Saravanan666/pypi-org huggingface dataset:
Package: megdiffusion
MegDiffusionMegEngine implementation of Diffusion Models (in early development).Current maintainer:@MegChaiUsageInfer with pre-trained modelsNow users can usemegengine.hubto get pre-trained models directly:importmegenginerepo_info="MegEngine/MegDiffusion:main"megengine.hub.list(repo_info)preatrained_model="ddpm_cifar10_ema_converted"megengine.hub.help(repo_info,preatrained_model)model=megengine.hub.load(repo_info,preatrained_model,pretrained=True)model.eval()Note that usingmegengine.hubwill download the whole repository from it's host or using cache.If you have downloaded or installed MegDiffusion, you can get pre-trained models frompretrainmodule.frommegdiffusion.modelimportpretrainmodel=pretrain.ddpm_cifar10_ema_converted(pretrained=True)model.eval()The samplescriptshows how to generate 64 CIFAR10-like images and make a grid of them:python3-mmegdiffusion.pipeline.ddpm.sampleTrain from scratchTake DDPM CIFAR10 for example:python3-mmegdiffusion.pipeline.ddpm.train\--config./configs/ddpm/cifar10.yaml[Optional] Overwrite arguments:python3-mmegdiffusion.pipeline.ddpm.train\--config./configs/ddpm/cifar10.yaml\--logdir./path/to/logdir\--parallel--resumeSeepython3 -m megdiffusion.pipeline.ddpm.train --helpfor more information.
For other options likebatch_size, we recommend modifying and backing up them in the yaml file.If you want to sample with model trained by yourself (not the pre-trained model):python3-mmegdiffusion.pipeline.ddpm.sample--nopretrain\--logdir./path/to/logdir\--config./configs/ddpm/cifar10.yaml# Coule be your customed fileDevelopmentpython3-mpipinstall-rrequirements.txt
python3-mpipinstall-v-e.Develop this project with a new branch locally, remember to add necessary test codes.
If finished, submit Pull Request to themainbranch then just wait for review.AcknowledgmentThe following open-sourced projects was referenced here:hojonathanho/diffusion: The official Tensorflow implementation of DDPM.w86763777/pytorch-ddpm: Unofficial PyTorch implementation of Denoising Diffusion Probabilistic Models.pesser/pytorch_diffusion: Unofficial PyTorch implementation of DDPM and provides converted torch checkpoints.openai/improved-diffusion: The official codebase for Improved Denoising Diffusion Probabilistic Models.Thanks to people including@gaohuazuo,@xxr3376,@P2Oileenand other contributors for support in this project. The R&D platform and the resources required for the experiment are provided byMEGVIIInc. The deep learning framework used in this project isMegEngine-- a magic weapon.Citations@article{ho2020denoising,
    title   = {Denoising Diffusion Probabilistic Models},
    author  = {Jonathan Ho and Ajay Jain and Pieter Abbeel},
    year    = {2020},
    eprint  = {2006.11239},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}@article{DBLP,
  title     = {Improved Denoising Diffusion Probabilistic Models},
  author    = {Alex Nichol and Prafulla Dhariwal},
  year      = {2021},
  url       = {https://arxiv.org/abs/2102.09672},
  eprinttype = {arXiv},
  eprint    = {2102.09672},
}
Package: meg-django-tastypie
Creating delicious APIs for Django apps since 2010.Currently in beta (v0.13.3) but being used actively in production on several
sites.RequirementsCorePython 2.7+ or Python 3.4+Django 1.7 through Django 1.9dateutil (http://labix.org/python-dateutil) >= 2.1Format SupportXML: lxml 3 (http://lxml.de/) and defusedxml (https://pypi.python.org/pypi/defusedxml)YAML: pyyaml (http://pyyaml.org/)binary plist: biplist (http://explorapp.com/biplist/)OptionalHTTP Digest authentication: python3-digest (https://bitbucket.org/akoha/python-digest/)What’s It Look Like?A basic example looks like:# myapp/api.py# ============fromtastypie.resourcesimportModelResourcefrommyapp.modelsimportEntryclassEntryResource(ModelResource):classMeta:queryset=Entry.objects.all()# urls.py# =======fromdjango.conf.urlsimporturl,includefromtastypie.apiimportApifrommyapp.apiimportEntryResourcev1_api=Api(api_name='v1')v1_api.register(EntryResource())urlpatterns=[# The normal jazz here then...url(r'^api/',include(v1_api.urls)),]That gets you a fully working, read-write API for theEntrymodel that
supports all CRUD operations in a RESTful way. JSON/XML/YAML support is already
