Saravanan666/pypi-org huggingface dataset:
Package: carefree-creator
An open sourced, AI-powered creator for everyone.This is the backend project of theCreatorproduct. If you are looking for theWebUIcodes, you may checkout thecarefree-drawboard🎨 project.Most of the contents have been moved to theWikipage.Wiki|WebUI CodesInstallationcarefree-creatoris built on top ofcarefree-learn, and requires:Python>=3.8pytorch>=1.12.0. Please refer toPyTorch's official website, and it is highly recommended to pre-install PyTorch with conda.Hardware RequirementsRelated issue:#10.This project will eat up 11~13 GB of GPU RAM if no modifications are made, because it actually integrates FIVE different SD versions together, and many other models as well. 🤣There are two ways that can reduce the usage of GPU RAM - lazy loading and partial loading, see the followingRunsection for more details.pip installationpipinstallcarefree-creatorIf you are interested in the latest features, you may usepipto install from source as well:gitclonehttps://github.com/carefree0910/carefree-creator.gitcdcarefree-creator
pipinstall-e.Runcarefree-creatorbuilds a CLI for you to setup your local service. For instance, we can:cfcreatorserveIf you don't have an NVIDIA GPU (e.g. mac), you may try:cfcreatorserve--cpuIf you are using your GPU-powered laptop, you may try:cfcreatorserve--limit1The--limitflag is used to limit the number of loading models. By specifying1, only the executing model will be loaded, and other models will stay on your disk.See#10for more details.If you have plenty of RAM resources but your GPU RAM is not large enough, you may try:cfcreatorserve--lazyWith the--lazyflag, the models will be loaded to RAM, and only the executing model will be moved to GPU RAM.So as an exchange, your RAM will be eaten up! 🤣If you only want to try the SD basic endpoints, you may use:cfcreatorserve--focussd.baseAnd if you only want to try the SD anime endpoints, you may use:cfcreatorserve--focussd.animeMore usages could be found by:cfcreatorserve--helpDockerPrepareexportTAG_NAME=cfcreator
gitclonehttps://github.com/carefree0910/carefree-creator.gitcdcarefree-creatorBuilddockerbuild-t$TAG_NAME.If your internet environment lands in China, it might be faster to build withDockerfile.cn:dockerbuild-t$TAG_NAME-fDockerfile.cn.Rundockerrun--gpusall--rm-p8123:8123$TAG_NAME:latestCreditsStable Diffusion, the foundation of various generation methods.Stable Diffusion from runwayml, the adopted SD-inpainting method.Waifu Diffusion, the anime-finetuned version of Stable Diffusion.Real ESRGAN, the adopted Super Resolution methods.Latent Diffusion, the adopted Inpainting & Landscape Synthesis method.carefree-learn, the code base that has re-implemented all the models above and provided clean and handy APIs.And You! Thank you for watching!
Package: carefree-cython
carefree-cythoncarefree-cythonimplements some commonly used cython functions.Installationcarefree-cythonrequires Python 3.8 or higher.pipinstallcarefree-cythonorgitclonehttps://github.com/carefree0910/carefree-cython.gitcdcarefree-cython
pipinstall-e.
