Saravanan666/pypi-org huggingface dataset:
Package: hyperdb-py
HyperDB Python ClientAuthor:Afaan BilalIntroductionHyperDB Pythonis a Python client package for theHyperDB server.Installationpip install hyperdb-pyExample usagefromhyperdb_py.HyperDBimportHyperDB# Setup with address (default: http:#localhost:8765)hyperdb=HyperDB("http://localhost:8765")# OR# Setup with address and authentication# hyperdb = new HyperDB("http://localhost:8765", "username", "password")# Ping the serverr=hyperdb.ping()print(str(r))# True# Get the version numberr=hyperdb.version()print(str(r))# "[HyperDB v0.1.0 (https:#afaan.dev)]"# Set a valuer=hyperdb.set("test","value")print(str(r))# value# Check if a key is presentr=hyperdb.has("test")print(str(r))# True# Get a valuer=hyperdb.get("test")print(str(r))# value# Get all stored datar=hyperdb.all()print(str(r))# {test: "value"}# Remove a keyr=hyperdb.delete("test")print(str(r))# True# Delete all stored datar=hyperdb.clear()print(str(r))# True# Check if the store is emptyr=hyperdb.empty()print(str(r))# True# Persist the store to diskr=hyperdb.save()print(str(r))# True# Reload the store from diskr=hyperdb.reload()print(str(r))# True# Delete all store data from memory and diskr=hyperdb.reset()print(str(r))# TrueContributingAll contributions are welcome. Please create an issue first for any feature request
or bug. Then fork the repository, create a branch and make any changes to fix the bug
or add the feature and create a pull request. That's it!
Thanks!LicenseHyperDB Pythonis released under the MIT License.
Check out the full licensehere.
Package: hyperdb-python
HyperDBA hyper-fast local vector database for use with LLM Agents.Want to invest?Now accepting SAFEs ($135M cap minimum.)Join theHyperDBiscordAdvantagesSimple interface compatible withalllarge language model agents.Highly optimized C++ backend vector store with HW accelerated operations via MKL BLAS.Enables users to index documents with advanced features such asidsandmetadata.InstallationInstall the package from PyPI:pipinstallhyperdb-pythonUsageHere's an example of using HyperDB to store and query documents with information about all 151 original pokemonin an instant:importjsonfromhyperdbimportHyperDB# Load documents from the JSONL filedocuments=[]withopen("demo/pokemon.jsonl","r")asf:forlineinf:documents.append(json.loads(line))# Instantiate HyperDB with the list of documentsdb=HyperDB(documents,key="info.description")# Save the HyperDB instance to a filedb.save("demo/pokemon_hyperdb.pickle.gz")# Load the HyperDB instance from the save filedb.load("demo/pokemon_hyperdb.pickle.gz")# Query the HyperDB instance with a text inputresults=db.query("Likes to sleep.",top_k=5)ReturnsName: Snorlax
Pokedex ID: 143
HP: 160
Type: normal
Weakness: fighting
Description: Very lazy. Just eats and sleeps. As its rotund bulk builds, it becomes steadily more slothful.

Name: Drowzee
Pokedex ID: 96
HP: 230
Type: psychic
Weakness: dark
Description: Puts enemies to sleep then eats their dreams. Occasionally gets sick from eating bad dreams.

Name: Pinsir
Pokedex ID: 127
HP: 160
Type: bug
Weakness: fire
Description: When the temperature drops at night, it sleeps on treetops or among roots where it is well hidden.

Name: Abra
Pokedex ID: 63
HP: 160
Type: psychic
Weakness: dark
Description: Sleeps 18 hours a day. If it senses danger, it will teleport itself to safety even as it sleeps.

Name: Venonat
Pokedex ID: 48
HP: 160
Type: bug
Weakness: fire
Description: Lives in the shadows of tall trees where it eats insects. It is attracted by light at night.*Benchmark Credit: Benim Kıçım
Package: hyperdim
Hyperdimensionality computing machine learning library.Forked fromhttps://gitlab.com/alehd/hd-libInstallation and useFork this repository and put it in you projects folder or use pip or pipenv:pip install --user hyperdimIf you are using keras or sklearn it will be easy to use hyperdim.
To use a model of dimensionality, say 10000:from hyperdim.hdmodel import HDModel
from hyperdim.utils import to_categorical

import numpy as np

# Dummy datasets
samples = 100
features = 15
classes = 5

x = np.random.random(size = (samples, features))
y = [ int(np.random.random()*classes) for _ in x ]
y = to_categorical(y)

print(x.shape) # (samples, features)
print(y.shape) # (samples, classes)

