Saravanan666/pypi-org huggingface dataset:
Package: lorien
Lorien: A Hyper-Automated Tuning System for Tensor OperatorsLorien is a system built on the top of TVM to massively explore/benchmark the best schedule configs
of TOPI schedules.MotivationAlthough TVM already has a TOPI (TVM Operator Inventory) with the implementations of algorithm
and schedules for commonly used operators such as conv2d and dense, there is a challenge makes
TOPI hard to be improved efficiently.The best schedule of TOPI is stored inTopHub,
which is a JSON file in GitHub. However, it has the following problems.Storing all schedules in a single text file has low accessibility and scalability.
Every time AutoTVM has to load an entire JSON file in order to find only one
schedule config for a workload.The coverage of workloads and platforms are insufficient in the current version.
For example, the latest TopHub covers only 690 workloads forCUDA backend,
including conv2, depthwise conv2d, and 5 GPU models.Comparing to TVM that has several commits everyday, TopHub is not frequently updated.
As a result, some schedule configs are out-of-date and cannot achieve
good performance anymore.Since it is impractical to use TVM CI to benchmark the performance for every pull request, we need
a separate system to regularly benchmark and update the stored schedule configs.Commandline Interface and Example UsagesThe system has a complete CLI with hierarchical commands. All commands can also be
specified in a config file in YAML format, and use a prefix "@" to expand them.
See the following examples for CLI usages, andconfigs/samplesfor example configurations.
Note the the complete description of each command can be retrieved by the help command:python3-mlorien<commands>-hExtract workloads from a Gluon CV model.python3-mloriengenerateextractgcv--modelalexnet--targetllvmExtract workloads from a TF model.python3-mloriengenerateextracttf--model./mobilenet.pb--targetllvmExtract workloads from a Gluon CV model and mutate them to generate new workloads.python3-mloriengeneratemutatemodelzoorules.yaml--modelalexnet--targetllvmTune workloads with RPC servers.# tune.yaml
rpc:
    llvm -mcpu=skylake-avx512:
      - localhost:18871
db:
    endpoint_url:
        http://localhost:10020
log-s3-bucket: saved-tuning-logs
ntrial: 3000python3-mlorientune@tune.yaml@gcv_workloads_llvm.yamlSystem RequirementsPython 3.6+Amazon DynamoDB (local or aws): DynamoDB is used for storing and maintain the tuned schedules.
You can choose to either 1) launch alocal versionand specify endpoint URL (e.g.--db "endpoint_url: http://<your IP>:8000"), 2) or launch an AWS service, configure
AWS CLI in your machine, and specify the region name (e.g.,--db "region_name: us-west-1") when invoking the tuning.AWS S3 (optional): S3 is used to store the full tuning logs (JSON files generated by AutoTVM). This is an optional requirement,
so if you did not specify--log-s3-bucket bucket_name, then the full tuning logs will not be uploaded but only the best
schedule config will be submitted to the DynamoDB.DocumentationTBA
Package: lorikeet
⚠️Note:Lorikeet is a work in progress. It may change at any time,
and you shouldn’t use it in production yet.Lorikeet is a simple, generic, API-only shopping cart framework for
Django.Lorikeet currently supports Django 2.2 and 3.0 on Python 3.6+. New versions of Lorikeet will supportDjango versions that currently have extended support.Why use Lorikeet?E-commerce apps are divided into two types: simple ones that work well so long as what you’re selling is simple, and complex ones that try to be all things to all people by way of a maze of checkboxes and dropdowns.Lorikeet isn’t an e-commerce app; it’s a shopping cart framework.With Lorikeet, you define models for line items (the things that go in your cart), delivery addresses and payment methods yourself. For complex shops, this means you can model exactly the functionality you need without fighting the system. For simple shops, this is a simple process that requires way less code than you’d expect, and gives you a system without unnecessary bloat, but with room to grow.Lorikeet only cares about the cart itself; everything outside of that, including