Saravanan666/pypi-org huggingface dataset:
Package: sealedsecretmgr
sealedsecret: A tool to manageSealedSecretsInstallationpip install sealedsecretmgrUsageTo list existing SealedSecrets with keys in your namespace:$ sealedsecret list
my-super-secret
	DATABASE_PASSWORDYou can pass an optional--namespaceor-nargument before any command:$ sealedsecret -n dev list
my-super-secret
	DATABASE_PASSWORDTo retrieve and view a SealedSecret you can get it.sealedsecret get secret-nameTo create a new SealedSecret:sealedsecret create new-secret-name my-key my-value-to-protectTo update a SealedSecret file on disk, you can use the--merge-intoflag of the create command:sealedsecret create my-super-secret DATABASE_PASSWORD new-valueYou can use this command to add new keys or edit existing keys.To add a key or edit an existing key in an exitsing SealedSecret:sealedsecret update existing-secret-name my-new-key a-valueThe update and create commands only print the resource, you can redirect the output of edit an update to a file and then apply it usingkubectl apply -for you can pipe directly tokubectl apply -. You can use the global argument-oto choose between JSON or the default YAML output.``
sealedsecret -o json create a-secret a-key a-value
Package: sealights-python-agent
The sealights-python-agent package integrates with the Sealights Quality Intelligence Platform.Language SupportPython 3.6Python 3.7Python 3.8Python 3.9Python 3.10Installation$ pip install sealights-python-agentUsageGenerating a session ID$ sl-python config --appname myApp --branchname master --buildname 1 --exclude "*venv*"Scanning a build$ sl-python buildRunning your tests3.1 Running tests withunittest$ sl-python unittest --teststage "Unit Tests" <your args...>3.2 Running tests withpytest$ sl-python pytest --teststage "Unit Tests" <your args...>3.3 Running tests withunittest2$ sl-python unit2 --teststage "Unit Tests" <your args...>Changes1.0.14 (2022-11-20)Added support for Python 3.8, 3.9, 3.101.0.6 (2022-08-30)Fixed “TIA Not Supported” Indicator for Test Optimization1.0.5 (2022-07-05)Dropped support for Python 2.7, 3.4 and 3.5.Upgraded dependenciesUsing coverage.py 6Bugfix: build session ids and lab ids were not encoded0.2.247 (2022-05-19)feature - tests can now run using labid only if build session id is not known.Relies on at least one app that is active and configured with the same lab id.0.2.245 (2022-03-28)bugfix - test recommendations were running more tests than expected0.2.244 (2022-02-20)Added support for pytest-xdist version 2 and above0.2.243 (2022-02-16)bugfix - test recommendations API upgraded to v30.2.240 (2021-12-02)entry log “Not upgrading agent” is now INFO0.2.238 (2021-07-22)bugfix - nose args were parsed incorrectly. First arg was ignored.0.2.234 (2020-05-06)bugfix - wrong xml coverage report when using pytest-xdist0.2.230 (2020-03-11)Handling not ready test recommendations0.2.229 (2020-03-11)Added better test recommendations error handling0.2.228 (2020-02-22)Fixed bug when running the agent after dependency update - gitdb2 - ModuleNotFoundError0.2.219 (2019-10-30)Pytest test selection support0.2.213 (2019-10-16)Python 3.7 support0.2.201 (2019-07-14)Use pytest + nose exit code0.2.199 (2019-02-24)Performance optimizationAdded –per-test flag for pytest, nose, unittest, unit2 and run commands. default is on.0.2.198 (2019-01-21)changed default scm provider from github to null0.2.197 (2019-01-14)added –cov-report option to run command
Package: sealion-conda
TODOs[done] sealion-conda clone -b /path/to/conda/base -e /path/to/local/conda/to/be/cloned/env new_conda_envsealion-conda pkg -b /path/to/conda/base -e /path/to/local/conda/to/be/cloned/env /path/to/save/pkgs.zipsealion-conda create -f /path/to/save/pkgs.zip new_conda_envcheck if exists: conda, conda env, new conda env
Package: seam
seam====Seam is a simple layer between standard neuroimaging tools and your data. It generates useful commands around these tools so you can apply them against your data.Seam makes no decisions about the organization of your data or how the generated commands will ultimately be executed. That is up to you as the scientist & engineer.Initial planned support---------------- Freesurfer's `recon-all` and `trac-all` commands with helper functions for taking `tksurfer` & `tkmedit` screenshots, etc.- FSL's `dtifit`, `bedpost` & `probtrackx2`.