# Build model
dimensions = 10000
model = HDModel(features, classes, d = dimensions)

# Fit using 30% of the data for validation, using one_shot_fit only
history = model.fit(x, y, validation_split = 0.3, epochs = 1)

print(history.history["acc"])
print(history.history["val_acc"])
Package: hyperdiv
HyperdivHyperdiv is a framework for rapidly developing reactive browser UI apps in Python, with built-in components, terse immediate-mode syntax, and minimal tool boilerplate.Hyperdiv includes theShoelacecomponent system, markdown support viaMistune, charts viaChart.js, support for reading/writing browser local storage, and forms whose validation logic is implemented in Python.Click this image to watch the coding demo video:Intro ArticleCheck outthe websiteand read theintro article.Demo AppsThere is aseparate repowith a few demo apps built with Hyperdiv.InstallationInstall Hyperdiv from PyPI:pipinstallhyperdivHyperdiv requires Python 3.9+ and has been tested on macOS and Linux.DocumentationAfter installing Hyperdiv, open the documentation app locally with the following command:hyperdivdocsThe documentation app isbuilt with Hyperdiv.RoadmapThird-party pluginsDistributed stateDiffing changes to text inputsVisual component builderPerformance improvementsContactIf you're interested in deploying Hyperdiv, want to share ideas, or have questions,say hello.
Package: hyperdrive-quant
hyperdrive: Robinhood analytics and algorithmic tradinghyperdriveis a project to obtain stock data, create trading strategies, test against historical data (backtesting), and deploy strategies for algorithmic trading.Getting StartedPrerequisitesYou will need Python 3.8+ and a Robinhood account.Place your credentials in a file named.envin the project root directory.
Follow this structure:RH_USERNAME=...
RH_PASSWORD=...
RH_2FA=...
IEXCLOUD=...InstallationTo install the necessary packages, runpip install -r requirements.txtUseMaking ScriptsTo make a script, create a new .py file in thescripts/dir with the following code:import sys
sys.path.append('src')
from Algotrader import HyperDrive  # noqa autopep8

