Saravanan666/pypi-org huggingface dataset:
Package: hyper-inspector
A dummy/mocking server to inspect incoming HTTP connections. Use it to test/debug Webhooks. Provides nice console logging + a dump of every request as a file.$dockerrun-it-p5555:5555-v$(pwd)/logs:/appsantiagobasulto/hyperExplanation:-p P1:5555,P1is the local port in your host.-v YOUR_PATH:/app,YOUR_PATHis a volume in your file system to store the logs of the requests.Logs names have the conventionMETHOD.PATH.TIMESTAMP.request.jsonandMETHOD.PATH.TIMESTAMP.body.EXTENSION(if a body is sent). For example,POST.some.path.1617889344.request.jsonandPOST.some.path.1617889344.body.jsonIf you don't want to store the logs, don't pass a-voption.InstallationUsingpip:$pipinstallhyper-inspectorUsingpipx:$pipxinstallhyper-inspectorUsage$ hyper --help
usage: http_inspector [-h] [-r RESPONSE] [-f [ENABLE_FILE_LOGGING]] [-d LOGGING_DIRECTORY] [--log-body [LOG_BODY]] [--ip IP] [--port PORT]

Inspect and debug HTTP requests

optional arguments:
  -h, --help
            Show this help message and exit
  -r, --response [default 200]
            Default response for every incoming request
  -f, --enable-file-logging [default True]
            Enable file logging
  -d, --logging-directory [default .]
            Directory path to store logs
  --log-body [default True]
            Should it log the whole body to the console.
  --ip [default '']
            IP Addr to serve
  --port [default 555]
            Server Port to listen to
Package: hyperion-cli
HyperionHyperion is a CLI tool and rest api that facilitates searching for crime data
at specific geographical locations in the UK.Getting StartedSimply pip install the package, and then get started withpip install hyperion-cli
hyperion --helpDataData is aggregated and cached from the following sources:BikeRegisterUK PoliceGoogle mapsTwitter RSSUK PostcodesWikipediaWeb AppAweb appfor interacting
with this API is available as well.
Package: hyperion-logs
===========Hyperion V0.0.3===========Hyperion provides an intuitive and efficient way to store logs from python into AWS CloudWatch.Typical Usage:#import the packageimport hyperion#This will return the HyperionLogger Object which included additional built in functions.log = hyperion.get_logger()#add the handler with a unique stream name and log group name.#Note: You can do this anywhere in your code so if the stream_name has to be generated in code.#Bigger NOTE: If you do not add a handler it will not log to cloud watch!log.add_handler(stream_name,log_group)#There is also a set_level function to set the level for the logger.log.set_level(debug,info,warning, etc.)#In order to start logging do the following:log.info('message')log.debug('message')log.error(dict(foo='bar',details={'user_name':'user123','password':'password1'}))
Package: hyper-jax
hyper-jaxHyperdimensional computingwith Jax. This library provides a very minimal implementation of MAP (Multiply, add, permute) operations over bipolar vectors originally proposedhere.InstallComing soon...How to useGenerate 2 random vectors with dimension of 10 000from generator import random_vectors

dimensions = 10000
count = 2

key = random.PRNGKey(0)
vectors = random_vectors(key, dimensions, count)Bundle two hypervectorsfrom operation import bundle, unbundle

hypervector = bundle(vectors[0], vectors[1])Unbundle two hypervectors# original_vector == vectors[0]
original_vector = unbundle(hypervector, vectors[1])Bind two hypervectorsfrom operation import bind, unbind

bound_vector = bind(vectors[0], vectors[1])Unbind the hypervector# original_vector == vectors[1]
unbound_vector = unbind(bound_vector, vectors[0])
Package: hyperjet
