Saravanan666/pypi-org huggingface dataset:
Package: h0rton
Hierarchical Bayesian inference of the Hubble constant from samples of strong gravitational time delay lenses. This package represents LSST Dark Energy Science Collaboration research in progress. Please contact Ji Won Park (@jiwoncpark) for questions on using the code.InstallationVirtual environments are strongly recommended, to prevent dependencies with conflicting versions. Create a conda virtual environment and activate it:$conda create -n h0rton python=3.6 -y
$conda activate h0rtonYou’ll need a Fortran compiler and Fortran-compiledfastell4py, which you can get on a debian system by running$sudo apt-get install gfortran
$git clone https://github.com/sibirrer/fastell4py.git <desired location>
$cd <desired location>/fastell4py
$python setup.py install --userNow do one of the following.Option 2(a):clone the repo (please do this if you’d like to contribute to the development).$git clone https://github.com/jiwoncpark/h0rton.git
$cd h0rton
$pip install -e . -r requirements.txtOption 2(b):pip install the release version (only recommended if you’re a user).$pip install h0rton(Optional) To run the notebooks, add the Jupyter kernel.$python -m ipykernel install --user --name h0rton --display-name "Python (h0rton)"(Optional) To experiment with H0 inference using the TDLMC data, download all their data first using the provided convenience script:$source h0rton/tdlmc_data/download_tdlmc_data.shHow to trainGenerate the training and validation data, e.g.$python -m baobab.generate h0rton/trainval_data/train_tdlmc_diagonal_config.pyEdit the configuration parametersh0rton/example_user_config.py. Make sure thecfg.datafield agrees with the training data you generated.Run$python -m h0rton.train h0rton/example_user_config.pyYou can visualize the training results by running$tensorboard --logdir runsFeedback and MoreSuggestions are always welcome! If you encounter issues or areas for improvement, please message @jiwoncpark ormake an issue.h0rtonwas used to enter the Time Delay Lens Modeling Challenge:Challenge webpage:https://tdlmc.github.io/Experimental design paper:https://arxiv.org/abs/1801.01506This software was developed within the LSST DESC using LSST DESC resources, and so meets the criteria given in, and is bound by, the LSST DESC Publication Policy for being a “DESC product.” When referring to h0rton, please cite (Park et al 2020) and provide a link tothis repo.
Package: h1
h1A HackerOne API client for Python. The API closely maps to the REST API that HackerOne provides.
Documentation for their API isavailable here.LicenseMITInstallationFor installation via pip:pipinstallh1For development, In the project root run:virtualenvenvsourceenv/bin/activatemakebootstrapThe manual approach should work as well:pythonsetup.pyinstallExamplesInitializing the Client>>>fromh1.clientimportHackerOneClient>>>fromh1.modelsimportReport>>>c=HackerOneClient("YOUR-API-TOKEN-IDENTIFIER","YOUR-API-TOKEN")Getting all reports created in the last dayHackerOneClient.find_resources()allows you to specify a resource to find (onlyReportis
supported for now) and some criteria to filter on. The onlyrequiredfilter isprogram, which
must be set to the target HackerOne program’s name. Any additional filters may be passed as kwargs,
and everything inHackerOne’s filter documentationshould be supported.For example, here’s how we’d get all reports created in the past 24 hours:>>>importdatetimeasdt>>>day_ago=dt.datetime.now()-dt.timedelta(days=1)>>>listing=c.find_resources(Report,program=["test-program"],created_at__gt=day_ago)>>>len(listing)3>>>listing[0].titleu'This is a test report!'Getting all resolved reports in a programSimilarly, if we filter onstatewe can get all theresolvedreports:>>>resolved_listing=c.find_resources(Report,program=["test-program"],state=["resolved"])>>>resolved_listing[0].titleGetting a specific report by IDHackerOneClient.get_resource()allows you to pass a resource type (again, currently justReport,)
and an ID to fetch:>>>report=c.get_resource(Report,110306)>>>report.titleu'Test RCE SQLi'>>>report.stateu'not-applicable'Tallying report counts by userHere’s an example of using the client to figure out who your most prolific reporters are:>>>fromcollectionsimportCounter>>>reporter_count=Counter()>>>all_reports=c.find_resources(Report,program=["test-program"])>>>forreportinall_reports:...reporter_count[report.reporter]+=1...>>>print(reporter_count)Counter({<User-bestreporter>:21,<User-another_reporter>:12,<User-r3p0rt3r>:2,<User-newbie>:1})Running Testsvirtualenvenvsourceenv/bin/activatemakebootstrapmaketest
Package: h1d
HiC1DmetricsThis repository contain code and basic tutorial for "HiC1Dmetrics"Important Update(Feb 2024) Now h1d v0.2.8 support the user-difined juicertools. You can use your own version of juicer tool by the--juicertoolparameterInstallationHiC1Dmetrics was based onpython3and released onPyPI, it could be installed bypipinstallh1dAfter installation, try:$ h1d -V
h1d version 0.1.0DocumentsFor detailed usage, please refer toh1d DocumentsWeb applicationPlease clickhic1d webappCitationWang J, Nakato R. HiC1Dmetrics: framework to extract various one-dimensional features from chromosome structure data.Briefings in Bioinformatics, bbab509,2021.
