Saravanan666/pypi-org huggingface dataset:
Package: cpskin.diazotheme.trendybasic
This is a Cpskin Diazo themeInstallationAdd this to your buildout.cfg, or do something equivalentdevelop += src/cpskin.diazotheme.trendybasic

[instance]
eggs += cpskin.diazotheme.trendybasic
zcml += cpskin.diazotheme.trendybasicInstall the package in the quickinstallerActivate the diazo themeContributorsIMIO,support@imio.beLaurent Lasudry [Affinitic]Changelog0.2.10 (2018-09-11)Change css for #visual-portal-wrapper.
[mgennart]0.2.9 (2018-09-05)Add uninstall profile
[laulaz]Change css for icon to portlet
[mgennart]0.2.8 (2018-07-02)Delete CSS icone ::before to portlet.
[mgennart]0.2.7 (2017-12-20)Change width to .section-notheme #portal-header ul.navTreeLevel0.
[mgennart]0.2.6 (2017-11-06)Changing the column width and aligning the content in the homepage.
[mgennart]0.2.5 (2017-10-12)change manifest.
[mgennart]0.2.4 (2017-08-04)Change padding of #portal-globalnav li a.
[mgennart]0.2.3 (2017-07-25)Delete height to #visual-portal-wrapper.
[mgennart]0.2.2 (2017-07-17)Remove unused skin.
[bsuttor]0.2.1 (2017-07-12)Change css navigation for LESS.
[mgennart]0.2 (2017-05-29)Ensure cpskin.theme is migrated to LESS before migrating this theme
[laulaz]Complete migration to LESS
[laulaz]Add missing theme parameters (harmonization)
[laulaz]Buildout harmonization & add omelette
[laulaz]Fix IMIO pypi URL
[laulaz]0.1.4 (2017-05-17)Duplicate CSS rules for now to handle #slider -> #slider-a-la-une and
#carousel -> #carousel-a-la-une ids at the same time (#16991)
[laulaz]0.1.3 (2016-04-21)Add a new method to load NamedBlodImage in Plone Image Object (setuphandlers.py).0.1.2 (2015-06-22)Nothing changed yet.0.1.1 (2015-03-19)Fix randomly broken html (no head, …)
See details inhttp://trac.imio.be/trac/ticket/106150.1 (2015-02-19)Initial release.
Package: cpskin.diazotheme.vicinity
This is a Cpskin Diazo themeInstallationAdd this to your buildout.cfg, or do something equivalentdevelop += src/cpskin.diazotheme.vicinity

[instance]
eggs += cpskin.diazotheme.vicinity
zcml += cpskin.diazotheme.vicinityInstall the package in the quickinstallerActivate the diazo themeScreenshotContributorsIMIO,support@imio.beChangelog0.6 (2022-01-20)Revert last release to handle cookies viewlet in cpskin.core directly
[laulaz]0.5 (2022-01-20)Add cookies consent viewlet in minisite mode (in Diazo rules)
[laulaz]0.4 (2019-03-19)CSS fixes for Vincinity
[osnickers]CSS fixes for minisite dropdown menu
[laulaz]0.3 (2019-03-04)Fix menus order : portal dropdown menu is before minisite menu : MWEBOTT-20
[laulaz]Add css for dropdown menu for mini site : MWEBOTT-2
[mgennart]Add dropdown menu for mini site : WEB-2928
[mgennart]0.2 (2019-01-08)Add top slider : WEB-2407Add css for responsive.
[mgennart]Add class for collapsable minisite menu : WEB-2865
[mgennart]0.1 (2018-06-12)Initial release.
Package: cpskin.localfood
InstallationInstall cpskin.localfood by adding it to your buildout:[buildout]

...

