Saravanan666/pypi-org huggingface dataset:
Package: clearsilver
Clearsilver is a fast, powerful, and language-neutral HTML template system.  It
was designed through years of commercial web development at eGroups.com and
Yahoo!. Clearsilver’s speed comes from being written as a C module which is
exported for use in C, C++, Python, Perl, Ruby, and Java.The goal of Clearsilver is to get nearly all html, strings, and presentation
logic out of your code. By drastically reducing code size in your application,
readability and maintainability are improved. Furthermore, it becomes easier to
make UI changes without introducing bugs. Clearsilver unifies working with
cookies, form data, and the http environment, by making it trivial to access
them from either your code or templates in a standard fashion. It also makes it
very easy to skin the application, or allow end user UI customization.As a bonus, tools designed to work in concert with clearsilver are also
available, including an object to relational database mapping tool, odb.py, and
a transparent internationalization system, trans.py.Enjoy!
Package: clear-skies-auth-server
clearskies-auth-serverContains the handlers needed to manage an authentication server with clearskies.Getting startedpip install clear-skies-auth-serverClickhere for a detailed usage example.OverviewThis package provides the various pieces needed to manage an authentication server.  Specifically, it authenticates users and issues signed JSON Web Tokens (JWTs) for other services to validate.  This is intended for use as a stand-alone identity provider (IdP) and so requires a datastore where users (and, if needed, passwords) are validated.  Naturally, the datastore is provided via a clearskies model, and thus this can work with a wide variety of backends.In essence, you get to chose which parts of the auth server you need, provide a bit of configuration, and wrap the handlers up in a standardclearskies SimpleRouting handler.  Naturally, clearskies will auto-generate your OAI3 docs for you.Keep in mind that this is the backend API endpointsonly.  You'll have to provide your own frontend.HandlersThe following handlers are provided by this package to expose the necessary parts of the authorization server:generate-keysjwkspassword_loginpassword_less_email_request_loginpassword_less_validate_loginRoadmap for 1.0:MFAJWKSThis handler publishes the JSON Web Key Set (JWKS) that is necessary for JWT consumers to validate JWTs created by the service.Password LoginThis manages password-based logins.Password-less Email Request LoginFor a password-less login system, this allows a user to request a login.  Note that for minimalist systems, an explicit registration step is no longer required.Password-less Validate LoginValidates the login (and possibly completes registration) for a password-less login system.
Package: clear-skies-gql
clearskies-gqlclearskies bindings for Graph QL.  While this may eventually include handlers to create GQL servers, the current focus is just on a backend to interact with GQL servers.Installation, Documentation, and UsageTo install:pip3 install clear-skies-gqlUsageExampleInclude this in your binding modules, extend it if you need to specify authentication, and then use it in your models.import clearskies
import clearskies_gql

def process_pets_from_gql(pets):
    for pet in pets.where('name=fido'):
        print(pet.species)

class GqlBackendWithAuth(clearskies_gql.backends.GqlBackend):
    def __init__(self, requests, environment, logging, my_auth_method):
        super().__init__(requests, environment, logging)
        self.configure(
            url='https://gql.server.example.com',
            auth=my_auth_method,
        )

class Pet(clearskies.Model):
    def __init__(self, gql_backend_with_auth, columns):
        super().__init__(gql_backend_with_auth, columns)

    def columns_configuration(self):
        return OrderedDict([
            clearskies.column_types.string('name'),
            clearskies.column_types.string('species'),
        ])

process_pets = clearskies.Application(
    clearskies.handlers.Callable,
    {'callable': process_pets_from_gql},
    binding_modules=[clearskies_gql],
    binding_classes=[Pet, GqlBackendWithAuth],
)Then just wire up your application to a context and you're ready to go!Record nameclear-skies-gqlwill use the pluralized and snake_case version of the model class name as the descriptor of the records in GQL, e.g. the above model class results in this query:query Query {
  pets {
    name
    species
  }
}But you can override this behavior by specifying the table name for your model, e.g.:class Pet(clearskies.Model):
    def __init__(self, gql_backend_with_auth, columns):
        super().__init__(gql_backend_with_auth, columns)

