Saravanan666/pypi-org huggingface dataset:
Package: hippox
Hippox: High-order Polynomial Projection Operators for JAXWhat is Hippox?Hippox provides a simple dataclass for initializing High-order Polynomial Projection Operators (HiPPOs) as parameters in JAX neural network libraries such as Flax and Haiku.ExampleHere is an example of initializing HiPPO parameters inside a Haiku module:importhaikuashkfromhippox.mainimportHippoclassMyHippoModule(hk.Module):def__init__(self,state_size,measure)_hippo=Hippo(state_size=state_size,measure=measure)_hippo()self._lambda_real=hk.get_parameter('lambda_real',shape=[state_size,],init=_hippo.lambda_initializer('real'))self._lambda_imag=hk.get_parameter('lambda_imaginary',shape=[state_size,],init=_hippo.lambda_initializer('imaginary'))self._state_matrix=self._lambda_real+1j*self._lambda_imagself._input_matrix=hk.get_parameter('input_matrix',shape=[state_size,1],init=_hippo.b_initializer())def__call__(input,prev_state):new_state=self._state_matrix@prev_state+self._input_matrix@inputreturnnew_stateIf using a library (such as Equinox) which does not require an initializer function but simply takes JAX ndarrays for parameterization, then you can call the HiPPO matrices directly as a property of the base class after it has been called:importequinoxaseqxfromhippox.mainimportHippoclassMyHippoModule(eqx.Module):A:jnp.ndarrayB:jnp.ndarraydef__init__(self,state_size,measure)_hippo=Hippo(state_size=state_size,measure=measure)_hippo_params=_hippo()self.A=_hippo_params.state_matrixself.B=_hippo_params.input_matrixdef__call__(input,prev_state):new_state=self.A@prev_state+self.B@inputreturnnew_stateInstallationhippox can be easily installed through PyPi:pip install hippoxReferencesRepositorieshttps://github.com/HazyResearch/state-spaces- Original paper implementations in PyTorchhttps://github.com/srush/annotated-s4- JAX implementation of S4 models (S4, S4D, DSS)PapersHiPPO: Recurrent Memory with Optimal Polynomial Projections:https://arxiv.org/abs/2008.07669- Original paper which introduced HiPPOsEfficiently Modeling Long Sequences with Structured State Spaces:https://arxiv.org/abs/2111.00396- S4 paper, introduces normal/diagonal plus low rank decompositionHow to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections:https://arxiv.org/abs/2206.12037- Generalizes and explains the core principals behind HiPPOOn the Parameterization and Initialization of Diagonal State Space Models:https://arxiv.org/abs/2206.11893- S4D paper, details and explains the diagonal only parameterization
Package: hipppy
#HIPPYA Python interface to Hip which is a package for manipulating
unstructured computational grids and their associated datasets.RequirementsHippy comes with a minimal requirement for Python version that should be at least3.6InstallationHippy can be installed directly in a Python 3 environnement frompipby running the command:#ssh modepipinstallgit+ssh://git@nitrox.cerfacs.fr:open-source/hip.git#subdirectory=hippy# or https modepipinstallgit+https://nitrox.cerfacs.fr/open-source/hip.git#subdirectory=hippyThis command will install Hippy and all its dependencies (if needed). Once this step is over you can check the installation by executing the following command:python3-c"import hippy"UsagePlease refer to the Hippy project documentation for further details.#CreditsAuthor:Jens-Dominik Muellerj.mueller@qmul.ac.ukcontributor(s) :Gabriel Staffelbachgabriel.staffelbach@cerfacs.frAimad Er-raiyerraiya@cerfacs.fr#LICENCE
This software is governed by the CeCILL-B license under French law and
abiding by the rules of distribution of free software. You can use,
modify and/ or redistribute the software under the terms of the CeCILL-B
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info".As a counterpart to the access to the source code and rights to copy,
modify and redistribute granted by the license, users are provided only
with a limited warranty and the software's author, the holder of the
economic rights, and the successive licensors have only limited
liability. The users must also reference the original authors on each
use/communicaiton/publication.In this respect, the user's attention is drawn to the risks associated
with loading, using, modifying and/or developing or reproducing the
software by the user in light of its specific status of free software,
that may mean that it is complicated to manipulate, and that also
therefore means that it is reserved for developers and experienced
professionals having in-depth computer knowledge. Users are therefore
encouraged to load and test the software's suitability as regards their
requirements in conditions enabling the security of their systems and/or
data to be ensured and, more generally, to use and operate it in the
same conditions as regards security.The fact that you are presently reading this means that you have had
knowledge of the CeCILL-B license and that you accept its terms.
