Saravanan666/pypi-org huggingface dataset:
Package: seo-ville-slugger
Seo ville slugger will solve the problems you are having with django, media
files and filenames.Django’s default storage backend has some pretty annoying behaviour.
When uploading the same file for the second time, you get a second file on disk
with some kind of crazy hash value added at the end.There are 2 kinds of people that are not happy with that.The person that has to pay for disk spaceThe SEO people your customer has hired.This storage backend stores files named after their hash value and adds a symlink
with the original name. No duplication and you can SEO your heart out.Media file and pagespeed mobile scoreIdeally, you would want to have long cache expiry headers for your site’s uploaded files,
Just like you’ve got with your static files.
That usually poses a problem in conjunction with the pretty urls.
It is safe to put long expiry headers when using SEOStorage, because the files
are distributed over folders named after a tiny piece of the hash.
Obviously this small hash could lead to collisions, so the storage backend will prevent
it as follows:In the rare case we encounter two different files with exactly the same filename, and they end up in the same folder
due to hash collision, the new file will not get the pretty SEO url. Instead the full hashed filename
will be used instead. In these rare cases you might get a SEO unfriendly url.We can’t worry about that can we? Phat phat chance anyone will notice!UsageYou can use the storage backend on a global level by adding the following to
your django settings:DEFAULT_FILE_STORAGE = 'seo.ville.slugger.SEOStorage'Sorl thumbnailSuppose you want sorl thumbnail to also have nice urls based on the original
filename?:THUMBNAIL_BACKEND = 'seo.ville.sorl.SEOThumbnailBackend'
THUMBNAIL_STORAGE  = 'seo.ville.sorl.SEOThumbnailStorage'With sorl you can pass the slug as a parameter to the thumbnail tag:{% thumbnail image "330x450" upscale=True slug=slug as thumb %}SettingsHASH_BLOCKSIZEchange this if you think hashing of the file use too much or not enough memoryHASH_DIRNAME_SLICE_SIZEchange this if you find there are too many collisions.
Package: sep005-io-dxd
SEP005 .dxd ioBasic package to import data collected from Dewesoft .DXD files compliant with
SDyPy format for timeseries as proposed in SEP005.Using the packagefromsep005_io_dxdimportread_dxdfile_path=# Path to the tdms file of interestsignals=read_dxd(file_path)AcknowledgementsThis package was developed in the framework of theInterreg Smart Circular Bridge project
Package: sep005-io-fbgs
SEP005 FBGS ioBasic package to import data collected from FBGS data compliant with
SDyPy format for timeseries as proposed in SEP005.Using the packagefromsep005_io_fbgsimportread_fbgsfile_path=# Path to the tdms file of interestsignals=read_fbgs(file_path)AcknowledgementsThis package was developed in the framework of theInterreg Smart Circular Bridge project
Package: sep005-io-parquet
SEP005 parquet ioBasic package to import data collected from FBGS data compliant with
SDyPy format for timeseries as proposed in SEP005.Using the packagefromsep005_io_parquetimportread_parquetfile_path=# Path to the parquet file of interestsignals=read_parquet(file_path)Acknowledgements
Package: sep10
SEP10 WEB AUTHENTICATION TOKENFor A Full Understanding of Sep10, Please visit the official repoREAD MORE DETAILS HERE.After clone or fork this repo, thenpipinstallsep10After installation, it's ready for use, Below is a basic Example of how to use the repofromAuthenticator.StellarAuthyimportWebAuthywebauth=WebAuthy(asset_code,asset_issuer,user_key)# This Also Accept _horizon_url and _network, _horizon_url is the horizon url, the default is the mainnet, _network can also be changed to tesnet default is mainnettoken=webauth.run_auth()# JWT will be returnedChange Log0.0.1 (27/06/2021)First Release0.0.2 (27/06/2021)Bug fixesUpdated Readme0.0.3 (28/06/2021)Added validators to list of required packages
