Saravanan666/pypi-org huggingface dataset:
Package: enrichsdk
Enrich is a customizable, privacy law-aware enterprise Feature Store
ofScribble Data. This SDK is part of the feature store stack. It
enables local development, testing and documentation of simple to
complex feature transformations and other modules required for
building and managing robust features.This is not for general purpose use. Please get in touch with us athello@scribbledata.ioto discuss potential use of this SDK.For enterprise users of Enrich, the documentation is available on the
server. Please see the Developer section.Usagesudo apt-get updatesudo apt-get install python3.8-dev python3.8-venvpython3 -m venv venvpip3 install wheelpip3 install enrichsdkenrichpkg startDocspython3 -m venv venvpip3 install -r requirements.txtmkdocs serveUpdating Release TagAdd to $HOME/.bashrc. Make sure you source ~/.bashrc before using:function git_move_tag {
   git push origin; git tag -d $1; git tag $1 ; git push origin --tags --force
}

function git_push {
   git push origin $1; git push origin $1 --tags ;
   git push origin $1 refs/notes/*
}

function git_rm_tag {
   git push origin; git tag -d $1; git push --delete origin $1
}Bump the version. Version is typically a.b.c:# activate the environment
workon dev
cd scribble-enrichsdk # cd to the root

# If bumpversion doesnt exist
pip install bump2version

# Check existing tags once
git tag -l

# Bump the right version
bumpversion patch # for updating c
bumpversion minor # for updating b
bumpversion major # for updating aPush the changes to github:# See above
git_push masterUpdate deployment version:cd scribble-deploy-v2/configuration

# update enrichsdk_branch
vi defaults.json

# Make
git commit -a -m "Updated enrichsdk version"

git push originInstall at customer:cd scribble-deploy
fab install_enrichsdk:role=demoUpload to pypi.:python3 setup.py sdist

# This will require token in ~/.pypirc
twine upload -r pypi dist/enrichsdk-5.0.4.tar.gz

cat ~/.pypirc
[pypi]
username = __token__
password = pypi-AgEIcHlwaS5v...Known issuesThe package dependencies are a jungle.Problematic packages include:boto3
botocore
aiobotocore
jupyter-events
nbconvertUse the fix environment script to monkey-patch any dependencies in the worst case:./bin/fix-environment.py
Usage: fix-environment.py [OPTIONS] COMMAND [ARGS]...

  This package will help fix the environment

Options:
  --help  Show this message and exit.