Package: h1st
Join the Human-First AI revolution“We humans have .. insight that can then be mixed with powerful AI .. to help move society forward. Second, we also have to build trust directly into our technology .. And third, all of the technology we build must be inclusive and respectful to everyone.”— Satya Nadella, Microsoft CEOAs trail-blazers in Industrial AI, our team at Arimo-Panasonic has found Satya Nadella‘s observations to be powerful and prescient. Many hard-won lessons from the field have led us to adopt this approach which we call Human-First AI (H1stAI).Today, we‘re excited to share these ideas and concrete implementation ofH1stAI with you and the open-source data science community!Learn the Key ConceptsHuman-First AI (H1stAI) solves three critical challenges in real-world data science:Industrial AI needs human insight:In so many important applications, there isn‘t enough data for ML. For example, last year‘s product‘s data does not apply to this year‘s new model. Or, equipment not yet shipped obviously have no data history to speak of.H1stcombines human knowledge and any available data to enable intelligent systems, and companies can achieve earlier time-to-market.Data scientists need human tools:Today‘s tools are to compete rather than to collaborate. When multiple data scientists work on the same project, they are effectively competing to see who can build the better model.H1stbreaks a large modeling problem into smaller, easier parts. This allows true collaboration and high productivity, in ways similar to well-established software engineering methodology.AI needs human trust:AI models can't be deployed when they lack user trust. AI increasingly face regulatory challenges.H1stsupports model description and explanation at multiple layers, enabling transparent and trustworthy AI.Get startedH1struns on Python 3.8 or above. Install withpip install --upgrade pip
pip3 install h1stFor Windows, please use 64bit version and installVS Build Toolsbefore installing H1st.Start by reading about ourphilosophyandObject ModelSee theQuick Startfor simple "Hello world" examples of usingH1st rule-based model&H1st ML modeland usingH1st Graph.Read the Documentation, Tutorials, and API DocumentationGo overthe ConceptsFor a simple real-world data science example using H1st Modeler and Model API, take a look atModeler and Model with Iris dataset.H1st Oracle: Combine Encoded Domain Knowledge with Machine Learningin which we used Microsoft Azure Predictive Maintenance dataset to demonstrate the power of the Oracle.To fully understand H1st philosophy and power, check out theUse-case examples.For a deep dive into the components, please refer to our fullAPI Documentation.Join and Learn from Our Open-Source CommunityWe are collaborating with the open-source community. For Arimo-Panasonic, use cases include industrial applications such as Cybersecurity, Predictive Maintenance, Fault Prediction, Home Automation, Avionic & Automotive Experience Management, etc.We'd love to see your use cases and your contributions to open-sourceH1stAI.