HyperJet — Algorithmic Differentiation with Hyper-Dual numbers for Python and C++A header-only library for algorithmic differentiation with hyper-dual numbers. Written in C++17 with an extensive Python interface.Installationpip install hyperjetQuickstartImport the module:importhyperjetashjCreate a set of variables e.g.x=3andy=6:x,y=hj.variables([3,6])xandyare hyper-dual numbers. This is indicated by the postfixhj:x>>>3hjGet the value as a simplefloat:x.f>>>3The hyper-dual number stores the derivatives as a numpy array.Get the first order derivatives (Gradient) of a hyper-dual number:x.g# = [dx/dx, dx/dy]>>>array([1.,0.])Get the second order derivatives (Hessian matrix):x.hm()# = [[d^2 x/ dx**2 , d^2 x/(dx*dy)],#    [d^2 x/(dx*dy), d^2 x/ dy**2 ]]>>>array([[0.,0.],[0.,0.]])For a simple variable these derivatives are trivial.Now do some computations:f=(x*y)/(x-y)f>>>-6hjThe result is again a hyper-dual number.Get the first order derivatives offwith respect toxandy:f.g# = [df/dx, df/dy]>>>array([-4.,1.])Get the second order derivatives off:f.hm()# = [[d^2 f/ dx**2 , d^2 f/(dx*dy)],#    [d^2 f/(dx*dy), d^2 f/ dy**2 ]]>>>array([[-2.66666667,1.33333333],[1.33333333,-0.66666667]])You can use numpy to perform vector and matrix operations.Compute the nomalized cross product of two vectorsu = [1, 2, 2]andv = [4, 1, -1]with hyper-dual numbers:importnumpyasnpvariables=hj.DDScalar.variables([1,2,2,4,1,-1])u=np.array(variables[:3])# = [1hj, 2hj,  2hj]v=np.array(variables[3:])# = [4hj, 1hj, -1hj]normal=np.cross(u,v)normal/=np.linalg.norm(normal)normal>>>array([-0.331042hj,0.744845hj,-0.579324hj],dtype=object)The result is a three-dimensional numpy array containing hyper-dual numbers.Get the value and derivatives of the x-component:normal[0].f>>>-0.3310423554409472normal[0].g>>>array([0.00453483,-0.01020336,0.00793595,0.07255723,-0.16325376,0.12697515])normal[0].hm()>>>array([[0.00434846,-0.01091775,0.00647611,-0.0029818,-0.01143025,-0.02335746],[-0.01091775,0.02711578,-0.01655522,0.00444165,0.03081974,0.04858632],[0.00647611,-0.01655522,0.0093492,-0.00295074,-0.02510461,-0.03690759],[-0.0029818,0.00444165,-0.00295074,-0.02956956,0.03025289,-0.01546811],[-0.01143025,0.03081974,-0.02510461,0.03025289,0.01355789,-0.02868433],[-0.02335746,0.04858632,-0.03690759,-0.01546811,-0.02868433,0.03641839]])ReferenceIf you use HyperJet, please refer to the official GitHub repository:@misc{HyperJet,author="Thomas Oberbichler",title="HyperJet",howpublished="\url{http://github.com/oberbichler/HyperJet}",}
Package: hyperjson5
hyperjson5A hyper-fast, safe Python module to read and write JSON data. Works as a
drop-in replacement for Python's built-injsonmodule.
This is alpha software and there will be bugs, so maybe don't deploy to productionjustyet. :wink:Installationpip install hyperjson5Usagehyperjson5 is meant as a drop-in replacement for Python'sjson
module:>>>importhyperjson5>>>hyperjson5.dumps([{"key":"value"},81,True])'[{"key":"value"},81,true]'>>>hyperjson5.loads("""[{key: "value"}, 81, true]""")[{u'key':u'value'},81,True]Contributions welcome!Licensehyperjson5 is licensed under either ofApache License, Version 2.0, (LICENSE-APACHE orhttp://www.apache.org/licenses/LICENSE-2.0)MIT license (LICENSE-MIT orhttp://opensource.org/licenses/MIT)at your option.ContributionUnless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in hyperjson by you, as defined in the Apache-2.0 license, shall
be dual licensed as above, without any additional terms or conditions.
Package: hyperkuutio
HyperkuutioHypercube (Finnish: hyperkuutio) tool that scratches some itch.License: MIT|Country of origin: Switzerland|Export control classification number(ECCN): EAR99Third party dependencies are documented in the folderthird-party.DocumentationUser and developerdocumentation of hyperkuutio.Bug TrackerAny feature requests or bug reports shall go to thetodos of hyperkuutio.Primary Source repositoryThe main source ofhyperkuutiois on a mountain in central Switzerland.
We use distributed version control (git).
There is no central hub.
Every clone can become a new source for the benefit of all.
The preferred public clones ofhyperkuutioare:on codeberg- a democratic community-driven, non-profit software development platform operated by Codeberg e.V.at sourcehut- a collection of tools useful for software development.ContributionsPlease do not submit "pull requests" (I found no way to disable that "feature" on GitHub).
If you like to share small changes under the repositories license please kindly do so by sending a patchset.
You can either send such a patchset per email usinggit send-emailor
if you are a sourcehut user by selecting "Prepare a patchset" on the summary page of your fork atsourcehut.SupportPlease kindly submit issues athttps://todo.sr.ht/~sthagen/hyperkuutioor write plain text email to ~sthagen/hyperkuutio@lists.sr.htto submit patches and request support. Thanks.StatusExperimental.Note: The default branch isdefault.