Package: sepal
South Edinburgh Perfusion+ Analysis Library (SEPAL)Please note: This library is also hosted in theOSIPI DCE-DSC-MRI_CodeCollection repository, where unit tests and perfusion code by other authors can also be found.Python library for simulating and fitting DCE- and other quantitative MRI data. It permits arbitrary combinations of pulse sequence, pharmacokinetic model, water exchange model, etc. The code is a work-in-progress, has not been extensively tested and is not recommended or approved for clinical use.Created 28 September 2020@authors: Michael Thrippleton@email:m.j.thrippleton@ed.ac.uk@institution: University of Edinburgh, UKInstallation:pip install sepalUse:Most functionality is demonstrated in Jupyter notebook format in ./demoFunctionality:Enhancement-to-concentration conversion (assuming fast water exchange)Fit tissue concentration using pharmacokinetic modelFit signal enhancement using pharmacokinetic modelPharmacokinetic models: steady-state, Patlak, extended Tofts, Tofts, 2CXM, 2CUMPatlak fitting with multiple linear regressionAIFs: including patient-specific (measured), Parker, bi-exponential ParkerFitting free AIF time delay parameterRelaxivity models: linearSignal models: spoiled gradient echo, inversion-recovery spin-echoWater exchange models: FXL, NXL, NXL_beT1 fitting using variable flip angle method, IR-SPGR, DESPOT1-HIFI and inversion recoveryT2(*) fitting for multi-TE acquisitionsMTR and MTSat calculationNot yet implemented/limitations:Additional pharmacokinetic models (add by inheriting from PkModel class)Additional relaxivity models (add by inheriting from CRModel class)Additional water exchange models, e.g. 3S2X, 2S1X (add by inheriting from WaterExModel class)Additional signal models (add by inheriting from SignalModel class)R2/R2* effects not included in fitting of enhancement curves (but is included for enhancement-to-concentration conversion)Compartment-specific relaxivity parameters/modelsFitting water exchange parametersUpdatesRelease 1.0.2 - Changed AIF interpolation method to linear to further reduce oscillations.
Release 1.0.1 - Changed AIF interpolation method to avoid oscillations. Added demo notebook on interpolation.
Package: sepal-ui
Sepal_uiCurrently translated in the following languages:EnglishFrançaisEspañol中国人You can contribute to the translation effort on ourcrowdin project.sepal_uiis a lib designed to create elegant python based dashboard in theSEPAL environment. It is designed on top of the amazingipyvuetifylibrary and will help developer to easily create interface for their workflows.
By using this libraries, you’ll ensure a robust and unified interface for your scripts and a easy and complete integration into the SEPAL dashboard of application.The full documentation is availablehereand demo apps can be launched on Heroku following these links:Map style applicationPanel style applicationWe are happy to receive feedback and we welcome any kind of contribution.ContributeIf you want to contribute you can fork the project in you own repository and then use it.
If you consider working with us, please follow thecontributing guidelines.Meet ourcontributor.
Package: separability
separabilityMy basic library for studying LLMs, with support for Multi-GPU inference and
editing, as well as quantilised inference (not editing yet).
This includes functions for analysing the activations of the models for
different inputs, and for pruning different parts of the model based on those
activations.The currently tested list of models is:EleutherAI's PythiaMeta OptMeta GalacticaGPT2BERTFor check out theexamples folderto see in more detail how the library can be used.Pruning based on CapabilitiesFor a full example, seesrc/examples/prune_30.py.The simple example is:from separability.data_classes import PruningConfig
from separability.parser import cli_parser
from separability.prune import run_pruning

