Saravanan666/pypi-org huggingface dataset:
Package: hhcp
hhcpCopy a large number of files with multi thread to speed up normal cp.InstallpipinstallhhcpUsageCopy a list of files with a filelist to a specified folder.hhcp-fpath_of_file_list-ddestination_folder[--preroot_of_all_files][--force]Copy a dir to a specified folderhhcp-ppath_of_dir_to_be_copied-ddestination_folder[--force]Only one of-pand-fcan be used at the same time.ExampleCopy a list of files with a filelist calledlist.txtto a specified
folder/mnt/lustre/sharewith root path/mnt/hhand force
overwrite files with same name.hhcp-flist.txt-d/mnt/lustre/share--pre/mnt/hh--forceCopy the dirdir1in current path to another folderhhin the
current path without overwrite.hhcp-p./dir1-d./hhParameterspPath of the directory to be copied.fPath of the file list.dPath of the destinationforceAdd this to allow force overwritepreThe path prefix of the files in the filelist.Can only be used with-f.
Package: hhfetcher
hhfetcher is for fetching common resources in a directory.Current supported API:get_subfolders(root: str) -> [str]: Get immediate subfolders in folder of root.get_subfolder_names(root: str) -> [str]: Get names of immediate subfolder in folder of root.get_subfolders_recursively(root: str) -> [str]: Get all subfolders recursively in folder of root.get_files_in_dir(root: str, formats: [str]) -> [str]: Get all files just in directory of root.get_files_recursively_in_dir(root: str, formats: [str]) -> [str]: Get all files recursively contained in directory of root.get_imgs_in_dir(root: str) -> [str]: Get all imgs just in directory of root.get_imgs_recursively_in_dir(root: str) -> [str]: Get all imgs recursively contained in directory of root.
Package: hhhash
HTTP Headers Hashing (HHHash)HTTP Headers Hashing (HHHash) is a technique used to create a fingerprint of an HTTP server based on the headers it returns. HHHash employs one-way hashing to generate a hash value for the set of header keys returned by the server.For more details about HHHash background,HTTP Headers Hashing (HHHash) or improving correlation of crawled content.Calculation of the HHHashTo calculate the HHHash, we concatenate the list of headers returned by the HTTP server. This list is ordered according to the sequence in which the headers appear in the server's response. Each header value is separated with:.The HHHash value is the SHA256 of the list.HHHash formathhh:1:20247663b5c63bf1291fe5350010dafb6d5e845e4c0daaf7dc9c0f646e947c29prefix:version:SHA 256 valueExampleCalculating HHHash from a curl commandCurl will attempt to run the request using HTTP2 by default. In order to get the same hash as the python requests module (which doesn't supports HTTP2), you need to specify the version with the--http1.1switch.curl--http1.1-s-D-https://www.circl.lu/-o/dev/null|awk'NR != 1'|cut-f1-d:|sed'/^[[:space:]]*$/d'|sed-z's/\n/:/g'|sed's/.$//'|sha256sum|cut-f1-d" "|awk{'print "hhh:1:"$1'}Output valuehhh:1:78f7ef0651bac1a5ea42ed9d22242ed8725f07815091032a34ab4e30d3c3cefcLimitationsHHHash is an effective technique; however, its performance is heavily reliant on the characteristics of the HTTP client requests. Therefore, it is important to note that correlations between a set of hashes are typically established when using the same crawler or HTTP client parameters.HTTP2 requires theheaders to be lowercase. It will then changes the hash so you need to be aware of the HTTP version you're using.hhhash - Python LibraryThehhhash packagecan be installed via apip install hhhashor build with Poetry from this repositorypoetry buildandpoetry install.UsageIn [1]: import hhhash

In [2]: hhhash.buildhash(url="https://www.misp-lea.org", debug=False)
Out[2]: 'hhh:1:adca8a87f2a537dbbf07ba6d8cba6db53fde257ae2da4dad6f3ee6b47080c53f'

In [3]: hhhash.buildhash(url="https://www.misp-project.org", debug=False)
Out[3]: 'hhh:1:adca8a87f2a537dbbf07ba6d8cba6db53fde257ae2da4dad6f3ee6b47080c53f'

In [4]: hhhash.buildhash(url="https://www.circl.lu", debug=False)
Out[4]: 'hhh:1:334d8ab68f9e935f3af7c4a91220612f980f2d9168324530c03d28c9429e1299'