- Others as contributed or determined usefulPhilosophy----------Software is best written in layers. Each layer should encapsulate knowledge about how to best use the next lower layer. Its functionality should be exposed through as simple an API as possible.Seam will have no dependencies and minimal effort will be required to use it. It should integrate into any application ranging from a single script to something much more complicated.A simple example----------------```pythonfrom seam.freesurfer import recon_allsubject_data = {'sub001': '/path/to/first/t1.nii','sub002': '/path/to/other/t1.nii','sub003': '/path/to/third/t1.nii'}for subject_id, path_to_t1 in subject_data.iteritems():recon_command = recon_all(input=path_to_t1)script_name = 'recon_{}.sh'.format(subject_id)with open(script_name, 'w') as f:f.write(recon_command)```The generated `recon_all` commands will be the exact three for each subject,  though obviously with different inputs.Support/Questions/Development-----------------------------Seam is very much a work-in-progress. It is supported through [the Vanderbilt Univeristy Institute for Imaging Science][1]. Please use github for questions and issues but I cannot guarantee support.[1]:    http://vuiis.vanderbilt.edu
Package: seam-carver
seam_carver is a small tool for retargetting images to any dimension greater or smaller.
It uses the process of seam carving as originally described by Shai Avidan & Ariel Shamir inhttp://perso.crans.org/frenoy/matlab2012/seamcarving.pdfA combination of the gradient energy (determined with the sobel filter) and a simple color energy is
used to determine the least important seam. Additon of seams occurs by the same mechanism as described
in the paper.
Package: seam-carving
Seam CarvingA super-fast Python implementation ofSeam carving for content-aware image resizing, and the forward energy function proposed inImproved seam carving for video retargeting.With seam carving algorithm, the image could be intelligently resized while keeping the important contents undistorted. The carving process could be further guided, so that an object could be removed from the image without apparent artifacts.InstallationInstall a stable version from PyPI.pipinstallseam-carvingOr install the latest version from GitHub.pipinstallgit+https://github.com/li-plus/seam-carving.git@masterQuick StartTo scale an image, useseam_carving.resizemethod.importnumpyasnpfromPILimportImageimportseam_carvingsrc=np.array(Image.open('fig/castle.jpg'))src_h,src_w,_=src.shapedst=seam_carving.resize(src,(src_w-200,src_h),energy_mode='backward',# Choose from {backward, forward}order='width-first',# Choose from {width-first, height-first}keep_mask=None)Image.fromarray(dst).show()To remove an object from an image, useseam_carving.remove_objectmethod.src=np.array(Image.open('fig/beach.jpg'))mask=np.array(Image.open('fig/beach_girl.png').convert('L'))dst=seam_carving.remove_object(src,drop_mask=mask,keep_mask=None)Image.fromarray(dst).show()For more examples, please refer toexample/demo.py.Example ResultsScaling Up & DownResizing along the x-axis using original backward energy function.Backward Energy vs Forward EnergyReduce the width of the bench image using backward & forward energy function.Aspect Ratio ChangeThe image width and height could be changed simultaneously. The order of vertical and horizontal seams has little to do with the final results. Currently we only support two kinds of seams-order:width-firstandheight-first. Inwidth-firstmode, we remove/insert all vertical seams first, and then the horizontal ones, whileheight-firstis the opposite.Object ProtectionThe protected mask is free from seam removal and insertion.Object RemovalSpecify an object mask to remove (red) and a mask to protect (green, optional).BenchmarksWe compare the performance of our implementation and other popular Python repos oncastle.jpg. The image is narrowed or widened by 200 pixels using backward energy (BE) or forward energy (FE), respectively. Below is the running time (second) evaluated on a MacBook Pro.MethodsBE -200pxBE +200pxFE -200pxFE +200pxvivianhylee/seam-carving192.63217.04109.33108.53sameeptandon/python-seam-carving91.67124.21N/AN/Aandrewdcampbell/seam-carving91.3890.8998.47102.84dharness/seam_carving59.8173.04N/AN/AOurs1.121.141.131.22ReferencesAvidan, S., & Shamir, A. (2007). Seam carving for content-aware image resizing. In ACM SIGGRAPH 2007 papers (pp. 10-es).[paper][blog]Rubinstein, M., Shamir, A., & Avidan, S. (2008). Improved seam carving for video retargeting. ACM transactions on graphics (TOG), 27(3), 1-9.[paper]Das, A. (2019). Improved seam carving with forward energy.[blog]
Package: seamless
seamless provides an easy way to obtain an https session token via ssh.