# Configure initial model and tests
c = PruningConfig(
    wandb_project = "testing",
    model_repo   = "facebook/opt-125m",
    token_limit  = 1000,
    run_pre_test = True,

    # Removals parameters
    ff_scoring = "abs"
    ff_frac   = 0.02,
    ff_eps    = 0.001,
    attn_scoring = "abs",
    attn_frac = 0.00,
    attn_eps  = 1e-4,

    # Eval
    focus     = "pile_codeless",
    cripple   = "code",
    additional_datasets=tuple(),
)

# optionally, use parser to get CLI arguments.
# c, args = cli_parser(c)

# Run the iterated pruning
model, history = run_pruning(c)model.pyThis defines a wrapper function that encapsulates the HuggingFace implementation of Meta OPT.
To get the model, simply run:from separability import Model

m = Model("facebook/opt-125m", limit=1000)Where you can provide any of the model sizes that are pre-trained for OPT, and the token limit must be smaller than the max token length that the model is able to handle.Next, you can run the model to do 2 tokens of predictions, by, for example, running:text = 'Hello, my name is'
inpt, output = opt.predict( text, num=2 )We can look at the residual stream of how the output changes over time.residual_stream = opt.get_residual_stream( text )This will return a tensor of size2 + 2*n_layers.
i.e:the input (w/ positional encoding)n attention layer outputsn feed forward layer outputsthe final outputIf we want just the output of the attention / feed forward layers, we can instead look at the activations:inpt, attn_out, ff_out, output = opt.get_text_activations( text )or alternatively:inpt, attn_out, ff_out, output = opt.get_text_activations( residual_stream=residual_stream )To get the activations for the input text at all of the MLP mid layers, we can look at:opt.get_ff_key_activations( text )oropt.get_ff_key_activations( residual_stream=residual_stream ).texts.pyHas some basic tools for loading the text datasets I am using:'pile', ( EleutherAI's 'The Pile' dataset)'pile-codeless' (Pile without GitHub)'code' (CodeParrot's 'github-code' dataset)'python', (Subset of only python)'wiki', (WikiText)'civil', (Civil comments with toxicity < 0.2)'toxic', (Civil comments with toxicity > 0.8)...activations.pyHas code specific to the datasets I am using to analyze and attempt to remove capabilities from the models.
Package: separability-index
Separability IndexA suite of tools to assess the separability of two datasets.ExampleExample 1: Separability index for a case of hardly separable two-component datasets.Example 2: Separability index for a case of somewhat separable two-component datasets.Example 3: Separability index for a case of highly separable two-component datasets.An example application is presented by Pretell et al.:Pretell, R., Brandenberg, S.J., and Stewart, J.P. "Consistently computed ground motion intensity measures at liquefaction case history sites". (In preparation).CitationIf you use these codes, please cite:Pretell, R., and Brandenberg, S. (2023). separability_index: A suite of tools to assess the separability of two datasets (0.1.0). Zenodo.https://doi.org/10.5281/zenodo.10404856ContactFor any questions or comments, contact Renmin Pretell atrpretell@unr.edu.
Package: separableconv-torch
Unofficial PyTorch Module - Depthwise Separable ConvolutionAn illustration of Depthwise Separable Convolution. Credit:Depthwise Convolution Is All You Need for Learning Multiple Visual Domains.PyTorch (unofficial) implementation of Depthwise Separable Convolution. This type of convolution is introduced by Chollet inXception: Deep Learning With Depthwise Separable Convolutions. This package providesSeparableConv1d,SeparableConv2d,SeparableConv3d,LazySeparableConv1d,LazySeparableConv2d, andLazySeparableConv3d.InstallationInstallseparableconv-torchusingpip(require: Python >=3.7).pip install separableconv-torchParametersParameterDescriptionTypein_channelsNumber of channels in the input imageintout_channelsNumber of channels produced by the convolutionintkernel_sizeSize of the convolving kernelint or tuplestrideStride of the convolution. Default: 1int or tuple, optionalpaddingPadding added to all four sides of the input. Default: 0int, tuple or