Package: hippunfold
This tool aims to automatically model the topological folding structure of the human hippocampus. It is currently set up to use sub-millimetric T2w MRI data, but may be adapted for other data types. This can then be used to apply the hippocampal unfolding methods presented inDeKraker et al., 2019, and ex-vivo subfield boundaries can be topologically applied fromDeKraker et al., 2020.The overall workflow can be summarized in the following steps:Resampling to a 0.3mm isotropic, coronal oblique, cropped hippocampal blockAutomatic segmentation of hippocampal tissues and surrounding structures via deep convolutional neural network U-netLi _et al_., 2017_OR_ Manual segmentation of hippocampal tissues and surrounding structures usingthisprotocolPost-processing via fluid label-label registration to a high resolution, topoligically correct averaged templateImposing of coordinates across the anterior-posterior, proximal-distal, and laminar dimensions of hippocampal grey matter via solving the Laplace equationExtraction of a grey matter mid-surface and morpholigical features (thickness, curvature, gyrification index, and, if available, quantitative MRI values sampled along the mid-surface for reduced partial-voluming)Quality assurance via inspection of Laplace gradients, grey matter mid-surface, and flatmapped featuresApplication of subfield boundaries according to predifined topological coordinates
Package: hippunfold-toolbox
Useful Matlab & Python tools for plotting and manipulating outputs from [HippUnfold](https://github.com/khanlab/hippunfold).Example (Matlab):`plot_subject_foldunfold(<HippUnfold_dir>,<subjectID>,'subfields');`![generated plot!](docs/images/subfields_foldunfold.png)## InstallationMatlab:` git clonehttps://github.com/jordandekraker/hippunfold_toolbox.gitaddpath(genpath('hippunfold_toolbox_Matlab'))`Python:` git clonehttps://github.com/jordandekraker/hippunfold_toolbox.gitcd hippunfold_toolbox python setup.py install `
Package: hippybase
HippyBaseHippyBase is a Python library to interact with Apache Hbase through its REST api.InstallationpipinstallhippybaseExampleimporthippybaseconnection=hippybase.Connection('hostname')table=connection.table('table_name')table.put('row_key',{'family:qual':'value'})row=table.row('row_key')forkey,dataintable.scan():print(key,data)
Package: hipscat
HiPSCatHierarchical Progressive Survey CatalogA HiPSCat catalog is a partitioning of objects on a sphere. Its purpose is for
storing data from large astronomy surveys, but could probably be used for other
use cases where you have large data with some spherical properties.Check out ourReadTheDocs sitefor more information on partitioning, installation, and contributing.See related projects:HiPSCat Import (on GitHub)
(on ReadTheDocs)LSDB (on GitHub)
(on ReadTheDocs)ContributingSee thecontribution guidefor completed installation instructions and contribution best practices.AcknowledgementsLINCC Frameworks is supported by Schmidt Futures, a philanthropic initiative
founded by Eric and Wendy Schmidt, as part of the Virtual Institute of
Astrophysics (VIA).