Commands:
  lib  Fix a library dependency
Package: enrichwrap
High level descriptionThis tool willTake in a set of provided data, or use input.json in its placeRead in a set of available enrichment REST endpoints, with POST or GET structure definedRead in a set of steps to follow, to call multiple enrichment providers in parallel, serial, percentage,
or fallback meansRead in mapping XML, which will take the input data (step 1) and convert it into what the various REST endpoints
need (step 2).Read in outbound mapping XML, which will take the data that comes back from the enrichment tools (step 2) and map
it to data that is named what a customer would expect as outputFollows the steps defined (step 3), with the input data (step 1).For each step,For each enrichment called, map the incoming data to what the rest call needs.  Call the rest interfaceOn failure, capture the failure in the resultsOn success, capture the data that comes back. Map it to the variable names as defined in step 5Return back the set of data from each of the enrichment providers, remapped as requested, to the callerRequirementspip install requestspython -m pip install --upgrade pip
Package: enrocrypt
EnroCryptThis is a Python Module For Encryption, Hashing And Other Basic Stuff You Need, With Secure Encryption And Strong Salted Hashing You Can Do Whatever You Want ToDocsInstallationEnroCrypt Is Avaliable OnPyPiYou Can Install It As Follows:pip install -U enrocryptManual InstallationIf For Some Reason You Can't Install EnroCrypt From PyPi You Can Download It Manually Too:Clone This RepoCut-Paste This Repo In Your Python Scripts PathEnter In The Folder Where You See "setup.py" fileshift+right click in the Folder And Click On "Open Powershell window Here"Typepython setup.py installAfter Following All The Steps Mentioned Above (If You Don't Get An Error) EnroCrypt Is Installed, Now You Can Import It Right AwayFeaturesStrong EncryptionStrong Salted HashingFile Encryption-DecryptionSome Basic FunctionsUsage# For Encryptionfromenrocryptimportcoreobj=core.Core()value=obj.Encrypt(b'text')print(value)# For Decryptionoriginal_value=obj.DecryptList(value)print(original_value)There Is Also aDecryptFunction, But In This Function You Have To Enter The Key And The Data SeperatlyAdding ConfigurationsEnrocrypt Has The Ablity To Adopt Custom Configurations, Namely:- Custom SaltWe Provide A Function In Which You Can Pass All The Configurations But It Also Has A Syntax And Must Be Used "As is"fromenrocryptimportcoreconfig={'configs':{'salt_file':"The Path Of The File Where Your Salt Is Stored"}}# You need a Core Class Object to access that functionobj=core.Core()obj.set_config(config)Getting A Hashing Class ObjectWe Suggest Not To Use The Hashing Class By Importing It Directly As If You Do So You Can't Add Your Custom SaltTo Add Custom Salt Follow TheAdding Configuration. After You Did That You Have To Get A Hasing Class Object By A Core Class Functionfromenrocryptimportcoreobj=core.Core()hasing_obj=obj.get_hash_object()(See Discussion For More Info)
Package: enron_reader
Installationpip3installenron_readerPrerequisitesFor using this package, you must have the Enron email corpus locally. If you don’t, either download and extract it using the link providedhere, or rundownload_enron/path/to/download/enronin your terminal after installing this package.Then, you should have a folder named ‘maildir’ in that directory. It should contain a directory for each user (Enron employee)Usage examplefromenron_readerimportEnronReaderreader=EnronReader("/path/to/enron/root")print(reader.get_user_ids())mailbox=reader.get_mailbox_for_user("keiser-k")main_folders=mailbox.root_folder.subfolderssome_message=main_folders[0].messages[0]print(some_message.subject)
Package: ensae2019tmkw
ï»¿# READMEPrÃ©sentation du packageLe packageensae2019est un module graphique de visualisation (animÃ©e ou non) utilisant principalement une surcouche du packagematplotlib, Ã  savoircartopy.
Il contient deux fonctions qui visualisent l'Ã©volution temporelle d'une donnÃ©e numÃ©rique gÃ©olocalisÃ©e, de deux faÃ§ons distinctes:
Package: ensae2019tmkw2
ï»¿# READMEPrÃ©sentation du packageLe packageensae2019est un module graphique de visualisation (animÃ©e ou non) utilisant principalement une surcouche du packagematplotlib, Ã  savoircartopy.
Il contient deux fonctions qui visualisent l'Ã©volution temporelle d'une donnÃ©e numÃ©rique gÃ©olocalisÃ©e, de deux faÃ§ons distinctes:
Package: ensae-projects