eggs =
    cpskin.localfoodand then runningbin/buildoutContributeIssue Tracker:https://github.com/collective/cpskin.localfood/issuesSource Code:https://github.com/collective/cpskin.localfoodDocumentation:https://docs.plone.org/foo/barLicenseThe project is licensed under the GPLv2.ContributorsMartin Peeters [Affinitic], Original AuthorChangelog1.0.3 (2018-09-06)Use collection image scale in priority in faceted-cards
[ndemonte]1.0.2 (2018-04-23)Send mail when new professional match found
[ndemonte, mpeeters]Remove thecontact_byfield
[mpeeters]1.0.1 (2018-04-19)Change the cut for pictures on projects and community gardens
[mpeeters]Display the organization logo on faceted view
[mpeeters]1.0a10 (2018-04-16)Fix render of the form and cleanup
[mpeeters]1.0a9 (2018-04-16)Do not give access to anonymous users to susbscription form
[mpeeters]1.0a8 (2018-04-15)Add missing french translations
[mpeeters]1.0a7 (2018-04-15)Add a view with the map for the homepage
[mpeeters]Add professionals views (subscription and matchmaking)
[ndemonte, mpeeters]1.0a6 (2018-04-12)Add more field to theIContactCardbehavior
[mpeeters]Change add permission and add the IContactCard behavior to content types
[mpeeters]Add producer/buyer matchmaking, validation & product selection
[ndemonte]1.0a5 (2018-04-10)Fix an Acquisition problem
[mpeeters]1.0a4 (2018-04-08)Extend the addable types for homepage view
[mpeeters]Add the documents and images to the community garden view
[mpeeters]Add an overlay for project images
[mpeeters]Add a behavior and viewlet for labels
[mpeeters]Add a behavior and viewlet for contact cards
[mpeeters]Update faceted card view
[mpeeters]Include collective.behavior.richdescription for faceted descriptions
[mpeeters]Community Garden must be folderish and update allowed types
[mpeeters]1.0a3 (2018-04-04)Add custom views for projects and community gardens
[mpeeters]1.0a2 (2018-04-04)Add the rich text behavior toCommunity GardenandProjectcontent types
[mpeeters]1.0a1 (2018-03-30)Initial release.
[mpeeters, ndemonte]
Package: cpskin.workflow
ContentsIntroductionTestsContributorsChangelog0.7.2 (2020-10-27)0.7.1 (2019-03-13)0.7.0 (2019-03-01)0.6.4 (2017-10-04)0.6.3 (2017-01-31)0.6.2 (2016-10-02)0.6.1 (2016-08-19)0.6 (2016-08-18)0.5 (2016-07-27)0.4 (2016-05-10)0.3 (2016-02-12)0.2 (2016-01-19)0.1 (2014-07-02)IntroductionWorkflow package for cpskinTestsThis package is tested using Travis CI. The current status is:ContributorsLaurent Lasudry, Original Author [Affinitic]Jean-François Roche, Original Author [Affinitic]Benoît Suttor, Original Author [iMio]Changelog0.7.2 (2020-10-27)Upload to pypi.org.
[bsuttor]0.7.1 (2019-03-13)Update View private content permission, adding “List folder contents” and
“Access contents information”: WEBNAM-69.
[bsuttor]0.7.0 (2019-03-01)Add View private content permission : WEBNAM-69.
[bsuttor]0.6.4 (2017-10-04)Add ‘Private’ state in cpskin_moderation_workflow : #18850
[laulaz]0.6.3 (2017-01-31)Add usable upgrade step for adding collective.contact workflow.
[bsuttor]0.6.2 (2016-10-02)Add missing i18n attributes for workflows state ids
[mpeeters]0.6.1 (2016-08-19)Fix collective.contact features by keeping states ids in workflow
[laulaz]0.6 (2016-08-18)Add workflow for collective.contact contents with created / published states
to allow anonymous user to view published contents (#14368)
[laulaz]Add Dexterity tests.
[bsuttor]0.5 (2016-07-27)Add new Private state for cpskin_workflow (#14475)
[laulaz]Fix tests
[laulaz]0.4 (2016-05-10)Remove old AT references into members-policy.
[bsuttor]Add event on dexterity content for setting exclude_from_nav.
[bsuttor]0.3 (2016-02-12)Add dexterity support.