Package: hipscat-import
hipscat-importHiPSCat import - Utility for ingesting large survey data into HiPSCat structure.Check out ourReadTheDocs sitefor more information on partitioning, installation, and contributing.See related projects:HiPSCat (on GitHub)
(on ReadTheDocs)LSDB (on GitHub)
(on ReadTheDocs)ContributingSee thecontribution guidefor complete installation instructions and contribution best practices.AcknowledgementsLINCC Frameworks is supported by Schmidt Futures, a philanthropic initiative
founded by Eric and Wendy Schmidt, as part of the Virtual Institute of
Astrophysics (VIA).
Package: hips-hack
H.I.P.SHIPS or Hidden In Plain Sight is a command line utility that takes in a string and the pathway to a image in order to hide it inside the image bit by bit hidden in random pixels
It is also a web application linked bellowWeb App (using Flask)check out the web app herenojjktotvrgotyomnz.techthe website name is above, try cracking the cypher to see what it means (not including .tech)InstallHIPS is both a web application as well as a pip install terminal packageinstall using pippip3 install hips-hackthis will download the hips utility to your local machine
seehow to usefor the syntax and parameters for the terminal utilitydownload from githubif any of the other methods have gone down for some reason then you can always click this link to install the python packagedownload hereHow to Usesyntax(-e )encrypts the message to an image(-d )decrypts a message from image( -t )denotes the start of the string being encrypted< image path > is the local path to the image you are trying to encrypt or decryptto encryptpython3 -m hips_hack.stenograpghy -e <image path> -t <string to encode>to decryptpython3 -m hips_hack.stenograpghy -d <image path>the new image with the encryption will be placedHow it WorksTODO WRITE THIS PARTDevelopment TeamDev Team Name - SWMGJan Arvik - Team Lead / Website LeadTruman DeWelch- Command line/ PIP developerMichael Peters - Flask / AWS developerJack Savage - CSS / Typescript Developer
Package: hipsnp
hipsnp<<< This is a development version, there is no any warranty that it works >>>functions to handle SNP data, especially from the UKB.>>> import hipsnp
>>> genotypes = hipsnp.vcf2genotype('snp_epilepsy.vcf')
>>> genotypes
                         4303212 3351913 2982758  ... 1709854 5348682 5862730
rs2535288,6:31064007_C_A      CA      CA      CA  ...      CA      CC      CA
rs2858870,6:32572251_T_C      TT      TT      TT  ...      TT      TT      TT

[2 rows x 487409 columns]resourcesSNP databaseshttps://www.ncbi.nlm.nih.gov/snp/http://www.ensembl.org/Homo_sapienshttps://varsome.comToolshttps://www.well.ox.ac.uk/~gav/qctool/Infohttps://eu.idtdna.com/pages/education/decoded/article/genotyping-terms-to-knowhttps://faculty.washington.edu/browning/intro-to-vcf.htmlhttps://www.reneshbedre.com/blog/vcf-fields.htmlhttps://www.garvan.org.au/research/kinghorn-centre-for-clinical-genomics/learn-about-genomics/for-gp/genetics-refresher-1/types-of-variants
Package: hipsofcobra
HIPSOFCOBRAA python package to compute the HIggs Portal Scalar OFf-diagonal COupling Branching RAtios.DocumentationA brief user manual can be found in the paper cited below. Additionally, the Jupyter notesbook
hipsofcobra/demos.ipynb gives a basic explanation of the core usage.CitationIf you use hipsofcobra in a publication, please reference the following citation:[Publication pending]Bugs and feature requestsPlease submit bugs and feature requests usingGithub's issue system.ContributorsMaintainer:Patrick Blackstone (@blackstonep)Contributors (in alphabetical order):Patrick BlackstoneJaume Tarrús CastellàEmilie PassemarJure ZupanLicensehipsofcobra is released under the GNU General Public License.
Package: hipster
Hipster [Heap*]DescriptionHipster provides a thread-safe implementation of the MinHeap and MaxHeap. Each collection type exposes a simple API topush,pop,peek,removeandclear.