Package: hyperkv
hyperkvGet Hyper-V Key Value Pairs on Linux GuestSimple parser of the files made byhv_kvp_daemonon a Linux guest. This allows a guest to determine its name, the host's name, etc.Learn more about Hyper-V Data Exchange here.InstallpipinstallhyperkvUsagehyperkvwill write the contents as a JSON to standard out like this:{"HostName":"thehost.example.com","HostingSystemEditionId":"8","HostingSystemNestedLevel":"0","HostingSystemOsMajor":"10","HostingSystemOsMinor":"0","HostingSystemProcessorArchitecture":"9","HostingSystemProcessorIdleStateMax":"0","HostingSystemProcessorThrottleMax":"100","HostingSystemProcessorThrottleMin":"5","HostingSystemSpMajor":"0","HostingSystemSpMinor":"0","PhysicalHostName":"THEHOST","PhysicalHostNameFullyQualified":"thehost.example.com","VirtualMachineDynamicMemoryBalancingEnabled":"0","VirtualMachineId":"461B2364-3901-4349-B3C2-FA4821CBEEFE","VirtualMachineName":"my-guest-vm"}By default, this script reads from/var/lib/hyperv/.kvp_pool_3, but you can specify a different path with-fhyperkv-f/var/lib/hyperv/.kvp_pool_2Requirementshv_kvp_daemonmust be installed and working. If it is working, you will find files in/var/lib/hyperv.Ubuntuapt-getinstalllinux-azurelinux-azure-tools
grub-updateAt boot time, you need to pick advanced startup and make sure it boots with the newly installed Azure kernel. To make that more permanent, you can open/etc/default/grub, add the lineGRUB_SAVEDEFAULT=true, and changeGRUB_DEFAULT=0toGRUB_DEFAULT=saved. The next time you boot, choose the Azure kernel and that will become the default until you pick a different one at the GRUB menu.
Package: hyperledger
# hyperledger-py[![Build Status](https://travis-ci.org/yeasy/hyperledger-py.svg?branch=master)](https://travis-ci.org/yeasy/hyperledger-py)
[![PyPI Version](http://img.shields.io/pypi/v/hyperledger.svg)](https://pypi.python.org/pypi/hyperledger)Python client for [Hyperledger](https://github.com/hyperledger/hyperledger).This project will follow the [Hyperledger API](https://github.com/hyperledger/fabric/tree/master/docs/API) to let users use those APIs by importing a simple python library. e.g.,`python >>> from hyperledger.client import Client >>> c =Client(base_url="http://127.0.0.1:7050")>>> c.peer_list() {u'peers': [{u'type': 1, u'ID': {u'name':u'vp1'},u'address':u'172.17.0.2:7051'},{u'type': 1, u'ID': {u'name':u'vp2'},u'address':u'172.17.0.3:7051'}]}`If you want a quick start with a hyperledger cluster without any local
configuration and vagrant setup, please use this
[compose-file](https://github.com/yeasy/docker-compose-files#hyperledger).## Installation
The latest stable version is always available on PyPi.`sh $ pip install hyperledger--upgrade`The latestdevversion is on [github](https://github.com/yeasy/hyperledger-py).`sh $ git clonehttps://github.com/yeasy/hyperledger-py.git$ cdhyperledger-py$ pip install-rrequirements.txt $ python setup.py install `## Change Logs
See [change log](docs/change_log.md).## Documentation
The source is available in the [docs](docs) directory.[API Usage](docs/api.md)## Testing
All testing code is under [tests](tests) directory.After installation, you can run a quick full-functional testing (deploy,
invoke, query chaincode, etc.) with your hyperledger cluster by`python $ python tests/function_test.py HYPERLEDGER_API_URL `## Contribution
Please see [Contribution](CONTRIBUTION.md) and [Contribution
Instruction](docs/contribution.md).## AcknowledgementThis work is highly inspired by the following projects:[Hyperledger](https://github.com/hyperledger/hyperledger)[requests](https://pypi.python.org/pypi/requests)[docker-py](https://github.com/docker/docker-py)## LicenseThis work is licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for full license text.
