.Note on Missing ValuesThe weight of evidence is also calculated for missing values. Therefore, missing values should not be imputed before calling the woe model.Full working examplefrom woe_conversion.woe import *
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

titanic = sns.load_dataset('titanic')
train, test = train_test_split(titanic, test_size=0.30,random_state=111,stratify=titanic['survived'])

woemodel = WoeConversion(binarytarget='survived', features=['age','sex','class','fare'])
woemodel.fit(train)
transformedtrain = woemodel.transform(train)
transformedtest = woemodel.transform(test)

clf = LogisticRegression()
clf.fit(transformedtrain[['age','sex','class','fare']], transformedtrain['survived'])

transformedtrain['proba'] = clf.predict_proba(transformedtrain[['age','sex','class','fare']])[:,1]
train['proba'] = clf.predict_proba(transformedtrain[['age','sex','class','fare']])[:,1]
transformedtest['proba'] = clf.predict_proba(transformedtest[['age','sex','class','fare']])[:,1]
test['proba'] = clf.predict_proba(transformedtest[['age','sex','class','fare']])[:,1]Authorwoe was created by Bertrand Brelier. If you have any questions or issues, please feel free to contact the author.
Package: woe-iv
woe_ivcaculate woe(weight of evidence) of each feature and then iv(information value).Features1 Calculation of WOE  and IVdef WOE(cls, data, varList, type0=’Con’, target_id=’y’, resfile=’result.xlsx’):“””
对分类变量直接进行分组统计并进行WOE、IV值 计算
对连续型变量进行分组（default:10）后进行WOE、IV值 计算
:param data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table)
:param varList: variable list
:param type0: Continuous or Discontinuous(Category), ‘con’ is the required input for Continuous
:param target_id: y flag when gen the train data
:param resfile: download path of the result file of WOE and IV
:return: pandas DataFrame, result of woe and iv value according y flag
“””
pass2 Apply of WOE repalcement of ABTdef applyWOE(cls, X_data, X_map, var_list, id_cols_list=None, flag_y=None):“””
将最优分箱的结果WOE值对原始数据进行编码
:param X_data: pandas DataFrame, mostly refer to ABT(Analysis Basics Table)
:param X_map: pandas dataframe, map table, result of applying WOE, refer the func woe_iv.WOE
:param var_list: variable list
:param id_cols_list: some other features not been analysed but wanted like id, adress, etc.
:param flag_y: y flag when gen the train data
:return: pandas DataFrame, result of bining with y flag
“””
passCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-12-19)
0.2.0 (2018-12-2l)
——————First release on PyPI.
Package: wofry
# wofry
Wave Optics FRamwork in pYthonWOFRY [1] is the OASYS framework for waveoptics calculations. It contains a threefold functionality:
- it provides a generalization (or abstraction) of a software tool for wave optics, combining the component definitions from SYNED with the abstract declaration of wavefronts and propagators
- it defines a mechanism for interfacing a wave optics code (e.g., SRW, WISE etc.) in it, a first step for becoming interfaced in OASYS
- it provides native implementations of simple wavefronts (e.g., plane waves, spherical waves, Gaussian sources) and propagators [2] for prototyping optical systems.## References[1]https://doi.org/10.1117/12.2274232[2]https://github.com/oasys-kit/documents/blob/master/zoom_propagator_pirro_thesis.pdf
Package: wofrywiser
WOFRY (Wave Optics FRamework in pYthon) for Wiser libraryKernel classes for WOFRY - Wiser in python.It is an intermediate layer between LibWiser and Wofry. It exists in order to ensure interoperability between the rest of OASYS codes and LibWiser - numerical integration using Huygens integral.syned package requires Python 3.3 or newer.Work in progressinitial layer for scriptingInstallingwofrywiser is pip installable (https://pip.pypa.io/) by:pip install wofrywiserto install it.