    @classmethod
    def table_name(cls):
        return 'whateverYouWant'

    def columns_configuration(self):
        return OrderedDict([
            clearskies.column_types.string('name'),
            clearskies.column_types.string('species'),
        ])results in this query:query Query {
  whateverYouWant {
    name
    species
  }
}Server URLYou can also specify the URL to the GQL server by setting thegql_server_urlenvironment variable.  If your gql server doesn't require authentication, then you can use the graphql backend directly:# note: requires the gql_server_url environment variable to point to the server

class Pet(clearskies.Model):
    def __init__(self, gql_backend, columns):
        super().__init__(gql_backend, columns)

    def columns_configuration(self):
        return OrderedDict([
            clearskies.column_types.string('name'),
            clearskies.column_types.string('species'),
        ])
Package: clearskydays
# clearskydays# DescriptionA simple Python module that determines clear sky days based on solar radiation measurements.# Installationpip install clearskydays# Authors- Abel Delgado Villalba. Universidad Nacional de Asuncion, Paraguay. email: <adelgado@pol.una.py>- Marcelo de Paula Correa. Universidade Federal de Itajuba, Brazil. email: <mpcorrea@unifei.edu.br># Examplefrom clearskydays import Statisticsdata = Statistics(time_series) # input datadata.get_statistics()data.get_clear_sky_days()data.get_cloudy_days()Version 0.0.5 (20-06-2022 16:30 UTC)
Package: clearvision
AiReaderAdd a short description here!A longer description of your project goes here…Making Changes & ContributingThis project usespre-commit, please make sure to install it before making any
changes:pip install pre-commit
cd AiReader
pre-commit installIt is a good idea to update the hooks to the latest version:pre-commit autoupdateDon’t forget to tell your contributors to also install and use pre-commit.NoteThis project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: clearwind.arecibo
This package is an interface to the Arecibo application from ClearWind. It
provides an interface for Plone to report its errors.This is the version that is updated for use with Plone 4 and has not been
tested with previous versions but is likely to work with Plone 3 as well. It
also adds generic setup support and support for complete configuration from
the control panel.For more information see:http://areciboapp.comand specificallyhttp://www.areciboapp.com/docs/plone/Changelog0.7 (2013-08-02)Update getSite location for Plone 4.3 compatibility. [esteele]0.6 (2011-09-22)HTTP Post to throw-away threads [supton]remove incompatible 4.1 import [supton]0.5 (2011-06-20)Merge HISTORY.txt’s [eleddy]Add z3c.autoinclude to so that zcml slugs are not needed [eleddy]Get smtp_from from portal properties [eleddy]Add app_name to configuration panel so that the urls for posting
and email can be automatically generated [eleddy]Fix control panel icon [eleddy]GenerisSetup export/import [eleddy]Add configuration to ignore localhost errors [eleddy]Ignore errors based on the plone configuration of ignored errors [eleddy]0.2 > 0.3:Removed the skins and layersMade it catch all errors (even Zope ones) by patching SiteErrorLogAllowed the override of configuration values by the use of site_configuration.pyAdded in the ignores propertyMade uninstalling remove configletMade install optional (just site_configuration.py will be enough)Any errors sending to Arecibo are logged
Package: clease
CLEASECLuster Expansion in Atomic Simulation Environment (CLEASE) is a package that automates the cumbersome setup and construction procedure of cluster expansion (CE). It provides a comprehensive list of tools for specifying parameters for CE, generating training structures, fitting effective cluster interaction (ECI) values and running Monte Carlo simulations. A detailed description of the package can be found in thedocumentationand ourpaper.For information on how to contribute to CLEASE, please see thecontributingfile.InstallationInstall the CLEASE code by executingpipinstallcleaseAlternative, CLEASE is also available through anaconda oncondaviaconda-forge.