your navigation and product pages, is directly under your control, so you’re free to use a simpleListViewandDetailView, Wagtail, Mezzanine, Django CMS, or something totally bespoke. There’s not a single line of HTML or CSS in Lorikeet’s codebase either, so Lorikeet gives you total control over your visuals too.Lorikeet line items, delivery addresses and payment methods are designed to be orthogonal, so you can package them as reusable apps and share them internally between sites in your company, or with the world as open-source packages. In fact,Lorikeet already includes an optional Stripe payment method plugin, totally separate from the rest of the codebase and written against the same public API as your own apps.Because most modern payment providers require JavaScript anyway,Lorikeet is API-only. This lets you build a fast, frictionless shopping experience where users can add to and change their shopping carts without the entire page refreshing each time, and Lorikeet’s API is designed to allow logged-in repeat users to check out in a single click.Why use something else?Lorikeet isn’t turnkey.For simple sites, you won’t need to write much Python code; for complex ones, the time it takes to get up and running will probably be comparable to the time it takes to figure out how to bend e-commerce apps to your will. But the total control over the frontend that Lorikeet gives you means you’ll need to write a fair bit of HTML, CSS and JavaScript to get up and running, so if you need to go from zero to shop quickly, it’s best to look somewhere else.Lorikeet sites will require JavaScript.Lorikeet doesn’t provide regular HTML-form-based views for adding items to the cart and checking out; if you need this, Lorikeet isn’t for you.
Package: loris
LorisWith loris you can read and write different file formats from neuromorphic cameras such as.aedat4,.dat,.esor.csv. Loris is also anamazing animalSupported formatsversionreadwrite.aedat44☑☐.dat1/2☑☐.es2.x☑☑.csv-☑☐InstallpipinstalllorisHow to lorisRead a file, for example a .dat file and loop over all eventsimportlorismy_file=loris.read_file("/path/to/my-file.dat")events=my_file['events']foreventinevents:print("ts:",event.t,"x:",event.x,"y:",event.y,"p:",event.p)Write events from an array to an .es fileordering="xytp"# corresponds to the order in your arrayloris.write_events_to_file(event_array,"/path/to/my-file.es",ordering)Write structured events (with dtypes) to an .es fileloris.write_events_to_file(structured_event_array,"/path/to/my-file.es")Windows test (for developers)To make sure the library works on Windows (if you use macOS or Linux), follow these steps:Download Vagrant (https://www.vagrantup.com/)cd windowsvagrant up(this will download a virtual machine and build the package)vargrant ssh(optional, to inspect the Windows VM)vagrant destroy -f(cleanup)
Package: loristrck
loristrckis a wrapper for the C++ partial-tracking library Loris.It is written in cython and targets python 3 (>= 3.8 at the moment).
The source of the library is included as part of the project and
does not need to be installed previously.Documentationhttps://loristrck.readthedocs.ioInstallationpipinstallloristrckInstall from source in WindowsYou need to have Visual Studio installed# From a Developer Powershellpythonscripts/prepare_windows_build.py# From a normal promptpipinstall.Usageimportloristrckasltsamples,sr=lt.sndreadmono("/path/to/sndfile.wav")partials=lt.analyze(samples,sr,resolution=60)# partials is a python list of numpy arrays# select a subset of most significant partialsselected,noise=lt.select(partials,mindur=0.02,maxfreq=12000,minamp=-60,minbp=2)# print each partial as dataforpartialinselected:print(partial)# plot selected partialslt.plot_partials(selected)# now resynthesize both parts separatelylt.partials_render(selected,outfile="selected.wav")lt.partials_render(noise,outfile="noise.wav")Each partial will be a numpy array of shape = (numbreakpoints, 5)
with the columns:time, frequency, amplitude, phase, bandwidthSee the example scripts inbinfor more complete examplesAuthorEduardo Moguillanskyeduardo dot moguillansky @ gmail dot comLicenseGPL
Package: lorm
Lorm is a light weight mysql client library for Python.