ensae_projectsOutils pour des projets d’étudiants, des hackathons, anti-sèches.Helpers, tools for teachings, events, hackathons, cheat sheets.Installationpip install ensae_projectsLinksGitHub/ensae_projectsdocumentationBlogHistorycurrent - 2021-01-05 - 0.00Mb13: move CI to python 3.9 (2021-01-05)0.2.574 - 2019-11-21 - 0.27Mb12: Remove waffle (deprecated) (2019-07-13)7: move CI to python 3.7 (2019-07-13)11: implements knn to find close images (2018-11-11)10: add code about bicycle trajectories knowning their location when they are stopped (2018-10-14)9: function folium_html_street_map fails with folium 0.6 (2018-08-12)8: fix a bug in plot_streets_network (2018-07-27)2: replace basemap with cartopy, postpone until cartopy is ready (2018-07-23)6: finish the summary on the last hackathon (2018-04-14)5: add function to manipulate images in video (2018-01-07)4: add function to manipulate text in videos (2018-01-07)
Package: ensae-teaching-dl
ensae_teaching_dlIt contains material for teachings about deep learning
withtorchandkeras. Le contenu est principalement en
français.GitHub/ensae_teaching_dldocumentationBlogHistorycurrent - 2021-01-01 - 0.00Mb0.0.0 - 2021-01-01 - 0.00Mb7: Keras example is failing after keras update (2.4.3) (2020-07-02)4: add a notebook with a neural network which overloads the backwark method (2019-04-09)3: complete migration from ensae_teaching_cs (2018-07-27)1: empty shelve (2018-07-01)
Package: ensaio
Practice datasets to probe your codeDocumentation(latest)•Documentation(main branch)•Contributing•ContactPart of theFatiando a TerraprojectAboutEnsaio(Portuguese for "rehearsal") is a Python package for downloading
open-access sample datasets for Geoscience.
It taps into theFatiando a Terra FAIR data collectionthat
is designed for use in tutorials, documentation, and teaching.It usesPoochto manage downloading and
caching the data on your computer.
This means that datasets are only downloaded if they can't be found on your
computer already.Project goalsProvide minimal code for downloading our sample data (basically just creates
the relevant Pooch code).Only download and let the user load the data. This helps make tutorials and
examples more easily extended to a user's own data.Be fully backwards compatible. We achieve this by separatingdataversions fromEnsaioversions. Data fetching functions allow you to
choose any data version that is older than the version of Ensaio that's
installed. Major releases of Ensaio will be few and far between (if any).Project statusEnsaio is ready for use but still changing.This means that we sometimes break backwards compatibility as we try to
improve the software based on user experience, new ideas, better design
decisions, etc. Please keep that in mind before you update to a newer
version.We welcome feedback and ideas!This is a great time to bring new ideas on
how we can improve the project.Join the conversationor submitissues on GitHub.Getting involved🗨️Contact us:Find out more about how to reach us atfatiando.org/contact.👩🏾‍💻Contributing to project development:Please read ourContributing Guideto see how you can help and give feedback.🧑🏾‍🤝‍🧑🏼Code of conduct:This project is released with aCode of Conduct.
By participating in this project you agree to abide by its terms.Imposter syndrome disclaimer:We want your help.No, really.There may be a little voice inside your
head that is telling you that you're not ready, that you aren't skilled
enough to contribute. We assure you that the little voice in your head is
wrong. Most importantly,there are many valuable ways to contribute besides
writing code.This disclaimer was adapted from theMetPy project.LicenseThis is free software: you can redistribute it and/or modify it under the terms
of theBSD 3-clause License. A copy of this license is provided inLICENSE.txt.
Package: ensc
enscDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nensc--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallenscnbdev# activate conda environment$condaactivateensc# make sure the ensc package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the ensc package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/ensc.gitor fromconda$condainstall-cdsm-72enscor frompypi$pipinstallenscDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: ensdat
Useful modules for encryption and file encryption and decryption (currently only string decryption)testmaking key code :print(destr.make_key('aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa'))# result : [97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97,#           97, 97, 97, 97]encrypting codeprint(destr.ens('this is test str.',destr.key))# result : b'89k2hfSgnomKayxjTYPH4rxGGMSZORJJueh8x53OCaXq4+UFzTWeU2qjQQcqmtMV'print(destr.ens('this is test str.',destr.key))# result : b'qfEw+NuqhmIsq1DbXZh6glNEQSW3EUkZWxFEOdQwdSudTGWszjA17BNF27hSgBgs'print(destr.ens('this is test str.',destr.key))# result : b'xxMjhD7BbBTNKYDWM9W9F51n13VgxGtUqYbVGx5ErBvcWBVHG4umnaHPPsoobcj2'print(destr.ens('this is test str.',destr.key))# result : b'JdheT2cei4KTYHY3I8aMMi+9xYX#+ugySPBGLAuUpZE6VlSNWKmguyifgI5yxh8YG'decrypting codeprint(destr.des(b'89k2hfSgnomKayxjTYPH4rxGGMSZORJJueh8x53OCaXq4+UFzTWeU2qjQQcqmtMV',destr.key))# result : 'this is test str.'