We recommend installing CLEASE via conda on windows machines in order to simplify compilations, as pip tends to have
a hard time compiling the C++ code. Install into your conda environment:condainstall-cconda-forgecleaseGraphical User InterfaceClease has a stand-alone jupyter notebook GUI, which is capable of performing most
of the standard CE routines. It can be foundhere.CLEASE GUI can be installed from PyPI or anaconda using one of the two following commands.PyPIpipinstallclease[gui]Anacondacondainstall-cconda-forgeclease-guiDevelopmentIf you are a developer you might want to install CLEASE by executing the following command in the root folder of the projectpipinstall-e.In order to run the tests, the testing dependencies should be installed. They can be installed with the extratestoptionpipinstall.[test]There is an additional option for development purposes,dev, which contains some convenience packages.
All of the extras options can be installed via thealloption,
i.e.pipinstall.[all]Note, that if you are usingzsh, you need toescape the argument, e.g.pipinstall'.[all]'TroubleshootingIf you are running on Mac and get the errorfatalerror:'ios'filenotfoundtry this before installingexportMACOSX_DEPLOYMENT_TARGET=10.14
Package: clease-gui
CLEASE GUIThis is a GUI based on Jupyter notebooks forCLEASE.
For more information, head on over to thedocumentation.InstallationThe CLEASE GUI is available onPyPI:$ pip install clease-guiThe GUI is also available onconda-forge.
This is also the prefered method on Windows, in order to simply the compilation process of CLEASE.
To install into your conda environment, do:$ conda install -c conda-forge clease-guiRunning the GUIThe GUI is based on Jupyter notebooks. The GUI works in both "classic" jupyter
notebooks, and in Jupyter lab. Launch jupyter lab with:$ jupyter-labAlternativelyto use the classic jupyter notebook, do:$ jupyter notebookCreate a new notebook that you want to work in, and a new cell, add the following lines:%matplotlibtkfromclease_guiimportdisplay_uidisplay_ui(dev_mode=True);A line%matplotlib notebookcan be added at the start of the cell for nicer plots
(this only works in classic jupyter notebooks).
However, this may come at a significant performance cost, so do your own testing.Plot font sizesAll plots generated in this package are generated by thematplotlibpackage. However, the default font size inmatplotlibcan be rather small,
so it can be adjusted globally, e.g. by running the following in a cell:importmatplotlibmatplotlib.rcParams.update({'font.size':16})This will also affect the font sizes on the figures generated in the GUI.
Package: clebs-puc-csv-converter
File ConverterCSV to JSON converter.IntroductionTeaching how to deploy a lib on PyPi at PUC using Poetry.What this project can doRead acsvfile or afolderwith csv`s and convert them toJSON.
This project is a program running on terminal, preferably install with pipx:pipxinstallclebs-puc-csv-converterTo use, just type in:csv_converter--helpThis will list all available options.
Package: clebs-puc-eng-dados-csv-converter
File ConverterCSV to JSON converter.IntroductionTeaching how to deploy a lib on PyPi at PUC using Poetry.What this project can doRead acsvfile or afolderwith csv`s and convert them toJSON.
This project is a program running on terminal, preferably install with pipx:pipxinstallclebs-puc-csv-converterTo use, just type in:csv_converter--helpThis will list all available options.
Package: clebs-puc-ia-csv-converter
File ConverterCSV to JSON converter.IntroductionTeaching how to deploy a lib on PyPi at PUC using Poetry.What this project can doRead acsvfile or afolderwith csv`s and convert them toJSON.