drive = HyperDrive()Features:Broker authenticationAutomated data storageBacktesting engineMonte Carlo simulationsPlotting and technical analysisModel trainingStrategy definition (start with buy and hold)Buy and sell functionalityLive tradingDocumentationCheck out theRoadmapfor progress
...AuthUsing Robinhood 2FA, we can simply provide our MFA one-time password in the.envfile to login to Robinhood (viapyotp).DataPrice and VolumeActionsMergersBuybacksSentimentNews SentimentInstitutional SentimentAnalyst RecommendationsCompany / MicroProfile (Sector, # of Employees)EarningsCash FlowCEO CompensationGovernment / MacroUnemployment RateReal GDPUS Recession ProbabilitiesMarketGeneral Volatility (VIX)Sector Performance
Package: hyperDS
Copyright 2022 AbdulWahabPermission 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: hyperdx-opentelemetry
HyperDX OpenTelemetry Distro for PythonThis project is a fork fromhttps://github.com/honeycombio/honeycomb-opentelemetry-pythonSTATUS: this library is BETA.You're welcome to try it, and let us know your feedback in the issues!This is HyperDX's Distribution of OpenTelemetry for Python.
It makes getting started with OpenTelemetry and HyperDX easier!Latest release built with:OpenTelemetry version 1.18.0/0.39b0RequirementsPython 3.7 or higherGetting StartedHyperDX's Distribution of OpenTelemetry for Python allows you to streamline configuration and to instrument as quickly and easily as possible.DocumentationLicenseApache 2.0 License.
Package: hypereditor
# Django Hyper EditorDjango integration for [Hyper Editor](https://github.com/divineitlimited/hyper-editor).## InstallationInstall via pip```sybasepip install hypereditor```Add to ``settings.py`````pythonINSTALLED_APPS = [...'hypereditor']```Add to ``urls.py`````pythonurlpatterns = [...path('hypereditor/', include('hypereditor.urls')),]```## TODO- [x] Hyper Editor Output Parser- [x] Block System- [x] Create simple block without using js- [x] Django Model Integration ``HyperField``- [x] Django Admin Integration- [x] Django Form Integration- [x] Wagtail Integration ``HyperFieldPanel``- [x] Template tag for preview and render- [ ] Media Handling- [ ] Documentation
Package: hyperer
hypereraddsterminal hyperlinksto the output of other commands.
For example,hyperer-rgrunsripgrepand links to the files it finds.InstallationInstall Python 3.9 or later and runpip install hypererto install the hyperer commands into your system Python.
Alternatively, if you're using theNix package manager, depend on the flake.nix in this repo.Commandshyperer-cargo - wrapscargoand links to compilation failures, test failures, and backtraceshyperer-rg - wrapsripgrepand links to files it findshyperer-ruff - wrapsruffand links to failed checksCreditThe basic idea andhyperer-rgcomes fromkitty.hyperlinked_grepis kitty's version ofhyperer-rg.
If you only want ripgrep links and you already have kitty installed, you can runkitty +kitten hyperlinked_grepand you don't need to installhyperer.
I created this project to have a home forhyperer-cargoand to be able to hyperlink ripgrep without installing all of kitty.Many thanks to the kitty project!
Considersponsoring its creatorto help move the terminal forward.
Package: hyperfast
HyperFast : Instant Classification for Tabular DataHyperFastis a hypernetwork designed for fast classification of tabular data, capable of scaling to large datasets. Utilizing a meta-trained hypernetwork, HyperFast generates a dataset-specific target network in a single forward pass, eliminating the need for time-consuming model training.Installation:exclamation: Install the new release:HyperFast can be installed from PyPI usingpipinstallhyperfast==1.0.0This package contains example code to run HyperFast. Ensure that you are using Python 3.9 or later to run this project.