The data structure is built on top of theheapqalgorithm.Object UsageFor adding an object to the heap, the object needs to implement it's comparator logic.class GitRepo:
    def __init__(self, url, stars, forks):
        self.url = url
        self.stars = stars
        self.forks = forks

    # The object needs to implement the following methods
    def __eq__(self, other):
        return self.name == other.name and self.stars == other.stars and self.forks == other.forks

    def __ne__(self, other):
        return self.name != other.name or self.stars != other.stars or self.forks != other.forks

    def __lt__(self, other):
        # The two repos are first compared based on stars then forks and then sorted lexically based on url
        if self.stars == other.stars and self.forks == other.forks return self.url < other.url

    def __le__(self, other):
        if self.stars == other.stars and self.forks == other.forks return self.url <= other.url

    def __gt__(self, other):
        if self.stars == other.stars and self.forks == other.forks return self.url > other.url

    def __ge__(self, other):
        if self.stars == other.stars and self.forks == other.forks return self.url >= other.urlDependenciesPython 3Installationpip install --upgrade hipsterUsagefrom hipster.max_heap import MaxHeap
from hipster.min_heap import MinHeap

max_heap = MaxHeap()           # Creates an empty MaxHeap
max_heap.push(item)            # Pushes a new item on the MaxHeap
item = max_heap.peek()         # Returns the largest item from the heap without removing it, throws HeapError when called on an empty Heap
item = max_heap.pop()          # Pops an item off the MaxHeap, throws HeapError when called on an empty Heap
max_heap.remove(item)          # Removes the item from the heap if its present, or throws HeapError if the item is not present
max_heap.clear()               # Removes all items from the heap



min_heap = MinHeap()           # Creates an empty MinHeap
min_heap.push(item)            # Pushes a new item on the MinHeap
item = min_heap.peek()         # Returns the samllest item from the heap without removing it, throws HeapError when called on an empty Heap
item = min_heap.pop()          # Pops an item off the MinHeap, throws HeapError when called on an empty Heap
min_heap.remove(item)          # Removes the item from the heap if its present, or throws HeapError if the item is not present
min_heap.clear()               # Removes all items from the heapLicenseMITChangelog3.0.5Removed copy3.0.4Fixed a bug with the copy method3.0.3Added copy method3.0.1Fixed a bug with thread safety on remove3.0.0Added remove, refactored duplicated code to base class, added more tests, updated README2.0.2fixed README2.0.1Updated object usage example, updated README2.0.0Added thread safety, added more tests
Package: hipy
# hipy[![Travis CI Build Status](https://travis-ci.org/marthjod/hipy.svg?branch=master)](https://travis-ci.org/marthjod/hipy)[![Coverage Status](https://coveralls.io/repos/github/marthjod/hipy/badge.svg?branch=master)](https://coveralls.io/github/marthjod/hipy?branch=master)Convert Ruby output of older Hiera versions to equivalent Python or JSON data structures.## Usage```bashUsage: hipy [OPTIONS]Convert Hiera output to JSON/PythonOptions:--version          Show the version and exit.--json / --python  Format output as JSON/Python (default: JSON)--debug            Show debug output (mainly from parser)--help             Show this message and exit.```## Examples### Standalone CLI script```bash$ echo nil | hipynull$ echo nil | hipy --jsonnull$ echo nil | hipy --pythonNone$ echo nil | hipy --python --debugIn: nil<Node called "nil" matching "nil">Out: None```For more examples (and possible limitations), cf. the test examples.### Library```python>>> from hipy.parser import HieraOutputParser>>> parser = HieraOutputParser(text='nil')>>> parser.get_json()'null'>>> parser.get_python() is NoneTrue>>> parser = HieraOutputParser(text='nil', debug=True)<Node called "nil" matching "nil">```## Tests### tox```pip install toxtox```### manually```pip install -r test-requirements.txtnosetests -c nose.cfg```## Installation### PyPI`pip install hipy`### LocallyRun `python setup.py install`.### [PEX](https://pex.readthedocs.io/)```bashvirtualenv venvsource venv/bin/activatepip install hipy pexpex -r <(pip freeze) -c hipy -o hipy.pexdeactivatemv hipy.pex /tmpcd /tmpecho '{"a"=>6}' | ./hipy.pex --python{u'a': 6}```
Package: hiq-circuit
Huawei HiQ is an open-source software framework for quantum computing. It is based on and compatible withProjectQ.
