Saravanan666/pypi-org huggingface dataset:
Package: hyp3-sdk
HyP3 SDKA python wrapper around the HyP3 API>>>fromhyp3_sdkimportHyP3>>>hyp3=HyP3(username='MyUsername',password='MyPassword')>>>granule='S1A_IW_SLC__1SSV_20150621T120220_20150621T120232_006471_008934_72D8'>>>job=hyp3.submit_rtc_job(granule=granule,name='MyNewJob')>>>job=hyp3.watch(job)>>>job.download_files()InstallIn order to easily manage dependencies, we recommend using dedicated project environments
viaAnaconda/MinicondaorPython virtual environments.The HyP3 SDK can be installed into a conda environment withconda install -c conda-forge hyp3_sdkor into a virtual environment withpython -m pip install hyp3_sdkQuick UsageThere are 3 main classes that the SDK exposes:HyP3to perform HyP3 operations (find jobs, refresh job information, submitting new jobs)Jobto perform operations on single jobs (downloading products, check status)Batchto perform operations on multiple jobs at once (downloading products, check status)An instance of theHyP3class will be needed to interact with the external HyP3 API.>>>fromhyp3_sdkimportHyP3>>>hyp3=HyP3(username='MyUsername',password='MyPassword')>>>granule='S1A_IW_SLC__1SSV_20150621T120220_20150621T120232_006471_008934_72D8'>>>job=hyp3.submit_rtc_job(granule=granule,name='MyNewJob')>>>job=hyp3.watch(job)>>>job.download_files()Submitting Jobshyp3has member functions for submitting new jobs:rtc_job=hyp3.submit_rtc_job('granule_id','job_name')insar_job=hyp3.submit_insar_job('reference_granule_id','secondary_granule_id','job_name')insar_burst_job=hyp3.submit_insar_isce_burst_job('reference_granule_id','secondary_granule_id','job_name')autorift_job=hyp3.submit_autorift_job('reference_granule_id','secondary_granule_id','job_name')Each of these functions will return an instance of theJobclass that represents a new HyP3 job request.Finding Existing JobsTo find HyP3 jobs that were run previously, you can use thehyp3.find_jobs()batch=hyp3.find_jobs()This will return aBatchinstance representing all jobs owned by you. You can also pass parameters to
query to a specific set of jobsOperations on Job and BatchIf your jobs are not complete you can use the HyP3 instance to update them, and wait from completionbatch=hyp3.find_jobs()ifnotbatch.complete():# to get updated informationbatch=hyp3.refresh(batch)# or to wait until completion and get updated information (which will take a fair bit)batch=hyp3.watch(batch)Once you have complete jobs you can download the products to your machinebatch.download_files()These operations also work onJobobjectsjob=hyp3.submit_rtc_job('S1A_IW_SLC__1SSV_20150621T120220_20150621T120232_006471_008934_72D8','MyJobName')job=hyp3.watch(job)job.download_files()DocumentationFor the full SDK API Reference, seethe HyP3 documentationContact UsWant to talk about the HyP3 SDK? We would love to hear from you!Found a bug? Want to request a feature?Open an issueGeneral questions? Suggestions? Or just want to talk to the team?Chat with us on Gitter
Package: hypar
HyparHypar is a generative design platform for AEC. This package contains modules for use in creating a custom function to execute on the Hypar platform.ModulesglTFThe glTF module contains classes and methods for working withglTFmodels. Hypar stores geometry in glTF format.PrerequisitesFor testing, and deployment you'll need to install a number of packages.python3 -m pip install --user --upgrade setuptools wheel twinePackagingpython3setup.pysdistbdist_wheelUploading to PyPitwineuploaddist/*
Package: hypast
HypAST - Hypothalamus Automatic Segmentation ToolOn this package you will find a trained model for hypothalamus segmentation on T1 MRI images and a trainable class, in case you wish to use your own data.This tool is not suitable for clinical purposes.HypASTrequiresPython 3.7andPyTorch 1.9To install PyTorch:# CUDA 10.2
    conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
    # CUDA 11.3
    install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=11.3 -c pytorch -c conda-forge
    # CPU Only
    conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cpuonly -c pytorchGETTING STARTEDHypASTworks on .nii or .nii.gz input files for images and .nii, .nii.gz or .npy for annotations.
UsingHypASTyou will be able to predict hypothalamus segmentation using our model or to train with your own data.See more bellow:TrainerWithTraineryou can train the model using your own data.Example:import hypast as hyp
    train = hyp.Trainer(train_path, chkp_path, val_path, maxep=200, accum=16, weight=[1,4], lr=5e-3, bs=8)
    train.trainer()Input:train_path: path to h5py train setchkp_path: checkpoint pathval_path: path to h5py val setmaxep: Maximum # of epochs in training (defaul = 200)accum: Batch accumulation (defaul = 16)weight: Cross Entropy Weight (defaul = [1,4])lr: Learning Rate (defaul = 5e-3)bs: Batch Size (defaul = 8)Output:Checkpoint file on defined pathCreateHDF5To facilitate training usingHypAST, CreateHDF5 will adjust your data for you.Example:import hypast as hyp