This project is a program running on terminal, preferably install with pipx:pipxinstallclebs-puc-csv-converterTo use, just type in:csv_converter--helpThis will list all available options.
Package: cleese-stim
CLEESE ("Ministry of Silly Speech") is a sound and image manipulation tool
designed to generate an infinite number of possible stimuli; be it
natural-sounding expressive variations around an original speech recording, or
variations on the expression of a human face.More precisely, CLEESE is currently composed of two engines:PhaseVocoderandMediapipe.PhaseVocoderallows one to create random fluctuations around an audio
file’s original contour of pitch, loudness, timbre and speed (i.e. roughly
defined, its prosody). One of its foreseen applications is the generation of
very many random voice stimuli for reverse correlation experiments.Mediapipeusesmediapipe's Face Mesh
API to introduce random or precomputed deformation in the expression of a
visage on an image. This engine was designed to produce batches of deformed
faces for reverse correlation experiments.CLEESE is a free, standalone Python module, distributed under an open-source
MIT Licence on the IRCAM Forumnet plateform. It was designed by Juan José
Burred, Emmanuel Ponsot and Jean-Julien Aucouturier (STMS, IRCAM/CNRS/Sorbonne
Université, Paris), with collaboration from Pascal Belin (Institut des
Neurosciences de la Timone, Aix-Marseille Université), with generous funding
from the European Research Council (CREAM 335536, 2014-2019, PI: JJ
Aucouturier), and support for face deformation was added by Lara Kermarec
(2022).A tutorial notebook is availablehere.The user manual in PDF format is availablehere.
Package: cleez
CleezAbilian CleezSimple framework for building command line applications with multiple
commands and subcommands.Similar to Cleo, but based on the stdlib's argparse module.Free / open source software: Apache Software License 2.0Documentation:https://cleez.readthedocs.io. (nope!)FeaturesProvides a simple framework for building command line applications with multiple commands and subcommands.Based on the stdlib's argparse module.Supports command options and arguments.Supports command arguments validation.ANSI color output.Upcoming featuresCommand aliases.Command arguments completion.Better help messagesUsed byCleez is currently used by several projects:Nua- an Open source PaaS and application marketplaceAbilian Developer Tools-(Add yours)AlternativesClickTyperClizyMandodephell-argparseCreditsThis package was created withCruftand theabilian/cookiecutter-abilian-pythonproject template.
Package: clef
A Python wrapper for the Clef API. Authenticate a user and access their information in two lines of code.SetupYou can install this package by using the pip tool:$ pip install clefSetting up a Clef developer accountSign up for a Clef developer account at:https://getclef.com/developer/Using the Clef APIDocumentation for using the API can be found here:
-http://docs.getclef.com/v1.0/docs/Sample apps and guides can be found on the left hand side of the API reference pages.
Package: clehrity
ClehrityFeaturesEHR deconfounding via:Statistical tests of discontinuity / non-monotoncitiesIntegration with LLMs to find surprising / counter-causal effectsRequirementsPython >=3.9InstallationYou can installClehrityviapipfromPyPI:$pipinstallclehrityUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Clehrityis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: clem
ClemSimple and lightweight text variation/formatting language written in pure
Python.InstallationpipinstallclemUsageExample .clem file# Sections are optional, and contained within parenthesis
greeting (
    # [indicator] | [content]
    # The indicator is used to find the line in your Python program. It's referenced as 'section.indicator'.