there, and it’s easy to add related data/authentication/caching.You can find more in the documentation athttps://django-tastypie.readthedocs.io/.Why Tastypie?There are other API frameworks out there for Django. You need to
assess the options available and decide for yourself. That said, here are some
common reasons for tastypie.You need an API that is RESTful and uses HTTP well.You want to support deep relations.You DON’T want to have to write your own serializer to make the output right.You want an API framework that has little magic, very flexible and maps well to
the problem domain.You want/need XML serialization that is treated equally to JSON (and YAML is
there too).Reference Materialhttps://django-tastypie.readthedocs.io/en/latest/https://github.com/django-tastypie/django-tastypie/tree/master/tests/basicshows
basic usage of tastypiehttp://en.wikipedia.org/wiki/RESThttp://en.wikipedia.org/wiki/List_of_HTTP_status_codeshttp://www.ietf.org/rfc/rfc2616.txthttp://jacobian.org/writing/rest-worst-practices/Getting HelpThere are two primary ways of getting help.Go toStackOverflowand post a question with thetastypietag.We have an IRC channel (#tastypie on irc.freenode.net) to get help,
bounce an idea by us, or generally shoot the breeze.SecurityTastypie is committed to providing a flexible and secure API, and was designed
with many security features and options in mind. Due to the complex nature of
APIs and the constant discovery of new attack vectors and vulnerabilities,
no software is immune to security holes. We rely on our community to report
and help us investigate security issues.If you come across a security holeplease do not open a Github issue.
Instead,drop us an emailattastypie-security@googlegroups.comWe’ll then work together to investigate and resolve the problem so we can
announce a solution along with the vulnerability.
Package: meg-django-twitter-bootstrap
Starter templates for django sites based on twitter bootstraphttp://twitter.github.io/bootstrap/Development files are atgit@git.maithu.com:TwitterBootstrap.gitTo release a new versionadd the following to ~/.pypirc:[distutils]
index-servers =
    maithu

[maithu]
username: admin
password: password
repository: http://pypi.maithu.com/simple/python setup.py sdist upload -r maithu
Package: mege
mege is a combined content manager and content generator made specifically for
the website “metanohi” (available athttp://metanohi.name/). Because of
lazyness, currently mege is tightly integrated with this website, though it
should be possible to adapt it to work with other sites.mege is in constant on-and-off development — special features are only added
as they are needed — and there will not be any release out for a long time,
if ever.mege is both a command line tool and a Python module. It requires Python 3.1+.mege uses bottle as its web framework, serves HTML generated from Org-Mode
files, and uses Git for revisions. It uses modwsgi and the Apache web server.Licensemege is free software under the terms of the GNU Affero General Public License
version 3 (or any later version, see fileCOPYING.txtor webpagehttp://gnu.org/licenses/agpl.htmlfor full license). The author of mege is
Niels G. W. Serup, contactable atns@metanohi.name.InstallationThere is no actual release. To get mege, clone mege’s git repository. The
addres is available athttp://metanohi.name/projects/mege/To install mege, run:python3 setup.py installYou must be a superuser to do this.Dependenciesbottle:http://bottlepy.org/Pygments:http://pygments.org/WSGI Apache interface for Python 3:http://code.google.com/p/modwsgi/UseAs a command-line toolWhen you have installed mege, run:mege COMMAND [OPTION]... [INPUT FILE]...To see how to interact with mege in general, run:mege --helpAs a Python moduleWhen mege has been installed, it can be used just like any other Python
module. To see which functions and classes are available and usable, run:pydoc3 megeAnd also:pydoc3 mege.server
pydoc3 mege.htmlgen
pydoc3 mege.macrolang
pydoc3 mege.htmlfunc
pydoc3 mege.gitAnd so on.This documentCopyright (C) 2011, 2012  Niels G. W. SerupCopying and distribution of this file, with or without modification,
are permitted in any medium without royalty provided the copyright
notice and this notice are preserved.  This file is offered as-is,
without any warranty.