It aims at providing tools which facilitate inventing, implementing, testing, debugging, and running quantum algorithms using either classical hardware or actual quantum devices.
You can find more about Huawei HiQ athttp://hiq.huaweicloud.comThe Huawei HiQsimulator is a part of Huawei HiQ, includes a single-amplitude simulation engine, a full-amplitude simulation engine and an error-correction circuit simulation engine.Getting startedTo start using Huawei HiQsimulator, simply follow the installation instructions and code examples in theTutorials.
More example codes can be found onGitHub.How to contributeWe welcome contributions.
If you find a bug or want to propose a new idea, open an issue.
If you have written some code that should be merged, create a pull request describing your changes and why it should be merged.
If you have any questions, feel free to send an email tohiqinfo@huawei.com.How to citeWhen using Huawei HiQsimulator for research projects, please citeHuawei HiQ team, ‘Huawei HiQ: A High-performance Quantum Computing Simulator and Programming Framework’,http://hiq.huaweicloud.com.LicenseHuawei HiQsimulator is released under the Apache 2 license.
Package: hiq-projectq
ImportantThis package offers the same functionality as the officialProjectQpackage. It is however based on a development version that may contain some changes/bugfixes in order to better
integrate with other packages developped byHuawei-HiQ.Changelogv0.6.4 (2020-06-26)EnhancementsAdded support for parametric (ie. symbolic) argument for some gates (e.g. Rx, Ry, Rz, TimeEvolution, QubitOperator)
See documentation for more information.Update Travis CI configuration to allow building of binaries on all platformsBug FixesFix potential test failures on Travis CI due to reference countingQubitOperator invalid comparison to empty tuplev0.6.3 (2020-06-05)Update to ProjectQ v0.5.1v0.6.2 (2020-04-02)EnhancementsAdded JSONBackendBug FixesFix libs/histogram under Python 2.7v0.6.1 (2020-03-20)EnhancementsMigrated HiQ-ProjectQ to a namespace package for compatibility with other HiQ packages.v0.6.0 (2020-03-17)Update HiQ-ProjectQ with ProjectQ v0.5.0
Package: hirakanadic
hirakanadicInstall$pipinstallhirakanadicUsage$hirakanadicexample/input.txt-oout.txtinput fileコレステロール値
陰のうヘルニア
濾胞性リンパ腫
コリネバクテリウム・ウルセランス感染症resultこれすてろーる,5146,5146,7000,これすてろーる,名詞,普通名詞,一般,*,*,*,コレステロール,コレステロール,*,*,*,*,*
へるにあ,5146,5146,7000,へるにあ,名詞,普通名詞,一般,*,*,*,ヘルニア,ヘルニア,*,*,*,*,*
こりねばくてりうむ,5146,5146,7000,こりねばくてりうむ,名詞,普通名詞,一般,*,*,*,コリネバクテリウム,コリネバクテリウム,*,*,*,*,*
うるせらんす,5146,5146,7000,うるせらんす,名詞,普通名詞,一般,*,*,*,ウルセランス,ウルセランス,*,*,*,*,*
Package: hirakord
Easy to use command-line interface for building Discord bots in PythonNOTE:The helpers in this tool may not prove to be useful for everyone. You are welcome to open a PR if you notice any general improvements, bugs or want to contribute new helpers to this tool.BOT TEMPLATES ARE CURRENTLY WORK IN PROGRESS!!Key FeaturesModern Pythonic InterfaceEasy to usePlanned FeaturesWeb-based DocumentationBot templatesRelease on PyPiHelpersCogSlightly builds ondiscord.ext.commands.Cog.EmbedSlightly different approach todiscord.Embed.LoggerCustom logger class with colour-coded prefixes based on log type.TreeSlightly builds ondiscord.app_commands.CommandTree, adds a custom event for app command errorsUIDoes some of the component