Package: h1st-contrib
Join the Human-First AI revolution“We humans have .. insight that can then be mixed with powerful AI .. to help move society forward. Second, we also have to build trust directly into our technology .. And third, all of the technology we build must be inclusive and respectful to everyone.”— Satya Nadella, Microsoft CEOAs trail-blazers in Industrial AI, our team at Arimo-Panasonic has found Satya Nadella‘s observations to be powerful and prescient. Many hard-won lessons from the field have led us to adopt this approach which we call Human-First AI (H1stAI).Today, we‘re excited to share these ideas and concrete implementation ofH1stAI with you and the open-source data science community!Learn the Key ConceptsHuman-First AI (H1stAI) solves three critical challenges in real-world data science:Industrial AI needs human insight:In so many important applications, there isn‘t enough data for ML. For example, last year‘s product‘s data does not apply to this year‘s new model. Or, equipment not yet shipped obviously have no data history to speak of.H1stcombines human knowledge and any available data to enable intelligent systems, and companies can achieve earlier time-to-market.Data scientists need human tools:Today‘s tools are to compete rather than to collaborate. When multiple data scientists work on the same project, they are effectively competing to see who can build the better model.H1stbreaks a large modeling problem into smaller, easier parts. This allows true collaboration and high productivity, in ways similar to well-established software engineering methodology.AI needs human trust:AI models can't be deployed when they lack user trust. AI increasingly face regulatory challenges.H1stsupports model description and explanation at multiple layers, enabling transparent and trustworthy AI.Get startedH1struns on Python 3.8 or above. Install withpip install --upgrade pip
pip3 install h1stFor Windows, please use 64bit version and installVS Build Toolsbefore installing H1st.Start by reading about ourphilosophyandObject ModelSee theQuick Startfor simple "Hello world" examples of usingH1st rule-based model&H1st ML modeland usingH1st Graph.Read the Documentation, Tutorials, and API DocumentationGo overthe ConceptsFor a simple real-world data science example using H1st Modeler and Model API, take a look atModeler and Model with Iris dataset.H1st Oracle: Combine Encoded Domain Knowledge with Machine Learningin which we used Microsoft Azure Predictive Maintenance dataset to demonstrate the power of the Oracle.To fully understand H1st philosophy and power, check out theUse-case examples.For a deep dive into the components, please refer to our fullAPI Documentation.Join and Learn from Our Open-Source CommunityWe are collaborating with the open-source community. For Arimo-Panasonic, use cases include industrial applications such as Cybersecurity, Predictive Maintenance, Fault Prediction, Home Automation, Avionic & Automotive Experience Management, etc.We'd love to see your use cases and your contributions to open-sourceH1stAI.
Package: h2
This repository contains a pure-Python implementation of a HTTP/2 protocol
stack. It’s written from the ground up to be embeddable in whatever program you
choose to use, ensuring that you can speak HTTP/2 regardless of your
programming paradigm.You use it like this:importh2.connectionimporth2.configconfig=h2.config.H2Configuration()conn=h2.connection.H2Connection(config=config)conn.send_headers(stream_id=stream_id,headers=headers)conn.send_data(stream_id,data)socket.sendall(conn.data_to_send())events=conn.receive_data(socket_data)This repository does not provide a parsing layer, a network layer, or any rules
about concurrency. Instead, it’s a purely in-memory solution, defined in terms
of data actions and HTTP/2 frames. This is one building block of a full Python
HTTP implementation.To install it, just run:$python-mpipinstallh2DocumentationDocumentation is available athttps://h2.readthedocs.io.Contributingh2welcomes 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.Licenseh2is made available under the MIT License. For more details, see theLICENSEfile in the repository.Authorsh2was authored by Cory Benfield and is maintained
by the members ofpython-hyper.