Package: megham
meghamA library for working with point clouds and related concepts.IntroductionOver the last few years I have written a fair bit of code for both
theSimons ObservatoryandCLASSthat involves point clouds.
When writing that code I have found myself wanting for a cohesive library that handles all my point cloud
related tasks.
There are things that exist in bits and pieces
(numpy,scipy,sklearn's manifold module,pycpd, etc.)
but none of them had all of the features I wanted or were implemented in ways that weren't ideal for my usecase.Megham exists to help me bridge that gap.
Currently I am targeting only a small set of features that are relevant to my work:Fitting transforms between point cloudsPoint set registration (without known correspondence)Outlier detectionMulti dimensional scalingBut other features may exist down the line (and PRs are welcome).Getting StartedTo install this repository run:pip install meghamIf you will be actively developing the code may want to instead clone this repository and run:pip install -e .Documentation can be foundhereContributingAll are welcome to contribute to this repository as long as long as the code is relevant.
In general contributions other than minor changes should follow the branch/fork -> PR -> merge workflow.
If you are going to contribute regularly, contact me to get push access to the repository.Style and StandardsIn general contributions should bePEP8with commits in theconventional commitsformat.
This library followssemantic versioning, so changes that bump the version should do so by editingpyproject.toml.In order to make following these rules easier this repository is setup to work withcommitizenandpre-commit.
It is recommended that you make use of these tools to save time.Docstrings should follow thenumpy style. API reference docs are automatically built, but any additional narrative documentation or tutorials should go in thedocsfolder. This project usesmkdocsto generate documentation.Tool SetupInstall both tools withpip install commitizen pre-commit.cdinto themeghamrepository it you aren't already in it.(Optional) Setupcommitizento automatically run when you rungit commit. Follow instructionhere.Make sure thepre-commithook is installed by runningpre-commit install.Example WorkflowMake a branch for the edits you want to make.Code.Commit your code with aconventional commit message.cz cgives you a wizard that will do this for you, if you followed Step 3 above thengit commitwill also do this (but notgit commit -m).Repeat steps 2 and 3 until the goal if your branch has been completed.Put in a PR.Once the PR is merged the repo version and tag will updateautomatically.
Package: me-gpt
ME_GPTME_GPT revolutionizes the way you interact with Chat GPT by introducing a powerful memory storage system and self-cloning functionality. With ME_GPT, you can store your cherished memories in a local vector database and leverage them to give Chat GPT the extraordinary ability to clone your unique personality and knowledge.Quickstart:1. Pip install the librarypip install me-gpt2. Create a new .env filecp .env.example .env3. Add your OpenAI API key to the .env fileOPEN_AI_API_KEY = "API_KEY"OpenAI API keyVisithttps://platform.openai.com/account/api-keysto get your OpenAI API key4. Test the libraryfromme_gpt.aiimport*# initial questions to help your clone get started:initial_questions()# have your clone ask you more questions:more_questions(number_of_questions=3)# ask your clone a question and any instructions you'd like to give:question="What is your Name?"instructions="Give your answer with a pirate accent."print(prompt_memory(question,instructions))# chat with your clone in the terminal:chat_with_clone()Key Features:Memory Storage: Store memories, experiences, and information in a local vector database with ease. Capture the essence of your persona and knowledge, creating a treasure trove of data for Chat GPT to tap into.Self-Cloning Capability: Empower Chat GPT to clone your personality and knowledge using the memories stored in the vector database. Witness the remarkable transformation as Chat GPT adapts to your unique style, preferences, and expertise.Efficient Retrieval: Experience blazing-fast memory retrieval thanks to the vectorized storage system. Retrieve memories seamlessly, ensuring smooth and uninterrupted conversations with your cloned agent.User-Friendly Integration: The repository offers a simple and intuitive integration process, allowing developers and enthusiasts to effortlessly incorporate ME_GPT into their existing Chat GPT projects.Whether you're looking to personalize your conversational AI interactions, create virtual replicas, or explore the potential of memory-enhanced language models, ME_GPT is your go-to solution. Unlock the power of memories and witness the fascinating capabilities of Chat GPT clones with this cutting-edge repository. Embark on a journey of unparalleled dialogue experiences and reshape the future of conversational AI.ContributingIf you want to contribute, please check out theroadmap, in the GitHub repo. You are welcome to read thecontributing documentfor more information.We are currently looking for more maintainers and community organizers to help us build the community around this project. If you are interested, email -daniel@danielmcdonald.devif you are interested in an official role.