Built-in connection pool, Django style lookup expressions.InstallationThe last stable release is available on PyPI and can be installed withpip:$ pip install lormExampleCREATETABLE`pets`(`id`int(10)unsignedNOTNULLAUTO_INCREMENT,`name`varchar(20)NOTNULLDEFAULT'',`add_time`datetimeDEFAULTNULL,PRIMARYKEY(`id`))ENGINE=InnoDBDEFAULTCHARSET=utf8mb4;Connect to Mysql>>>importpymysql>>>importlorm>>>db=lorm.Hub(pymysql)>>>db.add_pool('default',host='localhost',port=3306,user='root',passwd='root',db='test',autocommit=True,pool_size=8,wait_timeout=30)Insert>>>db.default.pets.create(name='cat')1Query>>>db.default.pets.get(id=1){u'id':2,u'name':u'cat'}Row Style>>>db.default.pets.filter(id__lt=10).select('id')[:][{u'id':1},{u'id':2},{u'id':4},{u'id':5},{u'id':6},{u'id':7},{u'id':8},{u'id':9}]>>>db.default.pets.filter(id__lt=10).values('id')[:]((1,),(2,),(4,),(5,),(6,),(7,),(8,),(9,))>>>db.default.pets.filter(id__lt=10).flat('id')[:][1,2,4,5,6,7,8,9]Raw SQL>>>db.default.fetchall("select * from pets")((1,u'cat'),(2,u'dog'),(3,u'bird'))Transaction>>>withdb.defaultasc:>>>printc.pets.create(name='fish')For more examples, seeexample.pyFeaturesBuilt-in Connection poolDjango style lookup expressionsConcurrent safeGevent friendlyDynamically driver
Package: los-angeles-citywide-data-style
Los Angeles Citywide Data StyleStyling and themes for data science projects in the City of Los Angeles,
following the style guide documentedhere.This contains a Python package which can be installed and imported.
Upon import, it enables a custom matplotlibstyle sheet.It also includes a set of color maps which can be specified in various matplotlib charts.InstallationThis package may be installed withpipinstalllos-angeles-citywide-data-styleUsageYou can use it by importing it:importlos_angeles_citywide_data_styleAfter import, the above color maps will be registered, and can be referenced by their names.
Package: losaunideireccional
Proyecto Final de Python
Segundo BimestreObjetivos de aprendizajeAplicar de modulos y paquetesAplicación de funciones utilizando las diferentes normativasHerramientasgitgithubpythonpoetrynumpyOrganización de carpetasTODO: Actualizar, revisar uso de comandotreeydocumentode referenciapoetry-demo
├── pyproject.toml              
├── README.md                   # Documentación del proyecto 
├── pyproject.toml
├── propuesta.txt
├── poetry.lock       
├── __init__.py  
├── main.py  
├── LosaUnideireccional
│   └── __init__.py
│   └── armaduralongitudinal.py
│   └── controlDeflexión.py
│   └── determinación_de_Cargas.py
│   └── determinaciónMomentos_Ultimos.py
└── imgTODO: Actualizar la lista de archivos.DescripciónDiseño de Losa alivianadaEste Programa nos permite calcular la carga que soportara la losa que esta pensada para diseñarse
Es un losa de funciones basicas que se la utiliza para viviendas o escaleras, de construcción alivianada ya sea en su eje x o en su eje yFuente de datosTODO: Detallar la fuente de datos sólo en el caso que su aplicación utilice datos externos. Agregar URLs.Se indica las normativas estandarizadas para la construcción en el ecuadorTODO: Actualizar captura y enlace  a video en youtubeNormativas NECVersiones y evoluciones del productoTODO: CompletarVersión 0.1.2 Julio 2022Cálculo  de diseño de losaCálculo de momento flector negativoCálculo de momento flector positivoCorrelación de grafica de pandeo para analisis....