str, optionalpadding_mode'zeros','reflect','replicate'or'circular'. Default:'zeros'string, optionaldilationSpacing between kernel elements. Default: 1int or tuple, optionaldepth_multiplierThe number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal toin_channels * depth_multiplier. Default: 1int, optionalnormalization_dwdepthwise convolution normalization. Default: 'bn'str, optionalnormalization_pwpointwise convolution normalization. Default: 'bn'str, optionalactivation_dwdepthwise convolution activation. Default:torch.nn.ReLUCallable[...,torch.nn.Module], optionalactivation_pwpointwise convolution activation. Default:torch.nn.ReLUCallable[...,torch.nn.Module], optionalbiasIfTrue, adds a learnable bias to the output. Default:Truebool, optionalExample UsageFor 1-dimensional case.importtorchimportseparableconv.nnasnn# set inputinput=torch.randn(4,10,100)# define modelm=nn.SeparableConv1d(10,30,3)# process input through modeloutput=m(input)For 2-dimensional case.importtorchimportseparableconv.nnasnn# set inputinput=torch.randn(4,10,100,100)# define modelm=nn.SeparableConv2d(10,30,3)# process input through modeloutput=m(input)For 3-dimensional case.importtorchimportseparableconv.nnasnn# set inputinput=torch.randn(4,10,100,100,100)# define modelm=nn.SeparableConv3d(10,30,3)# process input through modeloutput=m(input)Stacked SeparableConv2d.importtorchimportseparableconv.nnasnn# set inputinput=torch.randn(4,3,100,100)# define modelm=nn.Sequential(nn.SeparableConv2d(3,32,3),nn.SeparableConv2d(32,64,3),nn.SeparableConv2d(64,96,3))# process input through modeloutput=m(input)For lazy 2-dimensional case.importtorchimportseparableconv.nnasnn# set inputinput=torch.randn(4,10,100,100)# define modelm=nn.LazySeparableConv2d(30,3)# process input through modeloutput=m(input)
Package: sepa-sctinst
SEPA SCTInst: A powerful Python SEPA toolkit for SCTInst PaymentsWhat is it?SEPA SCTInstis a python package that provides some features to manage SCTInst payments.Main FeaturesHere are just a few of the things that sctinst does well:XSD validationSCTInst Scheme implementationGenerate random paymentsWhere to get itThe source code is currently hosted on GitHub at:https://github.com/lquastana/sepa-sctinst.gitBinary installers for the latest released version are available at thePython
Package Index (PyPI)pipinstallsepa-sctinstDependencieslxml- lxml is the most feature-rich and easy-to-use library for processing XML and HTML in the Python language.Faker- Faker is a Python package that generates fake data for you.Installation from sourcesIn therootdirectory (same one where you found this file after
cloning the git repo), execute:python-mpipinstall-e.DocumentationThe official documentation generated withsphinxand hosted on Github:https://sepa-sctinst.readthedocs.io/
Package: sepbit.sistamapy
SistamapySimple statuses Mastodon for PythonThis package is compatible withPylint.Install# apt install -y python3 python3-pip python3-setuptools python3-wheel python3-venv python3-dev$python3-mvenvenv
$source./env/bin/activate
$pipinstallsepbit.sistamapyCredentialsIn theMastodonaccount, create an app withwrite:statusespermissionUsageThis is minimal example, seestatuses.fromsepbit.sistamapy.statusesimportStatusestoot=Statuses('mastodon.social','TOKEN')post=toot.post({'status':'test ','language':'por','visibility':'public'})print(post)TestsExecução de testes e verificar a cobertura de código$source./env/bin/activate
$pipinstall-rrequeriments.txt
$toxChange logPlease see theCHANGELOGfile for more information.ContributingPull Requests not accepted.SecurityIf you discover any security related issues, please emailcontato@sepbit.cominstead of using the issue tracker.LicenseGPL-3.0-or-later, please seeCOPYINGfile for more information.
Package: seperability
SeperabilityMy basic library for studying LLMs (currently, only the Meta OPT models).
This includes functions for analysing the activations of the models for different inputs, and for pruning different parts of the model based on those activations.Pruning based on CapabilitiesFor a full example, seesrc/seperability.ipynb.The simple example is:from model import Model
from activations import prune_and_evaluate, evaluate_all