Package: megpy
MEGPy: Magnetic Equilibrium Geometry PythonMEGPy is a package for quickextraction of (local) equilibrium quantitiesand visualisation of 2D magnetic equilibria used in magnetic confinement fusion research.
It offers both a command line interface (CLI) and Python API.Getting startedTo get MEGPy clone and install the latest version:$gitclonegit@github.com:gsnoep/megpy.git
$cdmegpy
$pipinstall--user-e.To use MEGPy call the CLI:$python-mmegpy<file_path><parameterization><x_fs>For details on the CLI options:$python-mmegpy-hOr use the Python API, of which some examples can be found in /examples/.Join the repository to contribute or raise issues!Supported equilibrium formatsEQDSK(g-file)from EFIT, ESCO, CHEASE or LIUQETo be added:IMAS equilibrium IDSSupported flux-surface geometriesMiller(doi)Turnbull-Miller(doi)Fourier / generalised Miller(doi)Miller eXtended Harmonic (MXH)(doi)CitationIf you use MEGPy in your research, please cite(bibtex):G. Snoep, J.T.W. Koenders, C. Bourdelle, J. Citrin and JET contributors, "Improved flux-surface parameterization through constrained nonlinear optimization,"Physics of Plasmas30, 063906 (2023).#&%%%-.
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                )%%%%%%%%&..    .\%.
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                  .::-P-..-&  &&-Y&--.
Package: megrok.form
megrok.form is a helper package that provides some useful fields, widgets and constraints for Grok.FieldsEmail, a TextLine field with a default contraintHTML, a Text field that uses z3c.widget.tinyFile, from collective.namedfileImage, from collective.namedfileBlobFile, from collective.namedblobfileBlobImage, from collective.namedblobfileWidgetsmegrok.form overrides the default widgets for:zope.interface.schema.Date (using zc.datetimewidget.widget.DateWidget)zope.interface.schema.Datetime (using zc.datetimewidget.widget.DatetimeWidget)And add new widgets for Image and File (from collective.namedfile).Validators / Constraintsmegrok.form add constraints for:SSNUS Phone NumbersInternational Phone NumbersZip CodeURLEmailInstallationTo use megrok.form under Grok all you need is to install megrok.form as an egg
and include it’s zcml. The best place to do this is to make megrok.form
a dependency of your application by adding it to your install_requires
list in setup.cfg. If you used grokprojet to create your application setup.cfg
is located in the project root. It should look something like this:install_requires=['setuptools',
                  'grok',
                  'megrok.form',
                  # Add extra requirements here
                  ],Then include megrok.form in your configure.zcml. If you used grokproject to
create your application it’s at src/<projectname>/configure.zcml. Add the
include line after the include line for grok, but before the grokking of the
current package. It should look something like this:<include package="grok" />
<include package="megrok.form" />
<grok:grok package="." />Then run bin/buildout again. You should now see buildout saying something like:Getting distribution for 'megrok.form'.
Got megrok.form 0.2.2.That’s all.Using Blob StorageTo use blob storage you need to change zodb definition in your parts/zopectl/zope.conf like this:<zodb>
  <blobstorage>
    <filestorage>
      path /path_to_your_app/parts/data/Data.fs
    </filestorage>
    blob-dir /path_to_your_app/parts/data/blobs
  </blobstorage>
</zodb>The blob-dir specifies where you want to store blobs.AuthorsDirceu Pereira Tiegs (dirceutiegs at gmail dot com)
Package: megrok.genshi
Update 2013-05-27: I’m no longer using this project, if somebody wants to take over maintanance, email me.megrok.genshimegrok.genshi makes it possible to use Genshi templates in Grok. The project is no longer maintained due to lack of usage, but can still work as an example of how to do template integration in Grok.For more information on Grok and Genshi see:http://grok.zope.org/http://genshi.edgewall.org/RequirementsGenshi.  Tested with v 0.5.1.Grok v0.14 or later.  Tested with 0.14.InstallationTo use Genshi under Grok all you need is to install megrok.genshi as an egg
and include it’s zcml. The best place to do this is to make megrok.genshi
a dependency of your application by adding it to your install_requires
list in setup.cfg. If you used grokprojet to create your application setup.cfg
is located in the project root. It should look something like this:install_requires=['setuptools',
                  'grok',
                  'megrok.genshi',
                  # Add extra requirements here
                  ],Then include megrok.genshi in your configure.zcml. If you used grokproject to
create your application it’s at src/<projectname>/configure.zcml. Add the
include line after the include line for grok, but before the grokking of the
current package. It should look something like this:<include package="grok" />
<include package="megrok.genshi" />
<grok:grok package="." />Then run bin/buildout again. You should now see buildout saying something like:Getting distribution for 'megrok.genshi'.