[bsuttor]0.2 (2016-01-19)Add dependency workflow to import_step.xml.
[bsuttor]0.1 (2014-07-02)Initial release
Package: cpslo-orbits
Orbital MechanicsSet of Orbital Mechanics functions transcribed from MATLAB or textbooks in Python to support larger Orbital Mechanics projects and classes.
Check out the official Pypi uploadhere!image source:WikipediaInstallationMethod 1: PipRun the following command in the command line to install the library via pip:pip install cpslo_orbitsCheck to make sure it was installed with the following command:pip listMethod 2: Local InstallationTo install a local copy e.g., for development clone the repository in the directory of your choosing:git clone https://github.com/gagandeepthapar/OrbitalMechanics.gitand install the required libraries from therequirements.txtfile:pip install -r requirements.txtand install the library locally:pip install -e .Ensure the library is installed by runningpip listor by runningpytest:cd tests/
pytestPytest should run and indicate the failed/passed tests.
Note: some tests may fail as there is ongoing development to create thorough test cases for each method presented in this module.StructureThe repository is broken up into 4 main packages:orbitalcore (Basic Orbital Mechanics)relativemotion (Rendezvous/Relative Motion Mechanics)perturbations (Perturbational Effects)observations (Observations/Filtering)This structure closely follows the breakdown of the Orbital Mechanics path at California Polytechnic State University - San Luis Obispo: AERO 351 (Basic Orbital Mechanics), AERO 452 (Rendezvous/Relative Motion, Perturbational Effects), and AERO 557 (Observations/Filtering, Optimal Transfer).ApproachThe approach in building this repository was to avoid using unnecessary classes and containers to minimize the learning curve and to provide the simplest form of usability for students and faculty. That being said, NumPy and MatPlotLib are heavily used throughout the repository to enable the math to be simply executed. Many optimization opportunities were not taken to minimize confusion and enable users to peek into the source code and understand what is happening.Extending the PackageI hope students will continue to add on to this package and improve usability for the curriculum. My intentions were to provide resources (e.g., pointing to specific textbooks/algorithms) for people to refer back to and ensure the code is very readable.Extensions/additional functions should always include type-hinting in the arguments and return statement and should include docstrings at the top of each function for context. The function docstring should include the purpose, where the function is adapted from (e.g., "Orbital Mechanics for Engineering Students", Curtis), and the explanations for each input and return argument.Do as I say and not as I do and use proper git-techniques by creating branches from main to develop features/fixes and pulling intomainas opposed to developing everything directly inmain.Organize the package thoughtfully. Only put core methods inorbitalcore, perturbations inperturbations, etc. If a method is sufficiently unique then feel free to create a new module e.g., for optimal orbit transfers.
Package: cp_sqlalchemy
CherryPy-SQLAlchemy makes it easy to use SQLAlchemy within CherryPy
apps.CreditsThis package was primarily created fromSylvain’s SQLAlchemy
recipe.Basic UsageThere are two elements of CherryPy-SQLAlchemyA CherryPy tool that will create a session for use with each
request.A CherryPy plugin that can maintains information about the
database and binds the session to the specific DB.Here is an example connecting to a sqlite db:import cherrypy