set up for you such as optionally assigning custom_ids.Comes packaged with SelectMenus for all supported Channel types as well!LinksMy WebsiteMy Discord Server~ Support Available!InstallingPython3.8 or higher is requiredTo install the library withoutdiscord.py, run the following command# Linux / MacOS$python3-mpipinstall-Ugit+https://github.com/ExHiraku/hirakord# Windows$py-3-mpipinstall-Ugit+https://github.com/ExHiraku/hirakordTo install the library withdiscord.py, run the following command$gitclonehttps://github.com/ExHiraku/hirakord$cdhirakord# Linux / MacOS$python3-mpipinstall-U.[discord]# Windows$py-3-mpipinstall-U.[discord]Quick Example# Installing a helper (Linux / MacOS)$python3-mhirakordhelperaddlogger# Installing a helper (Windows)$py-mhirakordhelperaddlogger
Package: hirefire-resource
HireFire Integration Library for Python ApplicationsThis library integrates Python applications with HireFire's Dyno Managers (Heroku Dyno Autoscalers). Instructions specific to supported web frameworks and worker libraries are provided during the setup process.Supported web frameworks:DjangoFlaskQuartFastAPIStarletteSupported worker libraries:CeleryRQSince 2011, over 1,000 companies have trustedHireFireto autoscale more than 5,000 applications hosted onHeroku, managing over 10,000 web and worker dynos.HireFire is distinguished by its support for both web and worker dynos, extending autoscaling capabilities to Standard-tier dynos. It provides fine-grained control over scaling behavior and improves scaling accuracy by monitoring more reliable metrics at the application level. These metrics include request queue time (web), job queue latency (worker), and job queue size (worker), which contribute to making more effective scaling decisions.For more information, visit ourhome page.DevelopmentRunbin/setupto prepare the environment.Runpoetry shellto boot up the development environment.Seepaver --helpfor common tasks.ReleaseUpdate theversionproperty inpyproject.toml.Ensure thatCHANGELOG.mdis up-to-date.Commit changes withgit commit.Create agit tagmatching the new version (e.g.,v1.0.0).Push the new git tag. Continuous Integration will handle the distribution process.LicenseThis library is licensed under the terms of the MIT license.
Package: hirezapi
A small python wrapper for the Hi-Rez Studios API.Free software: MIT licenseInstallationpip install hirezapiYou can also install the in-development version with:pip install https://github.com/AidanJohnston/hirezapi/archive/main.zipDocumentationhttps://hirezapi.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.1 (2023-02-20)@AidanJohnston source file init@alek-ski Added enpoints and languages.@AidanJohnston Update readme0.0.0 (2023-02-19)First release on PyPI.
Package: hirise-tools
pyriseFree software: ISC licenseDocumentation:https://hirise-tools.readthedocs.io.Featurestheproductsmodule offers full standard path creations for all HiRISE products.thedownloadsmodule offers helpers to download HiRISE full and browse productsfor Mac only, a command-line tool called ‘abrowse’ is installed that downloads and opens annotated browse products in the Preview.appCreditsThis package was created withCookiecutterand the forkedmichaelaye/cookiecutter-pypackage-condaproject template.History0.6.0 (2019-04-11)Rename topyrise0.1.0 (2016-11-07)First release on PyPI.