Package: h264-to-h265-SpookyVicky
h264 to h265 simple recursive transcoder❤This is simple, you give the path to a parent folder and this will convert all '.mp4 (h264)' videos to '.mp4 (h265)' using only 60% of the original bitrate. If a video has already been converted or has an 'h265' codec it will be ignorednormally h265 allows you to save 40% of the total bitrate maintaining an acceptable quality, although of course, if you want you could specify the bitrate to be used in line 43❤requirements'ffmpeg-python'don't confuse it with python-ffmpegNoteIf you use the CPU to encode, make sure you use libx265 and not hvec_nvenc (nvidia only)VIDEO_CODEC = 'libx265'
Package: h2client
H2ClientThis library is intended to provide improved control when writing an http/2
client without adding a significant amount of boilerplate. In particular this
allows for easy connection management, since when an http/2 connection is opened
and closed will have a very significant impact on performance, and respects
Cache-Control headers (request and response) usingdiskcached, since
respecting these headers is often an insane performance improvement as it will
prevent a large number of requests from requiring any network calls at all.This does not support ETag style validation as it doesn't tend to result in any
significant improvements on REST api's where even the largest responses are
measured in kilobytes since ETags do not prevent network calls.Getting Startedpython-mvenvvenv
.venv/bin/activate# for windows, "venv/Scripts/activate.bat"python-mpipinstall--upgradepip
pipinstallh2client"""Make some requests using the postman-echo service run byhttps://www.postman.com/. h2client is not in any way affiliated with orendorsed by Postman."""importasynciofromh2client.sessionimportSessionimportjsonUSER_AGENT='H2ClientExamples/1 by /u/Tjstretchalot (+https://github.com/tjstretchalot/h2client)'asyncdefmain():client=Session('postman-echo.com',default_headers={'user-agent':USER_AGENT,'accept':'application/json'})asyncwithclient.conn()asconn:responses=awaitasyncio.gather(conn.get('/get',params={'foo1':'bar1'}),conn.post('/post',json={'payload':'asdf'}),conn.delete('/delete',data=b'look some data!',headers={'content-type':'text/plain'}))forresponseinresponses:print(f'{response.method}{response.path}-{response.status_code}{response.status_text}')print('Request Headers:')forkey,valinresponse.request_headers.items():print(f'{key}:{val}')print('Headers:')forkey,valinresponse.headers.items():print(f'{key}:{val}')print()print('Body (interpreted):')print(json.dumps(response.json(),indent=2,sort_keys=True))if__name__=='__main__':loop=asyncio.new_event_loop()asyncio.set_event_loop(loop)loop.run_until_complete(main())# Wait for the `ensure_future`s to finish cleanlypending=asyncio.all_tasks(loop)whilepending:loop.run_until_complete(asyncio.wait(pending,return_when=asyncio.ALL_COMPLETED))pending=asyncio.all_tasks(loop)
Package: h2m
pyh2msimple and flexible html to markdown python converterFree software: MIT licenseUsagesh2m.feed('''<h1>Level One Heading</h1>''')

assert h2m.md() == '''# Level One Heading'''

h2m.feed('''<h2>Level Two Heading</h2>''')

assert h2m.md() == '''## Level Two Heading'''

h2m.feed('''<h3>Level Three Heading</h3>''')

assert h2m.md() == '''### Level Three Heading'''Features2 levels table convertraw text with <b> <strong> <i> <em>CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2020-05-22)WIP: super 2 levels table convertWIP: raw text with <b> <strong> <i> <em>Fix: a lot of html convert bug in feed()0.1.4 (2020-05-20)Fix Table head convert ‘None’.0.1.2 (2020-05-15)only python 30.1.1 (2020-05-15)fix readme0.1.0 (2020-05-07)First release on PyPI.
Package: h2md
h2md本项目目的是将html中正文部分转化为markdown文件，部分参考自html2md，我把实现方式全改了，第一个 Python 项目，希望能给大家带来帮助。安装pipinstall-Uh2md用法使用命令行工具:# 转换一个文件h2mdexamples/hello.html# 从标准输入读取文件进行转换catexamples/hello.html|h2md使用 Python 脚本:importh2mdhtml='''<h1>Header</h1><b><i>Hello</i></b> from <code>h2md</code><pre class="py"><code>print('Hello')</code></pre>'''md=h2md.convert(html)print(md)最时髦的用法：注：使用此方法邮箱可能被保护curl-o-https://mydream.ink/archive/iaas-faas-serverless/|h2md# 标准输出curl-o-https://mydream.ink/archive/iaas-faas-serverless/|h2md>xx.md# 输出到 md 文件curl-o-https://mydream.ink/archive/iaas-faas-serverless/|h2md>>xx.md# 追加到 md 文件
Package: h2o
H2O scales statistics, machine learning and math over BigData. H2O is extensible and users can build blocks using simple math legos in the core. H2O keeps familiar interfaces like python, R, Excel & JSON so that BigData enthusiasts & experts can explore, munge, model and score datasets using a range of simple to advanced algorithms. Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. H2O has a vision of online scoring and modeling in a single platform.