Package: losd
losda very simple python package for SPARQL querying the LOSD (linked open social data) dataset.install with$ pip install losdor$ python setup.py losdFor greater control of customization, hacking and debugging, clone the repository and install with pip using -e:$ git clone https://github.com/ttm/losd.git
$ pip3 install -e <path_to_repo>usageQueries have the heading:PREFIX po: <http://purl.org/socialparticipation/po/>Examples of usage:importlosdaslpl=l.plainQueryValuesq=l.query# get all snapshots:query='''SELECT ?s WHERE {?s a po:Snapshot}'''res=pl(q(query))########### from here on, check to assure that the uris correspond to# the snapshot (types) intended# get all friendship relations in a facebook snapshot:uri=res[99]query='''SELECT ?a1 ?a2 WHERE {?f a po:Friendship . ?f po:snapshot <%s> .?f po:member ?a1, ?a2 .FILTER(?a1 != ?a2)}'''%(uri,)res2=pl(q(query))# get all retweet interactions in a Twitter snapshot:uri=res[-1]query='''SELECT ?a1 ?a2 WHERE {?m1 po:retweetOf ?m2 . ?m1 po:author ?a1 . ?m2 po:author ?a2 .?m1 po:snapshot <%s>}'''%(uri,)res3=pl(q(query))# get all interactions in a email snapshot:uri=res[48]query='''SELECT ?from ?to WHERE {?message1 po:snapshot <%s> . ?message2 po:replyTo ?message1 .?message1 po:author ?from . ?message2 po:author ?to .}'''%(uri,)res4=pl(q(query))# get all interactions in a IRC snapshot, with texts:uri=res[102]query='''SELECT ?a1 ?a2 ?t WHERE {?m a po:IRCMessage . ?m po:author ?a1 . ?m po:directedTo ?a2 .?m po:cleanText ?t . ?m po:snapshot <%s>}'''%(uri,)res5=pl(q(query))# get all interactions in the AA snapshot:query='''SELECT ?a1 ?a2 WHERE {?s po:author ?a1 . ?s po:checkParticipant ?a2 .}'''res6=pl(q(query))# get all friendshipts in the Participa.BR snapshot:uri=res[104]query='''SELECT ?a1 ?a2 WHERE {?f a po:Friendship . ?f po:snapshot <%s> .?f po:member ?a1, ?a2 .FILTER(?a1 != ?a2)}'''%(uri,)res7=pl(q(query))# get all interactions in the Participa.BR snapshot:uri=res[104]query='''SELECT ?a1 ?a2 WHERE {?a po:snapshot <%s> . ?a a po:Article .?a po:author ?a1 . ?c po:article ?a . ?c po:author ?a2 .}'''%(uri,)res8=pl(q(query))# get all interaction in the Cidade Democrática snapshot:uri=res[45]query='''SELECT ?a1 ?a2 WHERE {?t po:snapshot <%s> .  ?t a po:Topic . ?t po:author ?a1 .?c a po:Comment . ?c po:topic ?t . ?c po:author ?a2 .}'''%(uri,)res9=pl(q(query))interaction=[res3,res4,res5,res6,res8,res9]friendship=[res2,res7,]fr=[]forrelationsinfriendship:fr.append(l.mkRelationNetwork(relations))inte=[]forinteractionsininteraction:inte.append(l.mkInteractionNetwork(interactions))# finished. Play with the networks in fr and inte# maybe use networkx and pylab to plat them# :::deployment to pypiThis package іs delivered by running:
$ python3 setup.py sdist
$ twine upload dist/Maybe use "python setup.py sdist upload -r pypi" ?Further informationFurther information should be found in the LOSD article repository:Better usagePlease consider registration into Data.World to use LOSd in accordance with their policy and facilitating assistance by their staff.
Take a look attheir own python package.ContactAny issues, questions or ideas should be sent to:renato (dot) fabbri [AT] gmail {DOT} com:::
Package: los-docusign
Django-DocuSignDjango wrapper app for DocuSign functionalitiespip install django-docusignRunning TestsWe have a unit test defined for testing the los application.