    # You can add decisions by putting choices inside angle-brackets. Clem will select one at random.
    casual | How's it <hanging / going>, <bro/dude/dog/man>?

    # You can also nest decisions. (If a decision only contains one choice, it will be treated as an optional.)
    casual | I <<sincerely> hope / think> you will <have fun during / enjoy / <forever> remember> your stay.

    # You can set a choice's odds by prefixing it with '[number]:'. If ommitted this will default to 1.
    casual | <Sup / What's up>, my <2: dog / cat>? <Welcome to {location}.>

    # Remember you can make as many different indicators as you want.
    formal | Greetings, my <dear> <man / friend>. I <sincerely> hope you enjoy your <time/stay> here in {location}.
)Using Clem in PythonfromclemimportClemclem=Clem()# Load in linesclem.load_file("example.clem")# Add a new one at runtimeclem.add("casual | hi there! my name is <Jack <Black / White>\/ Selena <Gomez>>.","greeting")# Print out lineprint("Casual: ",clem.render("greeting.casual",location="Boston"))print("Formal: ",clem.render("greeting.formal",location="Covington"))HistoryOriginClem was originally developed for [Fallout Simulator 2]
(https://github.com/jakeledoux/fsim2) and based on the primitive templating
system of its predecessor, [Fallout Simulator]
(https://jakeledoux.itch.io/falloutsim). You can read the original format
specificationhere. This version
of Clem, originally titled Clem2, was adapted from the game's code to better
structure the logic and to remove dependencies so that it could run independent
of the game.NameThe Clem language is named after a user by the name of 'qclem', who contributed
to the content and localisation of the templates for the original Fallout
Simulator.
Package: clement
ClementGUI for Correlative Light and Electron MicroscopyA graphical program to correlate electron microscopy and fluorescent optical images of the same sample.InstallationThe package is pure python, and the easiest way to install it is through PyPi with the following command:$ pip install clementWe recommend create a separate environment using eithercondaorvirtualenvto manage dependencies.Further instructions are available in the Wikihere.TutorialA guide with screenshots for semi-automatic alignment of FM and EM images based on observable features is also described on thewiki.
Package: clenv
clenv - Unofficial ClearML CLI helperPre-requisitesclearmlinstalled, please refer toClearML installation guidefor more details.Runclearml-initand initialize your first ever config file.InstallationpipinstallclenvUsageSubcommandconfigNote: All config files must be in the format ofclearml-<profile_name>.confList all config profilesclenvconfiglistCreate a new config profileclenvconfigcreate<profile_name>Delete a config profileclenvconfigdel<profile_name>Switch to a config profileclenvconfigcheckout<profile_name>Reinitialize theapisection of a configclenvconfigreinit<profile_name># Please paste your multi-line configuration and press Enter:Then paste your multi-line configuration generated through clearML server.SubcommanduserGenerate user/password hocon configclenvusergenpass<user_name>SubcommandtaskNote: This command only support git repos for now. The project name of the task created on the ClearML server will be the same as the git repo name. So please make sure you have a meaningful, easy to read git repo name.Execute a task remotely on ClearML serverclenvtaskexecIt will prompt you to select an available queue, correct task type, your entrypoint script path and input the task name.After inputting all the required configs, it will ask you whether to save the configs. By typing 'y', the config will be saved. When you executeclenv task execnext time in the same repo, it will load the saved configs and skip the config input process. However, it will still ask you for confirmation before submitting the task.Ignore the saved run configs when starting a new executionIf you want to ignore the old run configs and freshly start a new execution, you can run:clenvtaskexec-NThe-Noption will tellclenvto ignore the config file you have, and prompt you to input all the configs again.An alternative way of doing that is to delete the config file manually, which is located at./.clenv/task_template.json. Then runningclenv task execagain will start a fresh execution as well.ExamplesCreate a new clearml config profile for privately hosted clearml serverInitialize profiles$clenvconfiglist# Input a name for your current profileCreate a new profile$clenvconfigcreatebraincoReinit the profile credentials$clenvconfigreinitbraincoCheckout the new profile$clenvconfigcheckoutbraincoRoadmapConfig managementConfig profile managementSupport custom config file pathPrivately hosted server managementBCrypt password generation (Feature to be deprecated when more sophisticated user management is implemented)Server side utils and config managementClearML Agent side utils and config managementRemote task managementA user friendly remote task execution wizardDisclaimer & LicenseThis project is not affiliated with Allegro AI, Inc. in any way. It is an independent and unofficial software. It's licensed under the MIT license.
Package: cleopatra
Current release infoNameDownloadsVersionPlatformscleopatra - matplotlib utility packagecleopatrais a matplotlib utility packagecleopatraInstalling cleopatraInstallingcleopatrafrom theconda-forgechannel can be achieved by:conda install -c conda-forge cleopatraIt is possible to list all the versions ofcleopatraavailable on your platform with:conda search cleopatra --channel conda-forgeInstall from GitHubto install the last development to time, you can install the library from GitHubpip install git+https://github.com/Serapieum-of-alex/cleopatrapipto install the last release, you can easily use pippip install cleopatra==0.4.1Quick start>>> import cleopatraother code samples=======
History0.1.0 (2022-05-24)First release on PyPI.0.2.4 (2022-12-26)bump up numpy versions to 1.23.5, add pandas0.2.5 (2022-12-26)plot array with discrete bounds takes the bounds as a parameter0.2.6 (2023-01-31)bump up versionsadd serapeum_utils as a dependency0.2.7 (2023-01-31)bump up numpy to version 1.24.10.3.0 (2023-04-11)change API to work completly with numpy array inputschenge to conda configadd hpc-utils to filter and access arraysrestructure the whole modules to array, statistics, and styles modules.all modules has classes.save animation function using ffmpeg.0.3.1 (2023-04-17)plot RGB plots0.3.2 (2023-04-23)bump up hpc version0.3.3 (2023-04-25)change the default value for the color bar label.0.3.4 (2023-04-26)pass the plot kwargs to the init of the array to scale the color bar using the vmin and vmax.0.3.5 (2023-8-31)Update dependencies0.4.0 (2023-9-24)Add a colors module to handle issues related toConverting colors from one format to anotherCreating colormaps0.4.1 (2024-1-11)add extent to the array plot.