It automates creation of user accounts and manages theirauthorized_keysas well as session token creation via forced ssh commands.
The authentication flow is as follows:A client connects toseamless-realm@seamless-hostvia ssh and authenticates with a public key.
The key is restricted to only execute the token creation command of the seamless binary.
The seamless command returns session token that is signed with a secret specific toseamless-realm.$ ssh -T seamless-realm@seamless-host
username.VgfAwA.v-xKIZh3qYawqcm2RRh4q-LPfVEThe client sends the obtained token in the HTTPAuthorizationheader of its requests toprotected-app.
The app uses the shared secret to validate the token.GET/HTTP/1.1Host:protected-appAuthorization:seamless username.VgfAwA.v-xKIZh3qYawqcm2RRh4q-LPfVEInstallationFrom a deb package:$ wget https://github.com/emulbreh/seamless/releases/download/v0.1.0/seamless_0.1.0_amd64.deb
$ sudo dpkg -i seamless_0.1.0_amd64.debAs Python package:$ pip install seamlessSetting up a seamless realmA seamless realm is a user account onseamless-host. The creation and management ofauthorized_keysof these accounts is handled by seamless.$sudoseamlessinitseamless-realm$sudoseamlessaddseamless-realm/path/to/public/key--userusernameA user with this public key is now able to get tokens via ssh:$ ssh -T seamless-realm@seamless-host
username.VgfOLQ.EB6NTfXiyv7dWSKUMQJ38JXa5awor from Python>>>importseamless>>>seamless.get_token('seamless-realm@seamless-host')'username.VgfOBA.dRBDY5EUmQvhB8OnqPDWlC1tml4'Protecting a webservice with WSGI middlewareseamless ships with WSGI middleware that verifies that a valid seamless token is passed via the Authorization header.fromseamless.wsgiimportSeamlessMiddlewareapp=...app=SeamlessMiddleware(app,max_age=60,secret='...')Requests without a validAuthorizationheader will be rejected with a 401 response.Making requests to such a protected app is made easy with an auth plugin forrequests:importrequestsfromseamless.requestsimportSeamlessAuthsession=requests.Session()session.auth=SeamlessAuth('name@seamless-host')session.get('http://protected-app/')The token obtained fromseamless-hostis cached.
It will be be automatically refreshed when it expires, and the failing request retried.CaveatsIf token validation is performed on a different host than token creation, clock skew may result in tokens that expire too early or too late.
Package: seamlessf5
SeamlessF5SeamlessF5 adds useful missing features that helps making transitions to
Oxford Nanopore Technologies'snew multi-read FAST5 format.InstallationSeamlessF5 can be installed usingpipfor Python 3.5+.pipinstallseamlessf5Conversion from single FAST5 files intarto multi-read FAST5With thesf5_single_to_multi_fast5wrapper, ONT'ssingle_to_multi_fast5becomes capable of loading single-read FAST5 files fromtaror compressedtarfiles.sf5_single_to_multi_fast5-ifast5.tar.gz-ssave_path--recursiveRunning albacore for multi-read FAST5This package also installs wrappers,sf5_read_fast5_basecaller.py,sf5_full_1dsq_basecaller.py, andsf5_paired_read_basecaller.pywhich
enable loading multi-read FAST5 files for ONT's albacore.sf5_read_fast5basecaller.py-imultiread-fast5-dir...