This can be executed using the below command.python manage.py testUsageIn order to use the system you must add los_docusign.apps.LosDocusignConfig to your installed apps in your settings.py file.INSTALLED_APPS=['los_docusign']Test file has the sample implementation of the test_appFunctions in client.pygenerate_docusign_preview_url(dict)Params required in dict:"envelope_id""authentication_method""email""user_name""client_user_id""return_url"create_envelope(payload)Params required:DocuSign payload in JSON formatdownload_docusign_document(dict)Params required in dict:"envelope_id""doc_download_option"Valid Values:archive - If the document to be downloaded in zip format.combined - If the document to be downloaded as a combined document.process_docusign_webhook(xml_string)Params required:Webhook XML string received from Docusign.Response dict:
{
"envelopeId": "c57ec066-c5fa-4aa0-873d-6f285d70242a",
"envelope_status": "sent",
"recipients": [
{
"recipient_id": "a7f73f21-c4ff-4bcb-97c4-b03c91b8528a",
"email": "test@test.com",
"name": "John Nash",
"status": "autoresponded"
},
{
"recipient_id": "511b2ad3-6650-4773-a6b4-47f64a0ccdaf",
"email": "jerry@test.com",
"name": "Jerry Tunes",
"status": "created"
},
{
"recipient_id": "0851505f-5af2-42df-bce4-9e0ebe8bd2e2",
"email": "tom@test.com",
"name": "Tom Tunes",
"status": "created"
}
]
}update_envelope_and_resend(envelope_id, signers_data)
This function is used to update the email/phone number of the signer.
This is also used to resend the same envelope to the signers. For resending, recipientId and email are mandatory.Params required:envelope_id - The envelope id for which we need to update the signers information or send the same envelope to the signers.signers_data - The signer array which has the information about the signers that needs to be updated.
Params required in signers_data:email - The email of the signer.recipientId - The recipient id of the signerphone  - The phone number of the signer (optional)signers_data example:
[
{
"recipientId":"123456",
"email":"test@test.com",
"phone":"1234567890"
}
]update_envelope_status(status_info):
This function is used to update the status of the envelope.Params required:status_info - The dictionary variale which has the following parameters.envelope_id - The envelope id for which the status needs to be updatedstatus - The status in which we need to set the envelopereason - The reason (if applicable) to be set for the status change. This will be visible to the signerstatus_info example:
{
"envelope_id":"b1435c5d-3d67-46e8-ab6a-54789f42924e",
"status":"voided",
"reason":"Voiding an envelope"
}
Package: lose
loselose, but in particularlose.LOSE, is a helper class for handling data usinghdf5file format andPyTables>>>fromloseimportLOSE>>>LOSE()<losehdf5datahandler,fname=None,atom=Float32Atom(shape=(),dflt=0.0)>generatorparameters:iterItems=None,iterOutput=None,batch_size=1,limit=None,loopforever=False,shuffle=Falseinstallationpip3install-Uloseorpipinstall-Ulosedocumentationdocumentation,change logbugs/issuesif you find any, raise an issuehere
Package: losoto
LoSoTo: LOFAR solutions toolMain developer:Francesco de GasperinContributed code:David RaffertyHenrik EdlerMaaijke MeviusJose Sabater MontesMartin HardcastleAndreas Hornefferand the LOFAR Survey Key Science ProjectCite:If you use LoSoTo for your data reduction, please cite: de Gasperin et al. 2019, Astronomy & Astrophysics, Volume 622, id.A5
(https://ui.adsabs.harvard.edu/abs/2019A&A...622A...5D/abstract)Install:Install latest official release of LoSoTo from PyPI:pip install --upgrade losotoOr, install latest development version of LoSoTo from GitHub:pip install --upgrade --user https://github.com/revoltek/losoto/archive/master.zipOr clone the LoSoTo repository fromhttps://github.com/revoltek/losoto, and install LoSoTo using:python setup.py install --prefix=~/mydir/Prepare aparsetfile, which can be based on the fileparset/losoto2.parsetIn case of problems write to Francesco de Gasperin:astro@voo.itH5parm plotter (GUI):This is an external project maintained by Frits Sweijen:https://github.com/tikk3r/lofar-h5plotDocumentation:Documentation of LoSoTo API is at:http://revoltek.github.io/losoto/losoto.htmlA detailed explanation of all the parameters is at:http://revoltek.github.io/losoto/losoto.operations.htmlA few important articles/how-to are on the github wiki:https://github.com/revoltek/losoto/wikiContents:bin/: the losoto executable and some manipulators for H5parmsdocs/: documentationexamples/: some examples h5parm to use with validation/test parsetslosoto/operations/: contains all the modules for operationsparsets/: some examples parsetstools/: contains some external toolsRequired packages:Python 3PyTables version:  >3.4.0HDF5 version:      >1.8.4NumPy version:     >1.9.0Scipy              >1.4 (for interpolatedirections)