Package: h2o4gpu
H2O4GPUis a collection of GPU solvers byH2Oaiwith APIs in Python and R. The Python API builds upon the easy-to-usescikit-learnAPI and its well-tested CPU-based algorithms.  It can be used as a drop-in replacement for scikit-learn (i.e.import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms.  H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option.  The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling.
Package: h2o-autodoc
H2O Automated Model Documentation (AutoDoc) is a Python package that automatically creates model documentation for supervised learning models created in H2O-3 and Scikit-Learn. Automated documentation is used in production in H2O Driverless AI. This industry-leading capability is now available as a new commercial module.Key CapabilitiesDistributed Automatic document generation in Microsoft Word (docx) or Markup (.md) formats.Out-of-the-box documentation template includedTemplate customization available to fit with your organization’s standards and requirementsSupport for models generated in H2O-3 and Scikit-LearnSupport for H2O-3: Deep Learning, Random Forest, GLM, Gradient Boosted Machines, Stacked Ensembles, and XGBoost modelsPlease send feedback to help improve the client and documentation tosales@h2o.ai.
Package: h2o-client
H2O scales statistics, machine learning and math over BigData. H2O is extensible and users can build blocks using simple math legos in the core. H2O keeps familiar interfaces like python, R, Excel & JSON so that BigData enthusiasts & experts can explore, munge, model and score datasets using a range of simple to advanced algorithms. Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. H2O has a vision of online scoring and modeling in a single platform.
Package: h2o-cloud-discovery
h2o-cloud-discoveryH2O Cloud Discovery Client.Installationpipinstallh2o-cloud-discoveryUsagePackage provides two main functions.  Synchronoush2o_discovery.discover()and asynchronoush2o_discovery.discover_async().  Both functions return
a discovery object that can be used to obtain the information the H2O Cloud
environment, its services and clients.When used within theH2O AI Cloudenvironment or locally with theH2O AI Cloud CLIconfigured, no further configuration is needed.Both functions accept aenvironmentargument that can be used to specify the
H2O Cloud environment for which the discovery should be performed.
Alternatively, theH2O_CLOUD_ENVIRONMENTenvironment variables can be used.See API documentation for more details.importh2o_discoverydiscovery=h2o_discovery.discover()# Print the H2O Cloud environment that was discovered.print(discovery.environment.h2o_cloud_environment)# Connect to the my service.my_service_client=my_service.client(address=discovery.services["my-service"].uri)ExamplesExample: Use with H2O.ai MLOps Python Client within the Wave Appimporth2o_authnimporth2o_discoveryimporth2o_mlops_clientasmlopsfromh2o_waveimportQ,app,uifromh2o_waveimportmain@app("/")asyncdefserve(q:Q):discovery=awaith2o_discovery.discover_async()token_provider=h2o_authn.AsyncTokenProvider(refresh_token=q.auth.refresh_token,issuer_url=discovery.environment.issuer_url,client_id=discovery.clients["platform"].oauth2_client_id,)mlops_client=mlops.Client(gateway_url=discovery.services["mlops-api"].uri,token_provider=token_provider,)...Example 2: Use within a notebook to connect to the H2O AI Drive# Install required packages.importsys!{sys.executable}-mpipinstallh2o-cloud-discoveryh2o-authn[discovery]# Load discovery for the current environment.importh2o_discoverydiscovery=h2o_discovery.discover()# Create a token provider using the credentials loaded from the environment.importh2o_authn.discoverytoken_provider=h2o_authn.discovery.create_async(discovery)# Install the H2O AI Drive client in the version specified by the available# service.importsys!{sys.executable}-mpipinstall'{discovery.services["drive"].python_client}'# Connect to the H2O AI Drive and list home objects.importh2o_drivehome=awaith2o_drive.MyHome(token=token_provider,endpoint_url=discovery.services["drive"].uri,)awaithome.list_objects()DevelopmentProject is managed usingHatch.TestingFor quick development tests use:hatchrundevtest:pytestFull test matrix can be run using:hatchenvremovetest&&hatchruntest:pytestLintinghatchrunlint:checkFormating and imports can be fixed using:hatchrunlint:fix