Got megrok.genshi 1.0.That’s all. You can now start using Genshi in your Grok application!Usagemegrok.genshi supports the Grok standard of placing templates in a templates
directory, for example app_templates, so you can use Genshi by simply placing
the Genshi templates in the templates directory, just as you would with ZPT
templates.  Although Genshi itself doesn’t have a standard for the file
extensions for Genshi templates, Grok needs to have an association between an
extension and a type so it knows which type of template each template is.
megrok.genshi defines the extension .g for Genshi HTML templates and .gt for
Genshi Text templates.  Genshi can also include templates, and although you can
use any extension for this we recommend you use .gi for any include templates,
to avoid any clashes with other templating languages.You can also use Genshi templates inline.  The syntax for this is:from megrok.genshi.components import GenshiMarkupTemplate, GenshiTextTemplate
index = GenshiMarkupTemplate('<html>the html code</html>')
index = GenshiMarkupTemplate('Text templates')Or if you use files:from megrok.genshi.components import GenshiMarkupTemplate, GenshiTextTemplate
index = GenshiMarkupTemplate(filename='thefilename.html')
index = GenshiMarkupTemplate(filename='thefilename.txt')AuthorsLennart Regebro (regebro@gmail.com)Guido WesdorpCHANGESVersion 1.0Support for Grok 0.14. (Paul Wilson)Version 0.9Initial release
Package: megrok.kss
megrok.kssGrokkish support for KSS.There is atutorial.GrokGrokis a web application framework for Python developers. It is aimed at both
beginners and very experienced web developers. Grok has an emphasis on agile development.
Grok is easy and powerful.Kinetic Style Sheets (KSS)KSS is an Ajax framework that allows UI development without writing any
Javascript. It uses style sheets with CSS-compliant syntax to declare and bind
dynamic behaviors in the browser. The engine supports a set of generic DOM-like
commands; they are computed on the server and sent back to manipulate the HTML
page.What is KSS?KSS is an AJAX framework. KSS has both a client-side Javascript library and
server-side support.The client-side Javascript library needs to be included in your page. It
fetches Kinetic style sheets from the server, parses them and binds a set of
actions to browser events and/or page elements. It is clean Javascript code that
can peacefully coexist with other clean Javascript libraries like JQuery or
ExtJS. It is about 100k in production mode. You can integrate your own
Javascript code by using its extension mechanism through plugins.Server-side code is currently available for Zope (2 and 3, which includes
Plone and Grok).The Javascript client-side code can be used independently of existing
server-side support. In other words, it is usable on platforms where we have
not built server-side support like PHP, Ruby or Java.More about KSSMore information and documentation can be found on theKSS project homepage.Changelog for megrok.kss(name of developer listed in brackets)megrok.kss - 0.2.3 (2010-02-14)Added license file.
[ulif]megrok.kss - 0.2.2 Released 2009-02-14Fixes for compatibility with grok 1.0a1. We now use
IGrokSecurityView.
[ulif]megrok.kss - 0.2.1 Released 2008-12-29Fixes for compatibility with security-patched grok versions
(0.14.1, 0.13.1, 0.12.2)
[ulif]megrok.kss - 0.2  Released 2008-08-18Fixes for 0.13 compatibility
[gotcha]To simplify the usage of KSS in Grok we renamed theKSSActionsclass
toKSS, which is more in line with the JSON and WSGI extensions. In
Grok, you apply KSS on a view rather than a content object.
Use: grok.view(MyViewClass) to register it.
[jladage]megrok.kss - 0.1 Released 2008-04-30basic kss.core support
[gotcha]AUTHORSJean-Paul Ladage <j.ladage@zestsoftware.nl>Godefroid Chapelle <gotcha@bubblenet.be>