Package: seamm-cookiecutter
Cookiecutter for SEAMM PluginsCookiecuttertemplate for a SEAMM plugin.GitHub repo:https://github.com/molssi-seamm/cookiecutter-seamm-pluginFree software: BSD licenseFeaturesTesting setup withunittestandpython setup.py testorpy.testCI/CD: Ready for Continuous Integration/Continuous Deployment using
GitHub Actions.Sphinxdocs: Documentation ready for generation with, for example,ReadTheDocsAuto-release toPyPIwhen you push a new tag to master (optional)QuickstartInstall the SEAMM Cookiecutter if you haven’t installed it yet (this requires
Cookiecutter 1.4.0 or higher):pip install -U seamm-cookiecutterGenerate a Python package project:seamm-cookiecutterAnd answer the prompts about your plug-in, etc. Then change to the new
directory:cd <plug-in name>Then create repository in GitHub with exactly the same name, just
giving the LICENSE type. Note that GitHub has changed the default
branch tomain, which is what the SEAMM cookiecutter also uses. Now
you need to merge the two:bash-3.2$ git remote add origin
git@github.com:<organization>/<plug-in name> .git
bash-3.2$ git pull --allow-unrelated-histories origin master
warning: no common commits
remote: Counting objects: 3, done.
remote: Compressing objects: 100% (2/2), done.
remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
Unpacking objects: 100% (3/3), done.
From github.com:<organization>/<plug-in name>
 * branch            main     -> FETCH_HEAD
 * [new branch]      main     -> origin/main
error: Terminal is dumb, but EDITOR unset
Not committing merge; use 'git commit' to complete the merge.Depending on what happens here, you may have a conflict in LICENSE. If
so, edit the file and fix the problem by keeping the appropriate lines.Once that is done, push to GitHub:bash-3.2$ git push --set-upstream origin main
Counting objects: 54, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (48/48), done.
Writing objects: 100% (54/54), 20.43 KiB | 261.00 KiB/s, done.
Total 54 (delta 10), reused 0 (delta 0)
remote: Resolving deltas: 100% (10/10), completed with 1 local object.
To github.com:<organization>/<plug-in name>.git
   30251d7..e2761fc  main -> main
Branch main set up to track remote branch main from origin.
bash-3.2$Deploy to PyPi. Once the code is in reasonable shape and working, you
can deploy to PyPi so that users canpip installit. You need an
account atPyPi.History2021.2.17 (17 February 2021)Initial working version on PyPi.
Package: seamm-dashboard-client
CMS PlotsDescriptionUtilities for creating plots for computational materials/molecular science (CMS)This package provides tools for creating plotly graphs for e.g. band structure, DOS, and
other specialized plots for CMS applications.Free software: LGPL.v3 or laterDocumentation:https://molssi-seamm.github.io/seamm_dashboard_client/index.htmlFeaturesDensity of states (DOS) plotsBand structure plotsCombined band structure & DOS plotsCreditsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from the National Science Foundation under
award ACI-1547580History2023.11.15 – Bugfix: boolean options now workBoolean options were not handled correctly when submitting jobs.2023.10.24 – Improvement for job handling.Added control parameters to the data stored for the job, to support filling out
menus identical to how the job was submitted.2023.7.29 – Bugfix: error if no required parametersApparently can’t use ‘–’ without subsequent parameters.2023.7.10 – Corrected handling of control parametersNow handle control parameters with multiple values.Separate the options from required parameters with ‘–’ as required.2023.6.28 – Improved error messages for login failures.2022.8.13 (13 August 2022)First release of a working version on PyPI.