When using HyperFast for the first time, the model weights will be downloaded automatically. The model weights are also availablehere.UsageHere's a quick example on how to use HyperFast's scikit-learn-like interface:importtorchimportnumpyasnpfromhyperfastimportHyperFastClassifierfromsklearn.metricsimportaccuracy_score# Load your datasetX_train,y_train=np.load("data/hapmap1_X_train.npy"),np.load("data/hapmap1_y_train.npy")X_test,y_test=np.load("data/hapmap1_X_test.npy"),np.load("data/hapmap1_y_test.npy")# Set the devicedevice=torch.device('cuda:0'iftorch.cuda.is_available()else'cpu')# Initialize HyperFastmodel=HyperFastClassifier(device=device)# Generate a target network and make predictionsmodel.fit(X_train,y_train)predictions=model.predict(X_test)accuracy=accuracy_score(y_test,predictions)print(f"Accuracy:{accuracy*100:.2f}%")Warning:Setcat_featuresas the list of indices of the categorical features in the dataset (if any), now in__init__.The current default hyperparameters provide medium speed-accuracy performance.:rocket: For the fastest inference (but less accurate) setn_ensemble=1andoptimization=None.:bar_chart: If you are dealing with animbalanced dataset, consider settingstratify_sampling=Truewithn_ensemble> 1.:globe_with_meridians: If you are dealing with a veryhigh-dimensional dataset(e.g., >3000 features), consider settingfeature_bagging=Truewithn_ensemble> 1.:ok_hand: For slower but most accurate predictions, optimize the inference parameters of HyperFast for each dataset. In this case, we recommend the following search space:param_grid={'n_ensemble':[1,4,8,16,32],'batch_size':[1024,2048],'nn_bias':[True,False],'stratify_sampling':[True,False],'optimization':[None,'optimize','ensemble_optimize'],'optimize_steps':[1,4,8,16,32,64,128],'seed':list(range(10))}LicenseThis project is under the CC BY-NC 4.0 license. SeeLICENSEfor details.Cite usIf you use HyperFast in your research, please cite ourpaper:@inproceedings{bonet2024hyperfast,
  title={HyperFast: Instant Classification for Tabular Data},
  author={Bonet, David and Montserrat, Daniel Mas and Gir{\'o}-i-Nieto, Xavier and Ioannidis, Alexander},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2024}}
Package: hyperfit
HyperFitA neat python package for fitting N dimensional data with (possibly covariant) errors with an N-1 dimensional plane. Put simply, if you have data with x and y errors and you want the correct method for fitting a straight line, use this package! But it also works for higher dimensions. Examples, tutorials and API here:https://hyperfit.readthedocs.io/en/latest/Based on the R HyperFit package by Aaron Robotham and Danail Obreschkow. More details on the method can be found here:https://ui.adsabs.harvard.edu/abs/2015PASA...32...33R/abstract. Please cite this paper if you use this package.
Package: hyperfoil-client
hyperfoil-python-clientThis is a python client forHyperfoil.Right now it's a wrapper for Hyperfoil's REST API. In the future, I would like to add factories for easier benchmark creation in python.UsagefromhyperfoilimportHyperfoilClientimportyaml# Initialize clientclient=HyperfoilClient('http://hyperfoil_url.com')# Create new benchmarkwithopen('tests/benchmarks/hello-world.yaml')asfile:data=file.read()data=yaml.load(data,Loader=yaml.loader)client.benchmark.create(params=data)# Start benchmarkbenchmark_run=client.benchmark.start('benchmark_name')# Kill benchmarkbenchmark_run.kill()# All stats of runstats=benchmark_run.all_stats()# Accessing logs:logs=client.log()Run the testsBefore runningthe tests you need to export theHYPERFOIL_URLenvironment variable.
Package: hyperframe
This library contains the HTTP/2 framing code used in thehyperproject. It
provides a pure-Python codebase that is capable of decoding a binary stream
into HTTP/2 frames.This library is used directly byhyperand a number of other projects to
provide HTTP/2 frame decoding logic.Contributinghyperframe welcomes contributions from anyone! Unlike many other projects we
are happy to accept cosmetic contributions and small contributions, in addition
to large feature requests and changes.Before you contribute (either by opening an issue or filing a pull request),
pleaseread the contribution guidelines.Licensehyperframe is made available under the MIT License. For more details, see theLICENSEfile in the repository.Authorshyperframe is maintained by Cory Benfield, with contributions from others. For
more details about the contributors, please seeCONTRIBUTORS.rst.
Package: hypergen-translation-plugin
Hypergen Translation PluginTranslation plugin forDjango Hypergen. Processes the AST
of Hypergen template python files and inserts them into the database for translation.Provides theTranslationPluginthat makes Hypergen liveviews and actions translatable based on the current
django translation language.UsageInstallation:pip install hypergen-translation-pluginChangesettings.py:# Add to installed apps:
INSTALLED_APPS = [
    ...
    'hypergen_translation'
]