    hyp.CreateHDF5(list_data, list_labels, out_path)
    create.create_links()Input:list_data: List containing paths of .nii or .nii.gz imageslist_labels: List containing paths of labels (.nii, .nii.gz or .npy)out_path: Path were .hdf5 files will be savedOutput:Return train.hdf5 and val.hdf5 on defined pathPredictorWithHypASTyou can also generate hypothalamus segmentation using our trained model.Example:import hypast as hyp
    pred = hyp.Predictor(list_data, out)
    pred.predictor()Input:list_data: List containing paths of .nii or .nii.gz files to be segmentedout: Path were segmentation will be savedOutput:Segmentation files on defined pathCONTACTFor more information or suggestions, please contactliviamarodrigues@gmail.comSee more onhttps://github.com/MICLab-Unicamp/HypAST
Package: hypatia_engine
Hypatia 0.3.6Make 2D action adventure games. For programmers and nonprogrammers
alike.Create games like*Legend of Zelda: Oracle of Ages* and *Oracle of
Seasons*.The included demo game (demo/game.py) in action:The demo game in action.What makes this project special?Each release tested in FreeBSD, Mac OS X, Linux, and WindowsBuilt and tested in FreeBSD firstA labor of love,permissively (MIT) licensed, meaning
you Hypatia for commercial or non-commercial purposes and not worry
about legalese–it’s really free for any purpose without strings
attached.Hypatia stricly enforces theCODE-OF-CONDUCT.md. We strive for a
safe, healthy social environment for all women, whethercisor trans.Trans women have access to mentorships, funding, team chat, and more!
For more information please see theHypatia Software Organization
website.ResourcesHypatia Software Organization: The
organization which made this fine product.Platform-specific
packagesHypatia Wiki(great
resource for nonprogrammers, too!)The official Hypatia websiteOfficial IRC support chat:#hypatia on Freenode
(webui!)You can contact the author via email:lillian.lynn.lemmer@gmail.com,
[@LilyLemmer](https:/twitter.com/LilyLemmer) on Twitter.To know your way around the project, I strongly recommend reading theCONTRIBUTING.mdfile. It covers everything you
need to know about contributing to Hypatia, as well as navigating the
project.Getting StartedIf you have just one version of Python installed, simply use:./scripts/bootstrapOtherwise, if you want to install for a specific version of Python, use
something like:python3.3scripts/bootstrapIf the bootstrap fails, you can try to install yourself:Install Pygame (platform-specific). Installing Pygame is a different
process on various systems. See theInstalling Pygamesection
below.pip install--user.Checkout the Demo$cddemo$pythongame.pyInstalling PygameYou can skip this section if the bootstrap worked for you.Installing Pygame on various platforms. I assume you have Python
installed and know how to usepip.FreeBSD, DragonflyBSD, PC-BSD, etc.The easiest thing to do is use Python 2.7. You can simply:sudopkginstallpy27-gameOpenBSDsudopkg_addpygameDebian, Ubuntusudoapt-getinstallpython-pygameMac OS XInstall pygame through Homebrew. You may want to install Python through
Homebrew as well.# install homebrewruby-e"$(curl-fsSLhttps://raw.githubusercontent.com/Homebrew/install/master/install)"# install pygame through homebrewbrewinstallhomebrew/python/pygameWindowsFor Python 2.x I recommend usingPygame’s official Windows
installers.If you’re using Python 3.x, I recommend using Christoph Gohlke’sunofficialPygame binaries. Make sure to download thewhlspecific
to your Python version and architecture (win32 vs win_amd64). To
install thewhldo the following in command prompt (in the directory
containing thewhl):pipinstallwheelpipinstallpygame-*.whl
Package: hypatia-py
HypatiaAn Operation and Planning Energy System Modelling FrameworkWhat is itHypatia is an open source modelling framework written in Python that provides
a technology-rich basis for optimizing both the operation and planning mode of
the energy systems in short-term and long-term time horizons. Hypatia is able
to analyze various energy transition scnerios based on different policies such
as coal phase out, carbon taxes, renewable incetives and other national and
international pledges for the possible future energy systems.QuickstartThere are different ways to install hypatia software on your machine. The fastest one is through pip:In case that you are using pip, it is suggested to create a new environment to avoid conflicts of the other packages.