Package: carefree-data
carefree-datacarefree-dataimplemented a data processing module with numpy.Update 2021.02.04carefree-datanow usesdatatableas backend, which significantly improves the performances on file inputs!Why carefree-data?carefree-datais a data processing module which is capable of handling 'dirty' and 'messy' datasets.For tabular datasets,carefree-datais able to:Elegantly deal with data pre-processing.ARecognizerto recognize whether a column isSTRING,NUMERICALorCATEGORICAL.AConverterto convert a column into friendly format (["one", "two"] -> [0, 1]).AProcessorto further process columns (OneHot,Normalize,MinMax, ...).And all the transforms could be inverse! (Seetests\unittests\test_tabular.py->test_recover_labels&test_recover_features).And these procedures are all completedAUTOMATICALLY!Handle datasets saved in files (.txt,.csv).For.txt," "will be the defaultdelimiter.For.csv,","will be the defaultdelimiter, and the first row will be skipped as default.delimiter,label index,skip firstcould be set manually.Pandas-freeThere is one more thing we'd like to mention:carefree-datais 'Pandas-free'. Pandas is an open source library providing easy-to-use data structures on structured datasets. Although it is a widely used library in almost every famous Machine Learning and Deep Learning module, we finally decided to escape from it, and the reasons are listed below:carefree-datawants to have full control on the data, and Pandas is not flexible enough.carefree-dataneeds higher performances. Pandas is fast, but not as fast as pure numpy (and sometimes cython) codes on some critical code paths.Pandas provides many powerful functions, butcarefree-datadoesn't need that much, which means Pandas is a little 'heavy' forcarefree-data.In short, Pandas is a more general library, and that's why we've written some codes to cover our needs instead of directly utilizing it.Currentlycarefree-dataonly supports tabular datasets.Installationcarefree-datarequires Python 3.8 or higher.pipinstallcarefree-dataorgitclonehttps://github.com/carefree0910/carefree-data.gitcdcarefree-data
pipinstall-e.Basic UsagesGet scikit-learn datasetsfromcfdata.tabularimportTabularDatasetiris=TabularDataset.iris()Read from array / datasetfromcfdata.tabularimport*iris=TabularDataset.iris()x,y=iris.xyassertTabularData().read(x,y)==TabularData.from_dataset(iris)Read from filefromcfdata.tabularimportTabularDatafile="/path/to/your/file"data=TabularData().read(file)assertdata.processed==data.transform(file)Licensecarefree-datais MIT licensed, as found in theLICENSEfile.
Package: carefree-learn
Deep Learning withPyTorchmade easy 🚀 !v0.5.x WIP!Here are the main design principles:The codes should be 'modulefirst', which means all previousmodels should be a simplemodulenow.Andmodelshould only be related to the training stuffs. If we only want to use the fancy AI models at inference stage,moduleshould be all we need.Themodules should be as 'native' as possible: no inheritance from base classes exceptnn.Moduleshould be the best, and previous inheritance-based features should be achieved bydependency injection.This helps themodules to be moretorch.compilefriendly.Training stuffs are not considered at the first place, but they will definitely be added later on, based on the modern AI developments.APIs will be as BC as possible.Licensecarefree-learnis MIT licensed, as found in theLICENSEfile.
Package: carefree-workflow
carefree-workflowcarefree-workflowis a lightweight library for building arbitray workflows with Python.HighlightsAsync:asyncis by design.Parallel: nodes can be executed in parallel.Powerful: complex locks / logics / dependencies can be handled.You can even perform a loop with loop backs in the workflow!Servable: all nodes, as well as the workflow itself, can be automatically turned into RESTful APIs.Extensible: you can easily extend the library with your own nodes.Serializable: the workflow can be serialized into / deserialized from a single JSON file.Human Readable: the workflow JSON file is human readable and easy to understand.Lightweight: the library is lightweight (core implementation is ~500 lines of code in a single filecore.py) and easy to use.Installationcarefree-workflowrequires Python 3.8 or higher.pipinstallcarefree-workfloworgitclonehttps://github.com/carefree0910/carefree-workflow.gitcdcarefree-workflow
pipinstall-e.UsagesCLIcarefree-workflowis installed with aCommandLineInterface (CLI) tool calledcflow, which can help you execute / render workflows easily:Workflow JSON examples can be found inexamples/workflows.cflowexecute-f<workflow_json_file>orcflowrender-f<workflow_json_file>Detailed information can be found by:cflow--helpCodeSincecarefree-workflowis extensible, you can easily extend the library with your own nodes:importasynciofromcflowimport*@Node.register("hello")classHelloNode(Node):asyncdefexecute(self):name=self.data["name"]return{"name":name,"greeting":f"Hello,{name}!"}asyncdefmain():flow=(Flow().push(HelloNode("A",dict(name="foo"))).push(HelloNode("B",dict(name="bar"))).push(EchoNode("Echo",dict(messages=[None,None,"Hello, World!"]),injections=[Injection("A","name","messages.0"),Injection("B","greeting","messages.1"),],)))awaitflow.execute("Echo")if__name__=="__main__":asyncio.run(main())Running the above codes will yield something like:[17:30:27] foo
           Hello, bar!