# Select which importable modules to look for translatable strings in.
HYPERGEN_TRANSLATION_MODULES = ["hypergen_first_app.views"]
# The base dir of the git/whatever project.
HYPERGEN_TRANSLATION_PROJECT_DIR = BASE_DIR.parent
# Optionally display link directly to file and linenumber for each translation occurence.
HYPERGEN_TRANSLATION_GITHUB = "https://github.com/runekaagaard/hypergen-translation-plugin"
# Optionally link to another branch than main.´ on github.
HYPERGEN_TRANSLATION_BRANCH = "dev"Collection collections and store it in the database:python manage.py hypergen_translation_collectThen edit in the admin.Add plugin to liveviews and actions:from hypergen_translation.plugins import TranslationPlugin

@liveview(..., user_plugins=[TranslationPlugin()])
def my_view(request):
    p("translate this!")
Package: hyperglass
The network looking glass that tries to make the internet better.A looking glass is implemented by network operators as a way of providing customers, peers, or the general public with a way to easily view elements of, or run tests from the provider's network.Documentation|Live Demohyperglass is intended to make implementing a looking glass too easy not to do, with the lofty goal of improving the internet community at large by making looking glasses more common across autonomous systems of any size.ChangelogFeaturesBGP Route, BGP Community, BGP AS Path, Ping, & TracerouteFull IPv6 supportCustomizable everything: features, theme, UI/API text, error messages, commandsBuilt in support for:Arista EOSBIRDCisco IOS-XRCisco IOS/IOS-XECisco NX-OSFRRoutingHuaweiJuniper JunOSMikrotikNokia SR OSTNSRVyOSConfigurable support for any othersupported platformOptionally access devices via an SSH proxy/jump serverVRF supportAccess List/prefix-list style query control to whitelist or blacklist query targets on a per-VRF basisREST API with automatic, configurable OpenAPI documentationModern, responsive UI built onReactJS, withNextJS&Chakra UI, written inTypeScriptQuery multiple devices simultaneouslyBrowser-based DNS-over-HTTPS resolution of FQDN queriesTo request support for a specific platform, pleasesubmit a Github Issuewith thefeaturelabel.Get Started →CommunitySlackTelegramAny users, potential users, or contributors of hyperglass are welcome to join and discuss usage, feature requests, bugs, and other things.hyperglass is developed with the express intention of being free to the networking community.However, if you're feeling particularly helpful or generous, small donations are welcome.Acknowledgementshyperglass is built entirely on open-source software. Here are some of the awesome libraries used, check them out too!FastAPINetmikoScrapliPydanticChakra UI
Package: hypergol
HypergolHypergol is a Data Science productivity toolkit to accelerate small team projects into production at the shortest possible time while still maintaining high standard of code. This is achieved by autogenerating structure and code in a form that is easy to extend, so only the project-specific code needs to be written.Hypergol provides parallel execution capabilities with little restrictions. This enables a small team to accelerate time-consuming tasks by renting larger instances from a cloud provider, but with no additional infrastructure, reducing the time and cost of setting up.The toolkit provides a standard serialisation (autogenerated) and storage system that natively enables parallel processing. The resulting data is also easily accessible from any python code, including jupyter notebooks.Every Hypergol Project is connected to a git repo that acts as a version controller. Both code and generated data are linked to git branches and commits. Full data lineage can be retrieved, and the storage system is verifiable through SHA1 hashes.Tensorflow model stubs can be generated that enables standardised model development and training that both connect to the storage system above, observeSOLIDprinciples and enable autogenerated deployment withFastAPI.See documentation for further details at:https://hypergol.readthedocs.io/en/latest/Join our community at ourDiscordserver.Quick StartInstall it with:pip install hypergolCreate your first projects:python3 -m hypergol.cli.create_project MyFirstProject
cd my_first_projectAnd follow instructions in the projectsREADME.mdor the tutorial at:https://hypergol.readthedocs.io/en/latest/tutorial.htmlGood Luck using Hypergol!The authors wish that Hypergol frees you from tedious tasks so you can focus more on the core part of Machine Learning and generate everything else.Join our community at ourDiscordserver.All feedback is welcome at:hypergol.developer@gmail.com
Package: hypergrad
hypergradSimple and extensible hypergradient for PyTorchInstallationFirst, installtorchand its accompanyingtorchvisionappropriately. Then,pip install hypergradMethodsImplicit hypergradient approximation (via approximated inverse Hessian-vector product)conjugate gradientNeumann-series approximationNyström methodImplementation of these methods can be found inhypergrad/approximate_ihvp.pyCitationTo cite this repository,@software{hypergrad,author={Ryuichiro Hataya},title={{hypergrad}},url={https://github.com/moskomule/hypergrad},year={2023}}hypergradis developed as a part of the following research projects:@inproceedings{hataya2023nystrom,author={Ryuichiro Hataya and Makoto Yamada},title={{Nystr\"om Method for Accurate and Scalable Implicit Differentiation}},booktitle={AISTATS},year={2023}}
Package: hypergrid
hypergridHypergrid is designed to enable concise declaration and manipulation of parameter grids possible, which are often required when tuning ML hyperparameters or defining large batch jobs.Hypergrid allows you to easily define multi-dimensional parameter grids, and then combine them in various ways, including sum-types, product-types, and co-iteration.  Once defined, you can easily map, filter, select, and apply arbitrary functions in order to easily compose and iterate over your grid.
Package: hypergz
Installation$pipinstallhypergzTry the algorithmTo run your own example clickhereIntrouctionThis repository is an implementation of two algorithms:1) Force-Directed Graph Drawing Using Social Gravity and ScalingLink for atricle: https://arxiv.org/pdf/1209.0748.pdfAuthors:Michael J. BannisterDavid EppsteinMichael T. GoodrichLowell TrottThis algorithm creates a pleasant drawing of agraphin a shape of a circle using attraction and rejection forces as well as social.This algorithm offers 3 methods of social gravity:Centrality by clossness:The closer a vertex is to other vertices the more central it will be.Centrality by betweeness:Vertex who is part of more shortest paths will be more central.Centrality by degree:Vertex with higher degree will be more central.Example of different centralities in tree graphNetworkxClossnessBetweenessDegree2) Hypergraph Drawing by Force-Directed PlacementLink for atricle: https://www.researchgate.net/publication/318823299_Hypergraph_Drawing_by_Force-Directed_PlacementAuthors:Naheed Anjum ArafatStephane BressanThis algorithm creates a pleasant drawing of ahyper-graphusing any force-directed algrithm.
We implemnted with the first algoritm.
This algorithm offers 4 methods of converting hyper-graph to a graph (to apply force-directed algorithm on):Complete graph: connecting all vertices with all vertices.Cycle graph: connecting each vertex to the next vertex.Star graph: connecting all vertices to their center of Mass.Wheel graph: union of theCycleand theStargraphs.Example of different graphsCompleteCycleBetweenessDegreeExample of hyper-graph with 10 vertices and 8 hyper-edgesCompleteCycleBetweenessDegreeThe team:Amit Sheer CohenNeta Roth
Package: hyperhdr-py
This library is a modified version ofDermot Duffy's excellenthyperion-pyThank you dermotduffy! This literally would not exist without you 🎉I am not a Python developer so you may run into something that doesn't work quite right. I've tested this on my own setup and it works well. If you do run into any problems, I'm sorry 🙁Feel free to open an issue and/or help if you know Python 😀LibraryPython library forHyperHDR.