To create a new environment, you should useAnaconda Prompt:condacreate-nhypatiapython=3.8If you create a new environment for hypatia, you need to activate the environment each time you want to use it, by writing
the following line inAnaconda Prompt:condaactivatehypatiaAfter activating the environment, you need to installCVXPY:condainstall-cconda-forgecvxpyThen, you can usepipto install hypatia as follow:pipinstallhypatia-pyMost of the open source solvers that are supported by CVXPY (the optimization library used in Hypatia), will be installed
automatically with the software. For the commercial solvers, you should follow the specific installation methods.
When Hypatia is installed, you can strat to use the embedded examples as a quick start:fromhypatiaimportload_example# Loading the planning exampleplanning=load_example('Planning')# Loading the Operation exampleoperation=load_example('Operation')# See the configuration of systemsprint(planning)print(operation)# see the description of systemsprint(planning.description)print(operation.description)# Running modelsplanning.run(solver='glpk')# Save the resultsplanning.to_csv(path='../save/directory')If you want to see the structure of inputs and how the examples are built, you can download the data files to a specific place in your machine:fromhypatiaimportdownload_example# Downloading the Planning exampledownload_example(example='Planning',destination_path='Where/To/Save')Python module requirementsSome of the key packages that Hypatia relies on are:PandasNumpyPlotlyCvxpy(domain-specific language)Hypatia supports differentOpen SourceandCommercialsolvers like:CBCGLPKOSQPECOSCVXOPTSCSCPLEXGUROBINoteThis project is under active development.LicenseThis work is licensed underApache 2.0
Package: hypatia-test
HypatiaAn Operation and Planning Energy System Modelling FrameworkWhat is itHypatia is an open source modelling framework written in Python that provides
a technology-rich basis for optimizing both the operation and planning mode of
the energy systems in short-term and long-term time horizons. Hypatia is able
to analyze various energy transition scnerios based on different policies such
as coal phase out, carbon taxes, renewable incetives and other national and
international pledges for the possible future energy systems.QuickstartThere are different ways to install hypatia software on your machine. The fastest one is through pip:In case that you are using pip, it is suggested to create a new environment to avoid conflicts of the other packages.
To create a new environment, you should useAnaconda Prompt:condacreate-nhypatiapython=3.8If you create a new environment for hypatia, you need to activate the environment each time you want to use it, by writing
the following line inAnaconda Prompt:condaactivatehypatiaAfter activating the environment, you can usepipto install hypatia as follow:pipinstallhypatiaMost of the open source solvers that are supported by CVXPY (the optimization library used in Hypatia), will be installed
automatically with the software. For the commercial solvers, you should follow the specific installation methods.
When Hypatia is installed, you can strat to use the embedded examples as a quick start:fromhypatiaimportload_example# Loading the planning exampleplanning=load_example('Planning')# Loading the Operation exampleoperation=load_example('Operation')# See the configuration of systemsprint(planning)print(operation)# see the description of systemsprint(planning.description)print(operation.description)# Running modelsplanning.run(solver='glpk')# Save the resultsplanning.to_csv(path='../save/directory')If you want to see the structure of inputs and how the examples are built, you can download the data files to a specific place in your machine:fromhypatiaimportdownload_example# Downloading the Planning exampledownload_example(example='Planning',destination_path='Where/To/Save')Python module requirementsSome of the key packages that Hypatia relies on are:PandasNumpyPlotlyCvxpy(domain-specific language)Hypatia supports differentOpen SourceandCommercialsolvers like:CBCGLPKOSQPECOSCVXOPTSCSCPLEXGUROBINoteThis project is under active development.LicenseThis work is licensed underApache 2.0
Package: hypdb
HypDBThe core HypDB module lives in theHypDB/directory. A web UI demo that demonstrates the capabilities of HypDB lives in thedemo/directory.PyPIOur package is published on PyPIhere.PaperOur paper (published in VLDB 2018 in Rio de Janeiro) can be foundhere.ContributingWe followangular-style commit message guidelines.To write code to solve an issue, branch off from master and name the branch with something unique and descriptive. We may open a PR at any stage of solving an issue, but request for code review when it might be ready to merge back into master. We can close the respective issue once the PR has been merged.
Package: hypeapi