Package: ensembl-assembly
ensembl_assemblySearch and download Ensembl data such as genome assemblies and gene annotationsThis program will produce a tabular-like format with the metadata for the genomes matching user criteria.
It may also download desired data to a local folder.InstallationInstall through conda:conda install -c mmariotti ensembl_assemblyOr, alternatively, through pip:pip install ensembl_assemblyUsageBy default, the program will show information for all available genome assemblies (most recent release).
To see options, run with:ensembl_assembly.py -hDependenciespandaseasytermThese are automatically installed through the conda or pip installation procedure.
Package: ensembl-converter
*Ensembl_converterTheEnsembl_converterpackage is a Python library that allows you to convert Ensembl IDs to gene symbols. It provides a simple and convenient way to retrieve gene symbols for a given list of Ensembl IDs using the Ensembl REST API.InstallationYou can installEnsembl_converterusing pip:pipinstallEnsembl_converterUsageTo convert Ensembl IDs to gene symbols, follow these steps:Import theEnsemblConverterclass from the Ensembl_converter package.Create an instance ofEnsemblConverterCall theconvert_idsmethod with a list of Ensembl IDs as input.Here's an example:fromEnsembl_converterimportEnsemblConverter# Create an instance of EnsemblConverterconverter=EnsemblConverter()# Provide a list of Ensembl IDsensembl_ids=['ENSG00000000457','ENSG00000273486','ENSG00000273493']# Convert Ensembl IDs to gene symbolsresult=converter.convert_ids(ensembl_ids)# Print the resulting DataFrameprint(result)This will output a DataFrame with two columns: ENSG (Ensembl IDs) and Symbol (gene symbols).OptionsTheEnsemblConverterclass supports the following options:use_progressbar(default=False): Set this toTrueto display a progress bar during the conversion process. This can be useful for larger gene lists.DependenciesThe Ensembl_converter package depends on the following libraries:requestspandastqdmThese dependencies will be automatically installed when you install theEnsembl_converterpackage (If you don't already have these installed).ContributingContributions toEnsembl_converterare welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.LicenseThis project is licensed under the MIT License. See theLICENSEfile for details.
Package: ensemble
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the documentation and/or
other materials provided with the distribution.Neither the name of OASES team nor the
names of its contributors may be used to endorse or promote products derived
from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Description: # EnsembleCreating hybrid LCI tables with science :)[![Build Status](https://travis-ci.org/OASES-project/ensemble.svg?branch=master)](https://travis-ci.org/OASES-project/ensemble)Platform: UNKNOWN
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Package: ensembleclustering
ClusterEnsemblesA Python package for cluster ensembles. Cluster ensembles generate a single consensus clustering label by using base labels obtained from multiple clustering algorithms. The consensus clustering label stably achieves a high clustering performance.This package was originally authored by Takehiro Sano but has since been removed from PyPi.
This a cloned version I am maintaining. All original code and functionality is unchanged, it is just maintained, tested, and published from here.Installationpip install ensembleclusteringUsageCE.cluster_ensemblesis used as follows.>>>importnumpyasnp>>>importensembleclusteringasCE>>>label1=np.array([1,1,1,2,2,3,3])>>>label2=np.array([2,2,2,3,3,1,1])>>>label3=np.array([4,4,2,2,3,3,3])>>>label4=np.array([1,2,np.nan,1,2,np.nan,np.nan])# `np.nan`: missing value>>>labels=np.array([label1,label2,label3,label4])>>>label_ce=CE.cluster_ensembles(labels)>>>print(label_ce)[1112200]Parameterslabels:numpy.ndarrayLabels generated by multiple clustering algorithms such as K-Means.Note:Assume that the length of each label is the same.nclass:int, default=NoneNumber of classes in a consensus clustering label.
Ifnclass=None, set the maximum number of classes in each label except missing values.