Package: hyperlink-preview
hyperlink_previewPurposeHyperlink_preview allows getting data needed to display a small visual preview of a http link.It searches deeper than only<meta property="og:tags. It also parses other tags in <head> or the html <body> if needed to have all data needed for the preview.It also have an "asynchronous" data getter to avoid having to wait for the full analysis of the images (see below).DemoA small demo is provided.Create an venv, install the package, and test (above code is for windows):py -3 -m venv venv
venv\Scripts\activate.bat
pip install hyperlink_preview
python -m hyperlink_preview.demo_html https://en.wikipedia.org/wiki/Your_NameIt will:get the data to build the preview:Data for preview:
    title: Your Name - Wikipedia
    type: website
    image: https://upload.wikimedia.org/wikipedia/en/0/0b/Your_Name_poster.png
    url: https://en.wikipedia.org/wiki/Your_Name
    description: Your Name (Japanese: 君の名は。,...
    site_name: en.wikipedia
    domain: en.wikipedia.organd open your web browser with an exemple of a preview:Installpip install hyperlink_previewUsageimporthyperlink_previewasHLPhlp=HLP.HyperLinkPreview(url="https://en.wikipedia.org/wiki/Your_Name")ifhlp.is_valid:preview_data=hlp.get_data()# Return a dict with keys: ['title', 'type', 'image', 'url', 'description', 'site_name']# Values are None or the value for building a preview.DetailsHyperLinkPreview searches forog tags.If the target link does not provide them (or not all), HyperLinkPreview searches deeper to find suitable data.About images and performanceIf no image is provided, we search for all img tags in the html. TodayGIF, PNG and JPG image formats are handled.We take the sizes of all those images, and we give preference to the largest, and whose ratio is <3 and whose sides are > 50px.For the sake of efficiency:read only bytes necessaryto know the dimensions of the images (not the whole image)parallelized requeststo all the imagesHowever, if the target link contains a lot of pictures, it can take a while (one to several seconds) to do all the requests. A hyperlink preview may need to be displayed quickly (for instance: on mouse hover). In that case:Get all data except image first, then imageimporthyperlink_previewasHLPhlp=HLP.HyperLinkPreview(url="https://en.wikipedia.org/wiki/Your_Name")ifhlp.is_valid:preview_data=hlp.get_data(wait_for_imgs=False)# returns as soon as the data are fetched, but don't wait to "parse" all images tags if needed.# it allows you to display a spinner as link preview image (or anything else to keep your user waiting).# ... later you can get the remaining image data if needed:ifpreview_data["image"]isNone:preview_data=hlp.get_data(wait_for_imgs=True)
Package: hyperlm
Hyper label modelA hyper label model to aggregate weak labels from multiple weak supervision sources to infer the ground-truth labels in a single forward pass.For more details, see our paperLearning Hyper Label Model for Programmatic Weak Supervision** To reproduce experiments of our paper or to re-train the model from scratch, please switch to the paper_experiments branch.How to useInstall the packagepip install hyperlmImport and create an instancefromhyperlmimportHyperLabelModelhlm=HyperLabelModel()Unsupervised label aggregation. Given an weak label matrixX, e.g.X=[[0, 0, 1], [1, 1, 1], [-1, 1, 0], [0, 1, 0]], you can infer the labels by:pred=hlm.infer(X)Note inX,-1represents abstention,0and1represent classes. Each row ofXincludes the weak labels for a data point, and each column ofXincludes the weak labels from a labeling function (LF).Semi-supervised label aggregation. Let's say the gt labels are provided for the examples at index 1 and 3, i.e.y_indices=[1,3], and the gt labels arey_vals=[1, 0]. We can incorporate the provided partial ground-truth with:pred=hlm.infer(X,y_indices=y_indices,y_vals=y_vals)
Package: hyperloglog
Python implementation of the Hyper LogLog and Sliding Hyper LogLog cardinality counter
algorithms. Added bias correction from HLL++.http://algo.inria.fr/flajolet/Publications/FlFuGaMe07.pdfhttp://hal.archives-ouvertes.fr/docs/00/46/53/13/PDF/sliding_HyperLogLog.pdfhttp://research.google.com/pubs/pub40671.html
Package: hyperloglogs
Allows you to take intersection of arbitrarily many hyperloglogs.hll = HyperLogLog(0.01) # 0.01 is % accuracy
hll.add(1) # can add objects of any type
hll2 = HyperLogLog(0.01)
hll3 = HyperLogLog(0.01)hll.intersectoin([hll2, hll3]) # returns cardinality of intersection set