           Hello, World!More code snippets can be found inexamples.Servingcarefree-workflowis designed to be servable, here are some rules:Only nodes withget_schemaimplemented can be automatically served.Seecflow/nodes/cv.pyfor some examples.Complex results (e.g.PIL.Image) can be handled ifget_api_responseis implemented.See ourtemplateif you are interested!The registered name will be turned into the API endpoint, with the dots (.) being replaced by slashes (/).e.g. if the node is registered withNode.register("foo.bar"), the corresponding API endpoint will be/foo/bar.You can use our CLI to launch a server under default settings easily:cflowserveMore options can be found bycflow serve --help.ExamplesCode snippets can be found inexamples.Custom node templates can be found incflow/nodes/templates.And some pre-implemented node examples can be found incflow/nodes/cv.py.Workflow JSON files can be found inexamples/workflows.These JSON files can be executed bycflow execute -f <workflow_json_file>.More execution options can be found bycflow execute --help.Licensecarefree-workflowis MIT licensed, as found in theLICENSEfile.
Package: careful-requests
careful-requests~~~~~~~~~~~~~~~This module provides an HTTP adapter and session for use with `Requests`_ whencommunicating with servers that are hyper-sensitive to standard HTTP headers.It may be sad, but not all HTTP servers are HTTP-compliant and some are evensuspicious of normal headers. Use careful-requests if you still want to use theexcellent Requests module... _`Requests`: http://python-requests.org/Example usage----------here you go.. code-block:: pythonfrom careful_requests import Carefuls = Careful()>>> s.get("http://httpbin.org/get")<Response [200]>"Accept-Encoding" will not be sent.Install----------.. code-block:: bashsudo pip install careful-requests.. code-block:: bashsudo python setup.py installTesting----------.. code-block:: bashmake testChangelog----------* 0.1.3: support both requests==1.0.4 and requests==0.14.2, which is useful for proxy support.* 0.1.2: HTTPSLicense----------BSD
Package: carehome
# Carehome
A stupidly named package for creating MOO-style objects with Python.Objects support multiple inheritance, properties, events, and methods. The
resulting database can be dumped and loaded to and from dictionary objects.## Events
The following events are used throughout the code. Any other events which are
baked into the main code base will be added here.### on_init
Called when the object is initialised.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.### on_attach
Called when the object is attached to the database.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.### on_destroy
Called before the object is destroyed, and the object is still valid.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.### on_enter
Called before an object enters another one.#### Arguments
* The new location of this object.
* The thing which is moving into this object.### on_exit
Called before an object exits another.#### Arguments
* The old location of this object.
* The object which is leaving.### on_add_parent
Called before a parent is added to this object.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The parent which is being added.### on_add_child
Called before this object gains a child.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The child which is being added.### on_remove_parent
Called before a parent is removed from this object.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The parent which is being removed.### on_remove_child
Called before a child is removed from this object.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The child which is being removed.### on_add_property
Called before a property is added to this object.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The new Property instance.### on_remove_property
Called before a property is removed from this object.#### Arguments
* The object that is being initialised. This allows the event call to propagate
up the object hierarchy.
* The name of the old property.
Package: carehomecouk
Unofficial CareHome.co.uk Python API clientUnofficial CareHome.co.uk API Client for Python.Installationpip install carehomecoukRequirementsPython 3.7+Usageimportdatetimefromautumna.clientimportAPIClientfromautumna.constantsimportCategoryfromautumna.resources.enquiryimportEnquiryapi_client=APIClient('MY_API_KEY')enquiries=Enquiry.many(api_client,date_time_from=datetime.datetime(2022,4,15),category_id=Category.CARE_ENQUIRY)forenquiryinenquiries:print(enquiry)
Package: carehttp
📦 CareHttpCareHttp cares your data.It wraps from Requests and Retrying, retry several times when you encounter requests error.Installationpip3installcarehttpUsageexample1 print the url:fromcarehttpimportCarehttpr=Carehttp().get(url='https://stackoverflow.com/',timeout=0.1)print(r.text)r=Carehttp().post('https://stackoverflow.com/',data={},json={},timeout=1)print(r.text)example2 print a title:s=requests.Session()r=Carehttp(session=s,mark='title').get('https://stackoverflow.com/',timeout=1)print(r.text)example3 custom retry setting:r=Carehttp(mark='title',tries=10,delay=1,max_delay=60).get('https://stackoverflow.com/',timeout=1)print(r.text)How to use carehttpIt's a combination fromRequestsandRetrying, only add a parammarkRequest part is exactly the same params withRequests.Retry part termed params. Usage more likeRetry.If ignoremark, error displays the url, or else display the text ofmark.What elseMore functions is planning.LicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: carepoint