In other words, setnclass=3automatically in the above.solver:{'cspa', 'hgpa', 'mcla', 'hbgf', 'nmf', 'all'}, default='hbgf''cspa': Cluster-based Similarity Partitioning Algorithm [1].'hgpa': HyperGraph Partitioning Algorithm [1].'mcla': Meta-CLustering Algorithm [1].'hbgf': Hybrid Bipartite Graph Formulation [2].'nmf': NMF-based consensus clustering [3].'all': The consensus clustering label with the largest objective function value [1] is returned among the results of all solvers.Note:Please use 'hbgf' for large-scalelabels.random_state:int, default=NoneUsed for 'hgpa', 'mcla', and 'nmf'. Please pass an integer for reproducible results.verbose:bool, default=FalseWhether to be verbose.Returnlabel_ce:numpy.ndarrayA consensus clustering label generated by cluster ensembles.Exampletsano430/egnmf:https://github.com/tsano430/egnmfSimilar PackageGGiecold/Cluster_Ensembles:https://github.com/GGiecold/Cluster_EnsemblesReferences[1] A. Strehl and J. Ghosh,
"Cluster ensembles -- a knowledge reuse framework for combining multiple partitions,"
Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.[2] X. Z. Fern and C. E. Brodley,
"Solving cluster ensemble problems by bipartite graph partitioning,"
In Proceedings of the Twenty-First International Conference on Machine Learning, p. 36, 2004.[3] T. Li, C. Ding, and M. I. Jordan,
"Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization,"
In Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 577-582, 2007.[4] J. Ghosh and A. Acharya,
"Cluster ensembles,"
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, no. 4, pp. 305-315, 2011.
Package: ensemble-eeg
Table of ContentsENSEMBLE EEGGetting StartedPrerequisitesInstallationUsageAcknowledgmentsENSEMBLE EEGEnsemble EEG is a library of EEG analyis tools for the ENSEMBLE study. As of
today it is focuses on 5 seperate things:AnonymizingEDF files in accordance with the ENSEMBLE study and the
requirements ofEDF+FixingEDF headers to adhere to theEDF+standardConvertingBRM toEDF+filesCombiningseperate aEEG channels into oneEDF+fileRenamingEEG files according to ENSEMBLE andBIDSstandardsGetting startedPrerequisitesThe following is required for the use of this softwarePython 3.10&pipFor instructions, please refer to the followinglinkInstallationpython3-mpipinstallensemble_eegUsageStart pythonFrom command lineOpen cmd / terminal / powershell or your preferred command line interpreterType python or python3Using your preferred python interpreterJupyter notebookPyCharmSpyderAnonymizing EDF-filesfromensemble_eegimportensemble_edfensemble_edf.anonymize_edf_header('path/2/your/edf/file')Fixing EDF headersfromensemble_eegimportensemble_edfensemble_edf.fix_edf_header('path/2/your/edf/file')Combine left and right aEEG channels into one single filefromensemble_eegimportensemble_edfensemble_edf.combine_aeeg_channels('path/2/your/left/channel','path/2/your/right/channel','new_filename')Rename EDF-files according to BIDS and ENSEMBLE standardsfromensemble_eegimportensemble_edfensemble_edf.rename_for_ensemble('path/2/your/edf/file')Example scripts for specific situations1) File is already .edf, but you do not know whether header is EDF+, the file is not anonymized, and not renamedfromensemble_eegimportensemble_edffile='path/2/your/edf/file'ensemble_edf.fix_edf_header(file)# for header checkensemble_edf.anonymize_edf_header(file)# for anonymizationensemble_edf.rename_for_ensemble(file)# for renaming2) File is .brmfromensemble_eegimportbrm_to_edffromensemble_eegimportensemble_edfbrm_file='path/2/your/brm/file'brm_to_edf.convert_brm_to_edf(brm_file)# for conversion, output edf is already anonymizededf_file='path/2/your/edf/file'# check which file was made in previous stepensemble_edf.rename_for_ensemble(edf_file)# for renaming3) Files are .edf, but left and right channel are seperatefromensemble_eegimportensemble_edfleft_file='path/2/your/left/edf/file'right_file='path/2/your/right/edf/file'ensemble_edf.combine_aeeg_channels(left_file,right_file)# output is automatically anonymizedensemble_edf.rename_for_ensemble(file)# for renaming4) Anonymize multiple .edf files in the same directoryfromensemble_eegimportensemble_edfimportglobimportosedf_directory='path/2/your/left/edf/directory'edf_files=glob.glob(os.path.join(edf_directory,"*.edf"))forfileinedf_files:ensemble_edf.fix_edf_header(file)ensemble_edf.anonymize_edf_header(file)ensemble_edf.rename_for_ensemble(file)5) Convert multiple .brm files in the same directoryfromensemble_eegimportbrm_to_edfimportglobimportosbrm_directory='path/2/your/left/edf/directory'brm_files=glob.glob(os.path.join(brm_directory,"*.brm"))forfileinbrm_files:brm_to_edf.convert_brm_to_edf(file)For more scripts, please refer to thedemosfolderAcknowledgementsedfrdInstall-Python-Instructions