from app import Root
from app.models import ORMBase
from cp_sqlalchemy import SQLAlchemyTool, SQLAlchemyPlugin


def run():
    cherrypy.tools.db = SQLAlchemyTool()
    cherrypy.tree.mount(Root(), '/', {
        '/': {
            'tools.db.on': True
        }
    })

    SQLAlchemyPlugin(
        cherrypy.engine, ORMBase, 'sqlite:////path/to/file.db'
    )

    cherrypy.engine.start()
    cherrypy.engine.block()One thin to note is the ORMBase we imported issqlalchemy.ext.declarative.declarative_base()that was used when
creating models.From there, each request will have access tocherrypy.request.db,
which is an instance of a SQLAlchemy session.There is a more completeexample.pyin the source.Free software: BSD licenseDocumentation:https://cp_sqlalchemy.readthedocs.org.
Package: cpstl
Python Copy and Paste standard LibraryPython Copy and Paste standard library (CPSTL) is a repository with a collection of data structure and algorithms.TODOAdd more contentLicenseCollection of algorithm's and data struct developer in different language
and with the Code Style guide line, to resolve Competitive Programming problem.
Copyright (C) 2020-2021 Vincenzo Palazzo vincenzopalazzodev@gmail.com

This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
Package: cpsvis
Convex Projective Surface Visualisation ToolThis GitHub project is dedicated to producing accurate and robust implementations of numerous algorithms in the study convex projective surfaces and the length spectra on them. These algorithms are founded inthis paper(Haraway, Löwe, Tate, Tillmann 2019).InstallationThis application is available onPyPI. To run, it requires a working installation oftkinter. For installation, execute the following commands based on the distribution used.Ubuntu Installationsudo apt-get install python3 python3-tk
python3 -m pip install cpsvisWindows & Mac (tkinter should already be distributed)pip3 install cpsvis
Package: cpt
CPTWhat is it ?This project is a cython open-source implementation of the Compact Prediction Tree algorithm using multithreading.CPT is a sequence prediction model. It is a highly explainable model specialized in predicting the next element of a sequence over a finite alphabet.This implementation is based on the following research papers:http://www.philippe-fournier-viger.com/ADMA2013_Compact_Prediction_trees.pdfhttp://www.philippe-fournier-viger.com/spmf/PAKDD2015_Compact_Prediction_tree+.pdfInstallationYou can simply usepip install cpt.Simple exampleYou can test the model with the following code:fromcpt.cptimportCptmodel=Cpt()model.fit([['hello','world'],['hello','this','is','me'],['hello','me']])model.predict([['hello'],['hello','this']])# Output: ['me', 'is']For an example with the compatibility with sklearn, you should check thedocumentation.FeaturesTrainThe model can be trained with thefitmethod.If needed the model can be retrained with the same method. It adds new sequences to the model and do not remove the old ones.MultithreadingThe predictions are launched by default with multithreading with OpenMP.The predictions can also be launched in a single thread with the optionmultithread=Falsein thepredictmethod.You can control the number of threads by setting the following environment variableOMP_NUM_THREADS.PicklingYou can pickle the model to save it, and load it later via pickle library.fromcpt.cptimportCptimportpicklemodel=Cpt()model.fit([['hello','world']])dumped=pickle.dumps(model)unpickled_model=pickle.loads(dumped)print(model==unpickled_model)ExplainabilityThe CPT class has several methods to explain the predictions.You can see which elements are considered asnoise(with a low presence in sequences) withmodel.compute_noisy_items(noise_ratio).You can retrieve trained sequences withmodel.retrieve_sequence(id).You can find similar sequences