Package: lospec2aseprite
lospec2asepriteImport a lospec palette into aseprite, personal use, feel free to read and modify this for your own needsHow to use# lospec2aseprite [URL]It will automaticaly create an extension per author and add the palette. If several palettes for an author are added, it automatically update the extension. You have to restart Aseprite between adds.How to installViapippip install lospec2asepriteVia the sourcesgit clone https://github.com/Konosprod/lospec2aseprite.gitcd lospec2asepritepoetry install
Package: loss
lossLossWARNING: This repo is in development. It was automatically generated withmkpylib. If you're reading this message, it means that I use this repo for my own purposes right now. It might not do anything at all; the default functionality isprint('TODO').If you really want to try it out, feel free. I recommend reading through thetestsand commit history to see if it does what you need, orask mefor status updates.Stay tuned!Installpip3 install -U lossOr develop locally:git clone https://github.com/shawwn/loss ~/loss
cd ~/loss
python3 setup.py developUsageimportlossprint('TODO')LicenseMIT. SeeLICENSEfile.ContactA library byShawn Presser. If you found it useful, please considerjoining my patreon!My Twitter DMs are always open; you shouldsend me one! It's the best way to reach me, and I'm always happy to hear from you.Twitter:@theshawwnPatreon:https://www.patreon.com/shawwnHN:sillysaurusxWebsite:shawwn.com
Package: losscape
loss + landscape =losscape🌄This islosscape, a lightweight, modular, and straightforward Python library that empowers you to visualize the loss landscape of your neural networks, as inVisualizing the Loss Landscape of Neural Nets.losscapeis designed as a plug-and-play extension for your existing PyTorch models. It boasts its simplicity, modularity, and fast rendering speed.Key Features ✨🏃Fast Rendering: Leveraging PyTorch'storch.no_grad()feature and controlling the number of examples to compute the loss,losscapespeeds up the visualization process dramatically, thus saving valuable wall time.📊Flexible Plotting: Supports both 2D and 3D plotting options for your loss landscapes, giving you the power to choose based on your requirements and computational constraints.🔌Plug and Play: Easy to integrate with your existing PyTorch models. No substantial modifications to your codebase are needed!Installation 📦You can installlosscapevia pip:pip install losscapeUsage ⚙️Here's a quick example on how to uselosscape:from losscape.train import train
from losscape.create_landscape import create_2D_losscape

model = ... # create your torch model (subclass of torch.nn.Module)
train_loader = ... # the DataLoader containing your favorite dataset

train(model, train_loader) # losscape can perform the training for you
create_2D_losscape(model, train_loader, output_vtp=True)Typical results :To visualize the loss landscape in 3D, use the.vtpfile created by the library and simply drag-and-drop it ina VTK viewer:Documentation 📖For more details, please refer to the documentation. It provides a global overview on howlosscapeworks, and on how you can leverage it with your own model.
(to be written : for now you can refer to the code, its simple enough to understand the structure of the library, and you can spin up using the juypter notebook example, all available on the Github repo).References 📚Visualizing the Loss Landscape of Neural NetsVisualizing the Loss Landscape of Winning Lottery TicketsRoadmap 🚀Here are a couple of additions I'm planning to add in the near future:📈 Validation Loss Landscape: I'm working on the ability to visualize the validation loss landscape, in addition to the training loss landscape.🛤 Optimizer Path Visualization: Future updates will include the ability to visualize the path of gradient descent (or any other optimizer) on these landscapes using PCA.Contact 📞Feel free to open an issue if you find a bug or have any suggestions to improve the library. Feedback is much appreciated!Enjoy your visualization journey withlosscape! 🎉