HypeAPI is an unofficial free-to-use open-source python library to work with the API.How to installIf you are using Windows:pip install hypeapiIf you are using Linux:python -m pip install -U hypeapiIf you are using Mac:pip3 install hypeapiFAQHow to use HypeAPI?Here an example:fromhypeapiimportAPIapi=API('your_api_key')# Do the things you want.# You can see more examples at https://github.com/Neruxov/HypeAPI/tree/master/examplesAre the any examples?Yessir! You can look at the examples atExamples.Got any issues or have any suggestions?Please tell us about them atIssues!
Package: hypeban
hypebanA terminal-based program that calculates days left until a given date that parses DateStrings
in multiple UNIX-compliant formats created by BupperNAMEhypeban, hpSYNOPSISPerform calendar date operations using UNIX-formatted date strings.USAGEEnter a date as follows.Example 1:> since 1969-07-20
Date input: 1969-07-20
Output: 19078Example 2:> since 01-31-2000
Date input: 01-31-2000
Output: 7926Example 3:> until 2021-10-31
Date input: 2021-10-31
Output: 3Example 4:> until 10-31-2021
Date input: 10-31-2021
Output: 3LICENSEGNU GENERAL PUBLIC LICENSE v3.0
Package: hypecli
⚙ A lightweight command line interface library for creating cli commands.About|Installation|Usage|Features|Contributors|LicenseAboutNext:InstallationHype CLIis anopen sourceframework use for building command line applications easirerfor cli applications that required different type of commands.It also comes with alot of differentfeaturesthat you may want to check out. Hype CLI was mainlybuilt forAnglo( a modern lightweight web framework for python 3. ). Because of Hype CLI's capabilityit becomes easier to build command-line application.Learn MoreInstallationYou can install the module @PyPI(recommended). or if you want you can install the module straight on the github repo (unrecommended)
    Or, download it atreleasepage. (recommended)$ pip install hypecliInstallation Guideat the Documentation SiteUsageYou can read thedocumentationfor more info andexamplesfor more examples.
Hyper CLI comes with a starter project template as well. After the installation, run$hypercreatedemoprojectOr you can create your own project by looking on this simple example.#: Import the main class of the modulefromhypeimportHype#: create a `app` instance of HyperCLI `:class:`app=Hype()#: Declare a command decorator.@app.command(description="Greet the given user")defgreet(name:str):#: print the name that user define.app.echo("Hello",name)if__name__=="__main__":#: Run the applicationapp.run()# Output:# python test.py greet --name Zenqi# Hello ZenqiFeaturesCheckHype Featuresfrom the documentation site.ContributorsFor contirbuting, seeCONTRIBUTING.mdThanks to these contributors who made the project maintained!ZenqiLicenseHype is license underMIT
Package: hypehd
HYPEHDA package to aid in data exploration for health data.Report Bug·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedInstallationUsageContactAbout The ProjectHYPEHD is a Python package that provides easy to use functions, designed to make the exploration of health data easier for people with various levels of python experience, but primarily for those who are less experienced. This package aims to be a tool for real-world and practical data analysis, assisting in reaching a quicker understanding of various health related data.(back to top)DependenciesPython 3.7 (with the following packages)numpy 1.19.4matplotlib 3.5.3seaborn 0.11.0pandas 1.0.1scipy.stats 1.4.1sklearn.cluster 0.22.1lifelines 0.27.4(back to top)Getting StartedThe source code is currently hosted on GitHub at:https://github.com/Alieyeh/hypehdRunning requires:Python>=3.7InstallationBinary installers for the latest released version are available at the Test Python Package Index (TestPyPI)https://test.pypi.org/project/hypehd/#descriptionpython-mpipinstall--index-urlhttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simplehypehdFor dependancies, you can also download the conda environment from this github and run it by issuing the following command on a terminalgitclonehttps://github.com/Alieyeh/hypehdcdhypehd
condaupdateconda
condacreate-fcodenv.yml
condaactivatehypehd_env(back to top)DocumentationThe official documentation can be found on:https://hypehd.readthedocs.io/en/latest/On the website, users can find examples, API and more detailed information about this package.(back to top)ContactProject Link:https://github.com/Alieyeh/hypehd(back to top)
Package: hype-html
hypeA minimal python dsl for generating html.InstallInstall via pippip install hype-htmlAlternatively, in the spirit of removing dependencies, you could simply copy/paste the hype/element.py file into your project.UsageHype exposes classes for each html tag.from hype import *