[![Build Status](https://api.travis-ci.org/laslabs/Python-Carepoint.svg?branch=release%2F0.1)](https://travis-ci.org/laslabs/Python-Carepoint)[![Coveralls Status](https://coveralls.io/repos/laslabs/Python-Carepoint/badge.svg?branch=release%2F0.1)](https://coveralls.io/r/laslabs/Python-Carepoint?branch=release%2F0.1)[![Codecov Status](https://codecov.io/gh/laslabs/Python-Carepoint/branch/release%2F0.1/graph/badge.svg)](https://codecov.io/gh/laslabs/Python-Carepoint)Python CarePoint Library========================This library will allow you to interact with CarePoint using Python.For the most part, it just provides some convenience wrappers to encapsulateall of the tables within one object/session for easy use.Most of the methods return SQLAlchemy ResultProxies.Installation------------To install this module, you need to:* Utilize a system able to access all CarePoint network resources (database, SMB)* This includes things like DNS entries for the NETBIOS names* Setup UnixODBC - http://help.interfaceware.com/kb/904* Install UnixODBC development headers - `apt-get install unixodbc-dev`* Install dependencies - `pip install -r requirements.txt`* Install library - `pip install .`Setup-----* Create an Active Directory user* Give AD user permissions to CarePoint images and data net shares* Give AD user read & write permissions on the following databases:* cph* grx_masterUsage-----### Connect to Database serverfrom carepoint import Carepointcp = Carepoint(server='127.0.0.1',user='test_db_user',passwd='db_pass',)### Perform a search for a patient with the last name Smithres = cp.search(cp['Patient'],{'lname': 'Smith'},)for row in res:print row.fname### Perform a patient search, but only return the `mname` columnres = cp.search(cp['Patient'],{'lname': 'Smith'},['mname'],)for row in res:print row.mname### Get patients modified in 2015res = cp.search(cp['Patient'],{'chg_date': {'<=': '2015-12-31','>=': '2015-01-01',}},)for row in res:print row.fname### Get image using SMB path from databasecp = Carepoint(**opts)img_rec = cp.search(cp['FdbImg']).first()image_file = cp.get_file(img_rec.IMAGE_PATH)image_data = image_file.read()Known Issues / Road Map-----------------------* More usage samples* A decent amount of models are missing* Create documentation of models* Add some basic validations* Create a column type that will retrieve file from SMB path in DB* Better SMB support (allow ftimes, dir list, caching)* FDB images only serve from store ID 1's net share* Having to pass the model to the Carepoint object is annoying, objectify the syntaxBug Tracker-----------Bugs are tracked on [Github Issues](https://github.com/laslabs/python-carepoint/issues>).In case of trouble, please check there if your issue has already been reported.If you spotted it first, please help us to smash it by providing detailed and welcomed feedback.Contributors------------* Dave Lasley <dave@laslabs.com>* Vinnie Corcoran <vcorcoran@laslabs.com>Maintainer----------[![LasLabs Inc.](https://laslabs.com/logo.png "LasLabs Inc.")](https://laslabs.com)This module is maintained by [LasLabs Inc.](https://laslabs.com)* https://github.com/laslabs/Python-Carepoint
Package: caret
caretconsists of a set of interfaces and libraries to allow programming of
Asterisk from Python. The library currently supports AGI, AMI, and the parsing
of Asterisk configuration files. The library also includes debugging facilities
for AGI.This project provides a single library for all Asterisk based needs
of Python users and an easy transition to Python for PHP users from the PHPAGI package.InstallationUsing PythonTo installcaret, simply run:$pipinstallcaretOn Debian/Ubuntu(Under development)This will install the latest version of the library automatically.DocumentationDocumentation is still under development and parts of it are hosted onhttps://ar13pit.github.io/caret/Use Python docstrings for time being using
Python’s built-in help facility:import caret
help (caret)
import caret.agi
help (caret.agi)
import caret.manager
help (caret.manager)
import caret.config
help (caret.config)Some notes on platforms: We now specify “platforms = ‘Any’” insetup.py. This means, the manager part of the package will probably
run on any platform. The agi scripts on the other hand are called
directly on the host where Asterisk is running. Since Asterisk doesn’t
run on windows platforms (and probably never will) the agi part of the
package can only be run on Asterisk platforms.CreditsThanks to Karl Putland for writing the originalpystpackage and
Matthew Nicholson for maintaining it for some years.Thanks to Randall Degges for maintaining thepyst2fork and accepting
pull requests for some years.Thanks to Arpit Aggarwal for carrying the development forward and starting thecaretproject.CopyrightThis project has been released under GNU Lesser General Public License v3.0.