# Load and Evaluate Model on Pile and Code

opt = Model('125m', limit=1000)
eval_data = evaluate_all(opt, 1e5)
print(eval_data)

# Prune Model, Removing coding capabilities (compared to pile), and evaluate

eval_data = prune_and_evaluate(opt, ff_prune_frac=0.05, attn_prune_frac=0.05,
    ff_eps=1e-3, sample_size=1e5, eval_size=1e5, cripple='code', focus='pile')
print(eval_data)model.pyThis defines a wrapper function that encapsulates the HuggingFace implementation of Meta OPT.
To get the model, simply run:from model import Model

opt = Model('125m', limit=1000)Where you can provide any of the model sizes that are pre-trained for OPT, and the token limit must be smaller than the max token length that the model is able to handle.Next, you can run the model to do 2 tokens of predictions, by, for example, running:text = 'Hello, my name is'
inpt, output = opt.predict( text, num=2 )We can look at the residual stream of how the output changes over time.residual_stream = opt.get_residual_stream( text )This will return a tensor of size2 + 2*n_layers.
i.e:the input (w/ positional encoding)n attention layer outputsn feed forward layer outputsthe final outputIf we want just the output of the attention / feed forward layers, we can instead look at the activations:inpt, attn_out, ff_out, output = opt.get_text_activations( text )or alternatively:inpt, attn_out, ff_out, output = opt.get_text_activations( residual_stream=residual_stream )To get the activations for the input text at all of the MLP mid layers, we can look at:opt.get_ff_key_activations( text )oropt.get_ff_key_activations( residual_stream=residual_stream ).texts.pyHas some basic tools for loading the two text datasets I am using:'the_pile' ( validation set of The Pile )'codeparrot-clean-valid' ( validation set of codeparrot )activations.pyHas code specific to the two datasets I am using to analyze and attempt to remove capabilities from the OPT.
Package: seperator
SeperatorInstallationfor create virtualenvpython3-mvenvseperator_env# according to used shell one of them# source seperator_env/bin/activate.sh# source seperator_env/bin/activate.csh# source seperator_env/bin/activate.fishfor install itpipinstallseperatorUsage Samples:fromseperator.linesimportline,datelineline()line("Hello World")line("Hello World",width=80)line("Hello World",char='*')line("Hello World",color='red')line("Hello World",align=5)line("Hello World",align=-5)line("Hello World",margin=(5,5))# you cannot additional# info but other parameters are same with linedateline()────────────────────────────────────────────────────────────────────────────────
─HelloWorld──────────────────────────────────────────────────────────────────
─HelloWorld────────────────────────────────────
*HelloWorld******************************************************************
─HelloWorld──────────────────────────────────────────────────────────────────
─────HelloWorld──────────────────────────────────────────────────────────────
──────────────────────────────────────────────────────────────HelloWorld─────
─HelloWorld──────────────────────────────────────────────────────────
─08Sep201910:35:04───────────────────────────────────────────────────────'
Package: sepiida
Sepiida=======An API framework for common tasks/behavior for a micro-service architecture.   Built on top of Flask, SQLAlchmeny, and Celery.Sepiida takes care of:- handling errors- REST methods- permissions (between microservices)- creating and managing sessions- ... and more.Contact info@authentise.com if you want to use it.Feature : Enviroment Loading=======Seipiida contains environment loading tools, to load environment variables. A default value needs to beset or created, and can be overridden by a environment variable, or by an exact-match to a docker secret.Feature: API Shaping========This tool contains a system for verifying incoming and outgoing  data matches a programmed API Signature, to verify basic dataconversion and type checking before reaching the inner post, put, get, list behavior.Feature: Filters========API's built in sepiida can take and process a standard set of URL filters to make it eaiser to shape and fliter dataFilters Examples:* page[offset]=NUMBER: Set how far into known data to paginate reults when there are many results* page[limit]=NUMBER: Set the count of how many responsees to return when paginating when there are many results* search[propertyy]=value : Searches 'property' of the specified endpoint for a matching value or partial valueLicense=======The MIT License (MIT)Copyright (c) 2016 Authentise Inc.Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: sepm-api
This is a wrapper around the Symantec Endpoint Protection Manager REST API.This includes some undocumented endpoints, that may not work as expected.All the information for the various endpoints were pulled fromhttps://apidocs.securitycloud.symantec.com/.If you find any bugs please open an issue or a pull request.NOTE: The Python wrapper API is work in progress and should be treated as a software in beta, especially regarding the “undocumented” API endpoints.UsageRefer to theexamplesfolder.Installation>>>pip install sepm-apiRequirementsPython :: 3.7+Environment Variables:API_USERNAME
API_PASSWORD
API_DOMAIN
API_BASE_URL  [default: https://localhost:8446/sepm/api/v1]CompatibilitySymantec Endpoint Protection Manager API 14.3LicenseThis software code is license underMIT.Authorssepm-apiwas written byAlex Almero.DisclaimersI am in no way affiliated with Broadcom. Symantec is a registered trademark by Broadcom.
Package: sepordeh
SepordehPython library for usingwww.sepordeh.compayemnt gatewaywebservice documentationکتابخانه پایتون جهت اتصال به درگاه پرداختwww.sepordeh.comمستندات وبسرویسInstallationpipinstallsepordehUsageایجاد فاکتورadd invoicefromsepordeh.paymentimportadd_invoice# returns invoice_idadd_invoice(merchant='',amount='',callback='',description='',orderId='',phone='')آدرس پرداختpayment urlfromsepordeh.paymentimportpayment_url# returns urlpayment_url(invoice_id='')تایید پرداخت فاکتورverify invoicefromsepordeh.paymentimportverify_invoice# returns card , invoice_idverify_invoice(merchant='',authority='')