doc = Doc(Html('Hello World!'))

print(doc)

# <!DOCTYPE html>
# <div>Hello World!</div>Elements can also be rendered by calling their render method.doc.render() == str(doc)Inner HTMLArguments passed in the element constructor are rendered using the str function and indented (if the element only has one argument it will not be indented.)body = Body(
    H1('Hello World'),
    P('This is a paragraph'),
    'Just a string',
    P('Another paragraph')
)

print(body)

# <body>
#   <h1>Hello World</h1>
#   <p>This is a paragraph</p>Just a string
#   <p>Another paragraph</p>
# </body>Append to an elements' list of inner html elements.body = Body(
    H1('Hello World'),
    P('This is a paragraph'),
    'Just a string'
)

body.append(P('Another paragraph'))

print(body)

# <body>
#   <h1>Hello World</h1>
#   <p>This is a paragraph</p>Just a string
#   <p>Another paragraph</p>
# </body>AttributesAttributes are passed as keyword arguments to the element's constructor.span = Span('span', width='50px')

print(span)

# <span width="50px">span</span>Since some built-in, and possibly custom, attributes conflict with python keywords, keywords will automatically have all leading underscores stripped.span = Span('span', _id='my-span', width='50px')

print(span)

# <span id="my-span" width="50px">span</span>Any remaining underscores will be converted to hyphens.span = Span('span', custom_attrbiute='custom')

print(span)

# <span custom-attribute="custom">span</span>Also add attributes using a method's attrs method add keyword arguments.span = Span('span')
span.attrs(_id='my-span', test='test')

print(span)

# <span id="my-span" test="test">span</span>Custom ElementsIf you to create a custom tag, just subtype the Element class and add a tag.class CustomTag(Element):
    tag = 'custom-tag'

tag = CustomTag()
print(tag)

# <custom-tag></custom-tag>To create a custom self closing tag, subtype the SelfClosingElement class.class CustomTag(SelfClosingElement):
    tag = 'custom-tag'

tag = CustomTag()
print(tag)

# <custom-tag/>IndentsThe indent to be used can be passed as a keyword arg to the Doc constructor.doc = Doc(content, indent=Indent.TAB)It can also be passed as a keyword arg when calling and element.div = Div(H1('Header'))
print(div(indent=Indent.FOUR_SPACES))

# <div>
#    <h1>Header</h1>
# </div>Async ElementsIf your elements content's use coroutines you can use the async elements in hype.asyncio.The API is nearly identical with two main differences. Of course, the render method is a coroutine. Because of they do not render when calling the str function on them You must explicitly call their render method instead.import ayncio

from hype.asyncio import *


div = Div(H1('Header'))

result = asyncio.run(div.render)
print(result)

# <div>
#    <h1>Header</h1>
# </div>
Package: hypeme
hypemehype yourself up!Installationpip install hypemeUsagetired of ur boring old print statement?>>>print('yeah')yeah:(as we like to say, hype yourself up!>>>importhypeme>>>print('how?')HOW?!!!!!!!>>>print('oh wow i am a good and caring person')OHWOWIAMAGOODANDCARINGPERSON!!should work on most stuff!!>>>importnumpyasnp>>>print(np.zeros((3,3)))ARRAY([[0.,0.,0.],[0.,0.,0.],[0.,0.,0.]])!!!!!!!!!>>>print(np.array)<BUILT-INFUNCTIONARRAY>!!!!!!!!!!>>>print([1,2,3,'birds',set('and'),b'bees'])[1,2,3,'BIRDS',{'A','D','N'},B'BEES']!!!!!!!!!if it doesn't it's still nice about itclassNoRepr:def__repr__(self):raiseException('You cant represent me!')>>>print(NoRepr())IFICANTYELLITTHENIWONTPRINTIT!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!>>>hypeme.relax()oksorryijustgetexcitedforyouisallTraceback(mostrecentcalllast):File"<stdin>",line1,in<module>File"/Users/jonny/git/hypeme/hypeme/__init__.py",line34,inrelaxbuiltins.ye_olde_print(_LAST_PRINT)File"<stdin>",line3,in__repr__Exception:Youcantrepresentme!and when you are fully hyped you can go back to the real world>>>hypeme.no_hyping()smellyalater!>>>print('so quiet in here all of a sudden')soquietinhereallofasudden