withfind_similar_sequences(sequence).You can not yet retrieve automatically all similar sequences with the noise reduction technique.TuningCPT has 3 meta parameters that need to be tuned. You can check how to tune them in thedocumentation. To tune you can use themodel_selectionmodule fromsklearn, you can find an examplehereon how to.BenchmarkThe benchmark has been made on the FIFA dataset, the data can be found on theSPMF website.Using multithreading,CPTwas able to perform around 5000 predictions per second.Without multithreading,CPTpredicted around 1650 sequences per second.Details on the benchmark can be foundhere.Further readingA study has been made on how to reduce dataset size, and so training / testing time using PageRank on the dataset.The study has been published in IJIKM reviewhere. An overall performance improvement of 10-40% has been observed with this technique on the prediction time without any accuracy loss.One of the co-author ofCPThas also published an algorithmsubseqfor sequence prediction. An implementation can be foundhereSupportIf you enjoy the project and wish to support me, abuymeacoffee linkis available.
Package: cp-tcgen
tcgenYet another test case generator (But hopefully better)Quickly and painlessly generate testcases with a minimal amount of codeInstallationpython3.7-mpipinstallcp-tcgenCapabilitiestcgen creates several endpoints to generate data with several different methodsPrimitivesPrimitive
Integer
Bool
Float
Char
PrimeDatatypesArray
String
NonDecreasing
StrictlyIncreasing
Permutation
Graph
Tree
LineGraph
Grid
DAG
StarGraph
KRegularTreeExampleshttps://dmoj.ca/problem/dpafromtcgenimport*# Bounds are 1..1e5 by defaultN=Integer(2,100000)print(N)print(Array(N,Integer(10000)))https://dmoj.ca/problem/dpbfromtcgenimport*classGen(Generator):defgenerate(self,case_num):N=Integer(L=2,wcnt=20)# Weighted randomK=Integer(100)self.p(N,K)self.p(Array(N,U=10000))gen=Gen()print(gen.get_test_cases(10))print(gen.get_test_case())fromtcgenimport*A,B=Integer(),Integer()print(A,B)# 1 <= N <= A+BN=Integer(A+B)print(N)fromtcgenimport*N=Integer()print(N)M=Integer(2*N)print(M)print(Graph(N,M).shuffle())fromtcgenimport*N=Integer()print(N)print(StrictlyIncreasing(N).shuffle())
Package: cptec-model
FrameworkDescrição
É um pacote in Python para a distribuição de dados brutos dos Modelos Numéricos de forma segmentada/particionada. Com esse pacote o usuário não necessita fazer o Download de todo o volume bruto o pacote auxilia a manipular somente a sua necessidade.support Python >= 3.10.Instalação
conda create -n  python=3.10conda activateconda install -c conda-forge matplotlib pycurl cfgrib netCDF4 pynio xarray dask esmpy scipy mpi4py xesmfconda install -c anaconda ipykernelpip install -ihttps://test.pypi.org/simple/Como Usar
import cptecmodel.CPTEC_BAM as BAMbam = BAM.model()date = '2023010800'vars = ['t', 'u10m']levels = [1000, 850]steps = 1f = bam.load(date=date, var=vars,level=levels, steps=steps)
Package: cptec-subsaz
Framework CPTEC SubSazonalÉ um pacote in Python para a distribuição de dados brutos de previsão subsazonal do CPTEC/INPE
produzidos com o modelo BAM-1.2. Os arquivos de dados contêm a média do conjunto
(de 11 membros) para todos os produtos e variáveis mostradas no sitehttps://subsazonal.cptec.inpe.br/.Esses arquivos estão disponíveis como anomalias computadas referentes à climatologia 1999-2018.Documentacao completa do Projeto -https://sazonal.readthedocs.io/en/latest/index.html
Package: cptemplate
cptemplateModule for creating a pre-defined competitive programming template for pythonView on PyPi:https://pypi.org/project/cptemplate/Installationcptemplatecan be installed using pip, and it is an OS independent moduleWindowspipinstallcptemplateLinuxpip3installcptemplateUsageUsage: cptemplate [OPTIONS] COMMAND [ARGS]...