Package: cargo2rpm
cargo2rpmcargo2rpm implements a translation layer between cargo and RPM. It provides a
CLI interface (for implementingRPM macros and generators) and
a Python API (whichrust2rpmis built upon).The project serves as a dependency-free, pure-Python replacement for the old
backend code in rust2rpm, which was no longer maintainable and could not be
adapted to support the latest cargo features (i.e. "namespaced dependencies" and
"weak dependency features", which were introduced in Rust 1.60.0).To decouple the release cycles of the different components involved in packaging
Rust projects (and prevent possible bootstrap problems), the code originally
developed under the "rust2rpm" umbrella was split into three projects:cargo2rpm: low-level functionality for translating between cargo and RPMrust-packaging: RPM macros and generators built on top of the cargo2rpm CLIrust2rpm: RPM spec file generator built on top of cargo2rpm
Package: cargo-deny
❌ cargo-denyCargo plugin for linting your dependenciesSee thebook 📕for in-depth documentation.To run on CI as a GitHub Action, seecargo-deny-action.Please Note: This is a tool that we use (and like!) and it makes sense to us to release it as open source. However, we can’t take any responsibility for your use of the tool, if it will function correctly or fulfil your needs. No functionality in - or information provided by - cargo-deny constitutes legal advice.Quickstartcargoinstall--lockedcargo-deny&&cargodenyinit&&cargodenycheckUsageInstallcargo-denyIf you want to usecargo-denywithout havingcargoinstalled, buildcargo-denywith thestandalonefeature. This can be useful in Docker Images.cargoinstall--lockedcargo-deny# Or, if you're an Arch userpacman-Scargo-denyInitializeyour projectcargodenyinitCheckyour cratescargodenycheckLicensesThe licenses check is used to verify that every crate you use has license terms you find acceptable.cargodenychecklicensesBansThe bans check is used to deny (or allow) specific crates, as well as detect and handle multiple versions of the same crate.cargodenycheckbansAdvisoriesThe advisories check is used to detect issues for crates by looking in an advisory database.cargodenycheckadvisoriesSourcesThe sources check ensures crates only come from sources you trust.cargodenychecksourcesContributingWe welcome community contributions to this project.Please read ourContributor Guidefor more information on how to get started.LicenseLicensed under either ofApache License, Version 2.0, (LICENSE-APACHEorhttp://www.apache.org/licenses/LICENSE-2.0)MIT license (LICENSE-MITorhttp://opensource.org/licenses/MIT)at your option.ContributionUnless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.
Package: cargo-insta
cargo-insta: review tool for insta, a snapshot testing library for Rustcargo-instaprovides a cargo command forinstasnapshot reviews.$ cargo install cargo-insta
$ cargo insta --helpTo install an old version ensure to pass the--lockedflag so that theCargo.lockfile is honored:$ cargo install cargo-insta --version 1.15.0 --lockedUsagecargo-instaprovides a few different commands to interact with insta snapshots.For running tests you can use thetestcommand, for reviewing snapshotsreview.
The reviewing process is interactive and prompts for all changes identified.
If you want to skip reviewing you can useacceptandrejectdirectly.For more information refer to thedocumentation.License and LinksDocumentationIssue TrackerLicense:Apache-2.0
Package: cargo-parse
cargo-parseA Python package to parseCargo.tomlmanifest files.InstallationThis package is not (yet) published on PyPI. For now, the best way to install the package is to usePoetry.Clone this repository and runpoetry install.Install in another environmentTo install the package in a system environment, or another virtual
environment besides the Poetry project environment:Build the package wheel withpoetry build.Install the package using the correct environment'spip:<your-python> -m pip install <path-to-repo>/dist/cargo-parse-*.whlUsageImport theparse_manifest_from_tomlfunction and use it to parse the contents ofCargo.toml:fromcargo_parseimportparse_manifest_from_tomlfrompathlibimportPathcargo_toml_file="Cargo.toml"manifest=parse_manifest_from_toml(Path(cargo_toml_file))# Print out the package versionprint(manifest.package.version)# Print out the dependenciesifmanifest.dependenciesisnotNone:print(manifest.dependencies)else:print(f"No dependencies defined in{cargo_toml_file}")