NOTE THAT IN ORDER TO TAKE INTERSECTION ALL SETS MUST BE THE SAME SIZE
or in other words, MUST HAVE THE SAME % ACCURACY
Package: hypermapper
HyperMapperSoftware and InstallationWe recommend installing HyperMapper withpip:pip install hypermapperWe recommend using pip version 18 or higher. Please see the wiki for thequick start guideandalternative installation.AbstractHyperMapper is a multi-objective black-box optimization tool based on Bayesian Optimization.HyperMapper was succesfully applied to real-world problems involving design search spaces with trillions of possible design choices. In particular it was applied to:Computer vision and robotics,Programming language compilers and hardware design,Database management systems (DBMS) parameters configuration.To learn about the core principles of HyperMapper refer to the papers section at the bottom.Contact and InfoFor any questions please contact Luigi Nardi: luigi.nardi at cs.lth.se.HyperMapper Slack ChannelJoin the channel for a quicker communication with the dev team:hypermapper.slack.comLicenseHyperMapper is distributed under the MIT license. More information on the license can be foundhere.PeopleMain ContributorsArtur Souza, Ph.D. student, Federal University of Minas Gerais
Leonard Papenmeier, Ph.D. student, Lund University 
Carl Hvarfner, Ph.D. student, Lund University
Erik Hellsten, Postdoc, Lund University
Luigi Nardi, Assistant Professor, Lund University, and Researcher, Stanford UniversityOther ContributorsBruno Bodin, Assistant Professor (National University of Singapore) 
Samuel Lundberg (Lund University)
Alfonso White (Imperial College London)
Adel Ejjeh, Ph.D. Student (University of Illinois at Urbana-Champaign)
Matthias Mayr, Ph.D. Student (Lund University)PapersIf you use HyperMapper in scientific publications, we would appreciate citations to the following paper:Nardi, Luigi, David Koeplinger, and Kunle Olukotun. "Practical Design Space Exploration", IEEE MASCOTS, 2019.For the list of all publications (including bibtex) related to HyperMapper and its applications, see ourPublicationspage.
Package: hypermat
HyperMATهايبرماتHyperelastic formulations using an algorithmic differentiation with hyper-dual numbers in Python.HyperMAT is based on the definitions of the first Piola-Kirchhoff stress $\large P$ and the tangent modulus $\large \hat{A}$ given below:$\large P = \frac{\partial{W}}{\partial{F}}$$\large \hat{A} = \frac{\partial^2{W}}{\partial{F}^2}$How to useThis is a quick example of how to use:importnumpyasnpimporthypermatashm# Initialise materialumat=hm.NeoHooke(C10=0.5,K=0)# Initialise deformation gradient valuesnp.random.seed(125161)F=(np.eye(3)+np.random.rand(50,8,3,3)/10).T# Get stress tensorP=umat.jacobian(F)# Get tangent modulus tensorA=umat.hessian(F)Sometimes a lucky engineer will have some tension or compression stress-strain test data, or simple shear test data. Processing and applying these data is a critical step to analyze the hyperelastic models. HyperMAT has a calibration module that can help to get the best fitted model parameters. Let's take a look on how are things going on:importosfromhypermatimportNeoHooke,Yeoh,read_file,to_dict,Uniaxial#Prepare material modelsumat1=NeoHooke(C10=1.5,K=2000)umat2=Yeoh(C10=0.5,C20=-0.01,C30=0.2,K=2000)#Prepare experimental datadir_path=os.path.dirname(os.path.realpath(__file__))dataset=read_file(dir_path+'//_data//_data_2.csv',delimiter=',',dtype=float)data=to_dict(dataset[1:,:],['time','strain','stress'])strain=data['strain']stress=data['stress']#Choose loading type (Uniaxial, Biaxial or Shear)test1=Uniaxial(umat1,data)test2=Uniaxial(umat2,data)#Plot experimental datatest1.plot()#Fit parameterstest1.fit_data([0,0],[20,2000],[True,False])test2.fit_data([0,-20,-20,0],[20,20,20,2000],[True,True,True,False])#Plot resultstest1.plot_model(c='r')test2.plot_model(c='g')You should get something like that:HyperMAT fitted parameters
{'C10': 0.6624343754510106}
{'C10': 0.5903745146776757, 'C20': -0.09056730756209555, 'C30': 0.3065185192428228}
MCalibration fitted parameters
{'C10': 0.623489155}
{'C10': 0.585555703, 'C20': -0.0846386036, 'C30': 0.304613717}A special thank you goes toDutzler, for providing us many powerful tools such asTensorTraxandhyperelastic.LicenseHyperMAT- Hyperelastic formulations using an algorithmic differentiation with hyper-dual numbers in Python, (C) 2023 Mohamed ZAARAOUI, Tunisia.This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, seehttps://www.gnu.org/licenses/.