Package: seamm-datastore
SEAMM DatastoreThis repository contains the SQLAlchemy models for the SEAMM datastore as well as some associated utilities such as dumping to JSON and checking permissions. These database models and permissions system were developed to be used inside a flask application context in theSEAMM Dashboard. However, you may use this package as a stand-alone (outside of flask) with limited permissions capabilities.QuickstartThis package contains SQLAlchemy models for the SEAMM datastore. The following gives an example of how to connect to a database in memory. You can switch the database by providing a different database URI.importseamm_datastore# Create a database sessionconnection=seamm_datastore.connect("sqlite:///:memory:")This will create a sqlite database stored in memory. Usinginitialize=Truewill result in a new database being created. You may substitute a different database URI in place ofsqlite:///memory. When connecting to a database on disk, you will need to specify an additional argument,initialize=True, if creating a new database.To login, use the login method. Your username is determined automatically by your username when runningconnectifinitializeisTrue.An admin user is also create which you can use to login (username=admin, password=admin).connection.login(username="YOUR_USERNAME",password="default")To import a datastore at a particular location, do:connection.import_datastore(FILEPATH)To use the sample data in this repository,jobs,projects=connection.import_datastore("seamm_datastore/data/Projects")JSON data of the added jobs and projects will be returned.TheSEAMMDatastoreclass has bound database models and a SQLAlchemy session factory which you can work with. These can be interacted with the same as other sqlalchemy models. To retrieve jobs for which you have "read" permissions from the database, use the bound SQLAlchemy models:jobs = connection.Job.permissions_query("read").all()To dump to json:fromseamm_datastore.database.schemaimportJobSchema# Create job schemajobs_schema=JobSchema(many=True)# To retrieve all users in dball_jobs=connection.Job.permissions_query("read").all()jobs_json=jobs_schema.dump(all_jobs)PermissionsThe SEAMM datastore has a permissions system built in usingflask-authorizefor authorization. This provides a "permissions" entry on each resource table (Jobs, Flowcharts, and Projects) where permissions for "owner", "group" and "world". The SEAMM datastore also has capabilities to set "special permissions" for users or groups on specific projects.Permissions are not automatically enforced when working directly with database models. In the SEAMM Dashboard, permissions are enforced withauthentication(verifying the user is who they say they are) using [flask-jwt-extended] andauthorizationusingflask-authorize.To use the permissions checking mechanisms of flask authorize outside of a flask app, use the helper function hereseamm_datastore.SEAMMDatastore.CopyrightCopyright (c) 2021, Jessica A. Nash (The Molecular Sciences Software Institute)AcknowledgementsProject based on theComputational Molecular Science Python Cookiecutterversion 1.5.
Package: seamm-exec
SEAMM exec[![codecov](https://codecov.io/gh/molssi-seamm/SEAMM exec/branch/main/graph/badge.svg)](https://codecov.io/gh/molssi-seamm/SEAMMexec/branch/main)Classes to execute background codes for SEAMMCopyrightCopyright (c) 2023, Paul SaxeAcknowledgementsProject based on theComputational Molecular Science Python Cookiecutterversion 1.1.
Package: seamm-ff-util
SEAMM Forcefield UtilitiesThe SEAMM Forcefield Utilities read and write forcefields, assigns them to molecules, and creates energy expressions.Free software: BSD licenseDocumentation:https://seamm_ff_util.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award OAC-1547580 and CHE-2136142.History2023.8.27 – Added support for tabulated angle potentials2023.4.6 – Added support for Buckingham potentialsAlso improved unit handling across all terms in forcefields.2023.3.5 – Added molecule numbers for LAMMPS inputAdded the molecule number for each atom for when using LAMMPS2023.2.6 – Added handling of OPLS-AA forcefieldAdded handling of the OPLS-AA forcefieldMoved documentation to new MolSSI theme and diátaxis layoutCleaned up internal dependencies and workflows for GitHub2022.5.29 – Fixed bug typing larger systemsFixed bug with atom typing due to limit in matches. by @paulsaxe in #592022.2.3 – Fixed bug due to changing ordering of atoms.Fixed bug with atom type assignment due to changed order of atoms. In the process,
switch to using RDKit directly, which is both more direct and avoids the ordering
problem.0.1.0 – (2017-12-05)First release on PyPI.
Package: seamm-installer
SEAMM Installer StepThe installer/updater for SEAMM.Free software: GNU Lesser General Public License v3+Documentation:https://molssi-seamm.github.io/seamm_installer/index.htmlFeaturesTODOCreditsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from the National Science Foundation under
award OAC-1547580 and CHE-2136142.History2023.11.3 BugFix: Internal update due to changes for versioneer2023.11.2 Bugfix: Fixed a crash casued by a minor change in the Zenodo API2023.6.7 Bugfix: Ensuring the LaunchAgents directory exists on Mac2023.4.2 Bugfix: Updating full conda environment breaks pinning.So removed that capability.2023.4.1.1 Bugfix: Problem with JobServer service2023.4.1 Bugfix: get latest version from Zenodo2023.3.31.1 Switching to ZenodoGetting the package list from ZenodoAdded ability to pin packages.2023.3.31Added new plug-in: QCArchive2023.3.23Added new plug-ins: Gaussian, TorchANI and NWChemUpdated creating the JobServer service to handle changes in the JobServer2022.10.25Added QuickMin and ensured the Dashboard is included.
Also hardened the code, handling more errors gracefully.