  competitive programming template for python

Options:
  --help  Show this message and exit.

Commands:
  info  information about the module
  new   create a new template file
  test  run and test the codecptemplatecommandscptemplatecan be used by directly typing the commandcptemplatein CMD/TerminalFor creating a new template file, the commandcptemplate newis used, which by default creates anew.pyfile in the directory wherecptemplatecommand is run. By usingcptemplate new -n <file_name>, template with namefile_namewill be created.Usage: cptemplate new [OPTIONS]

  create a new template file, default name new.py

Options:
  -n, --file_name TEXT  Set custom name for file
  --help                Show this message and exit.default command, creates filenew.pycptemplatenew-n <file_name>command, creates file with namefile_namecptemplatenew-nmainFor running/testing the file, the commandcptemplate testis used to runnew.py(default command). To open a file with custom name,
the commandcptemplate test -n <file_name>is used, to runfile_name.pyfileUsage: cptemplate test [OPTIONS]

  run and test the code, by default runs new.py

Options:
  -n, --file_name TEXT  Open .py file with custom name
  --help                Show this message and exit.runs by defaultnew.pycptemplatetestrunsfile_name.pycptemplatetest-nmainContributingContributions can be made to the project by making PR's :)LicenseMIT License
Package: cp-template
cp-templateThis is a very simple utility to generate directories based on templates.Installpipinstallcp-templateUsagecp-template TEMPLATE_PATH DESTINATION_PATH key=value ...Suppose you have the following directory structure (Note: the {{}}s are part of the filenames){{project}}/
  .gitignore
  README.md        # File contains "{{project}} by {{author}}"
  {{project}}/
    __init__.pyThen you can run the following command:cp-template './{{project}}' pineapple author=meAnd it will generate this in the current directory:pineapple/
  .gitignore
  README.md        # File contains "pineapple by me"
  pineapple/
    __init__.pyThe template directory does not have to contain{{...}}. If it does not, the stem of the destination path (last part of the path minus the extension) is placed in theSTEMvariable, so you can refer to it in the files using{{STEM}}.More features will be added as I need them, but feel free to make PRs to contribute some.
Package: cpthook
If you use a central repository manager such as gitolite
or gitosis you probably have at least a few repositories using custom git
hooks.Managing the hooks in all your repositories by hand, is time consuming
and prone to error. If you need to take advantage of the functionality
provided by more than one hook script you are left to do it yourself.cpthook allows you to run more than one hook in each repo and manage
them all in one location.
Package: cptk
Competitive Programming Tool KitThe Competitive Programming Tool Kit(cptkfor short), is a command-line interface (CLI) that aims to be your personal assistant for everything competitive-programming-related. Some of the main features of cptk are:Supports all large competitive programming websites, includingcodeforces.com,cses.fiandkattis.comAutomatically download and run example test casesManage and catalog the solutions in your competitive programming folderFull git integration: commits and pushes solutions after you submit themAnd much more!Are you using cptk?Show us love and display thebadge in your repository!Getting startedInstallationThe Competitive Programming Tool Kit is implemented fully in Python and is
distributed using the Python Package Index (PyPI).To use cptk make sure you haveCPython>=3.7installed.
Then, install cptk usingpip:pipinstallcptkYou can check that the installation went successfully by running:cptk--helpIf you are experiencing problems while installing cptk, check out the more
detailedInstallation Guide.ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog,
and this project adheres toSemantic Versioning.0.1.0a3- 28.2.2022FixedDefault configurations and project structures are now bundled with the wheel
release.This resolves anUNEXPECTED ERRORwhen trying to run thecptk initsubcommand for wheel installs (including ones provided bypipfrom PyPI).0.1.0a2- 26.2.2022This alpha release adds thecptk testsubcommand to cptk! 🥳Starting from this release the project configuration files supports a new
field calledtest. It will be automatically created for you when you
initialize a new cptk project usingcptk init, and using it cptk will know
where to store the scraped sample tests when cloning problems.By default, runningcptk testwill try and run the tests for the last problem
that is known to cptk. It is also easy to add your custom test cases that will
run when executingcptk test: just add.inand.outtext files to the
problem's testing folder!For more information and a full tutorial on thecptk testsubcommand,
you are more than welcome to visit ourwiki page.AddedThecptk testsubcommand!Optionaltestfield for each recipe in therecipes.cptk.yamlconfiguration fileOptionalclone.recipe.testfield in theproject.cptk.yamlconfiguration file0.1.0a1- 16.2.2022FixedDefault templates are now bundled with the package,
which fixes the bug incptk init.0.1.0a0- 15.2.2022The first (alpha) release of cptk! 🥳
Feel free to use the software and report any bugs that you find!
Enjoy and have a nice day! 😃AddedSupported commands are:cptk initialize,cptk clone,cptk move,cptk bakeandcptk serve.