In [5]:Other librariesc-hhhash- C++ HTTP Headers Hashing CLIgo-hhhash- golang HTTP Headers Hashing CLIR hhhash- R library HHHash
Package: hhhrealpython-reader
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:python -m pip install realpython-readerThe reader is supported on Python 3.7 and above. Older versions of Python, including Python 2.7, are supported by version 1.0.0 of the reader.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorials, call the program without any arguments:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: hhlpy
HHLPyHHLPy is a formal verification tool for hybrid systems. It is based on an
extension of Hoare logic to hybrid systems called Hybrid Hoare Logic.InstallationBase installationThe base installation of HHLPy does not require Wolfram Engine:Install Python 3.9 or higher:https://www.python.org/downloads/.Runpip install hhlpyorpython -m pip install hhlpyto install HHLPy.Runpython -m hhlpyto start HHLPy. Your browser should open automatically.
(If it doesn't, openhttp://127.0.0.1:8000/in your browser.)Install Wolfram EngineTo be able to proof more verification conditions, install Wolfram Engine on your system:Download Wolfram Engine and install it:https://www.wolfram.com/engine/Get a license for Wolfram Engine and activate it.If you use the standard installation path, HHLPy should be able to
find it automatically; simply runpython -m hhlpy.If you see the messagePlease install Wolfram Kernel ..., you
need to set the environment variableWolframKernelto the path of the fileWolframKernelorWolframKernel.exethat comes with the Wolfram Engine
installation. Then restart your terminal and runpython -m hhlpy.If you see the lineSocket exception: Socket operation aborted.in the terminal,
you probably still need to activate your license.First StepsClick on the filebasic1.hhlin the list of example files on the left panel. A
file with the following content will open:pre [x >= 0];
x := x+1;
post [x >= 1];This example program has a single instruction: it increases the variable x by 1.
The only precondition isx >= 0; the only postcondition isx >= 1.
These conditions seem correct: Ifxis at least0and increased by1, it
is at least1afterwards.On the right side, you see the verification condition panel. It contains a
single verification condition that has been generated from the program:assume:
  x >= 0
show: x + 1 >= 1Click on the buttonVerifyto check the verification condition. A green
checkmark appears below the condition. And the counter next to the button
indicates that1/1verification conditions have been proved.Next, add a second instruction to the program that divides x by 2:pre [x >= 0];
x := x+1;
x := x/2;
post [x >= 1];Observe that the verification condition on the right updates automatically.
Click the buttonVerifyto verify the new condition. You will see an X mark
indicating that the verification condition could not be verified. Try to adapt
the postcondition to make the verification go through...Explore the other example files to see more!
Package: hhpy
hhpy - Data Science convenience projectBuilt withPython Package Template Project.My attempt at creating a convenient way to use different data science and machine learning operations and frameworks using one unified way.
The main idea is to store data in pandas DataFrames and start from there.Also provides several extensions on pandas, matplotlib and seaborn for convenience.InstallingInstall and update usingpip:pipinstallhhpyOr get the latest dev version from githubpipinstallgit+https://github.com/rhedak/hhpyDocumentationDocumentation is available athttps://hhpy.readthedocs.io/
Package: hhs-vertretungsplan-parser
hhs-vertretungsplanPython parser for the Heinrich-Hertz-Schule, Hamburg, Vertretungsplan interfaceRetrieves latest status of classes being substituted.Ask your Elternvertreter for username and password.Usage:importaiohttpimportasyncioimportsysfromhhs_vertretungsplan_parser.vertretungsplan_parserimportHHSVertretungsplanParserasyncdefmain(user:str,password:str):asyncwithaiohttp.ClientSession()assession:hhs=HHSVertretungsplanParser(session,user,password)awaithhs.load_data()forvertretunginhhs.vertretungen:print(f"Klasse{vertretung.klasse}hat am{vertretung.datum}in Stunde{vertretung.stunde}im Fach{vertretung.fach}bei{vertretung.vertreter}Vertretung.")if(__name__=='__main__'):# on Windows add:# asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())iflen(sys.argv)==3:asyncio.run(main(sys.argv[1],sys.argv[2]))else:print(f"usage: python hhs-vertretungsplan.py <user> <password>")
Package: hhttpp
Handmade HTTP Project (HHTTPP)Free range artisnal HTTPHHTTPP is an educational tool that is part ofa series of blog postsbeing made to help teach about HTTP servers, and how to build one from scratch in python. The goals of the project are to build a package that:Can be installed from pip (uploaded to pypi)Can do all basic types of requests and responses (not every response code supported)Have a simple CLI to make it easy to useQuick-startInstallationFrom sourceClone this repo: (put github/source code link here)Runpip install .orsudo pip3 install .in the root directoryConfirm it is working by runninghhttpp -hwhich should show you the text you see inusageFrom PyPiRunpip install hhttppConfirm it is working by runninghhttpp -hwhich should show you the text you see inusageUsageHHTTPP has a CLI that you can use, the usage string can be found below:hhttpp

FreerangeartisnalHTTPserver

Usage:hhttpp[-h][-v][-pPORT][-fPROXY_FOLDER]Options:-h,--helpShowthishelpmessageandexit-v,--versionShowprogram'sversionnumberandexit-pPORT,--portPORTTheporttostarttheserveron-fPROXY_FOLDER,--folderPROXY_FOLDERLetsyouspecifyafoldertoproxyinsteadofcwdBy default just runninghhttppin a folder will proxy the current folder (unless otherwise specified) you're in and find an available port to bind to if one is not specified (starting with 8338).API ExamplesThe simplest way to usehhttppas an API is to import theServerobject, and let it run:fromhhttppimportServerServer().start_server()The server object in this case will have all the logs and server state, so if you want to change behaviours, modify it's initialization. For example initializing while only allowing certain files to be visible to the server:fromhhttppimportServers=Server(file_list=["index.html","/css/main.css"])s.start_server()
Package: hhunt
House hunterCopyright (c) 2015-2018 Jeremie DECOCK (http://www.jdhp.org)Web site:http://www.jdhp.org/projects_en.htmlSource code:https://github.com/jeremiedecock/hhuntIssue tracker:https://github.com/jeremiedecock/hhunt/issuesHouse hunter on PyPI:https://pypi.python.org/pypi/hhuntHouse hunter on Anaconda Cloud:https://anaconda.org/jdhp/hhuntDescriptionHouse hunter is an open source tool to manage house adverts for house seekers.WarningThis project is in beta stage.DependenciesPython >= 3.0Qt5 for PythonPandasInstallationGnu/LinuxYou can install, upgrade, uninstall House hunter with these commands (in a
terminal):pip install --pre hhunt
pip install --upgrade hhunt
pip uninstall hhuntOr, if you have downloaded the House hunter source code:python3 setup.py installWindowsYou can install, upgrade, uninstall House hunter with these commands (in acommand prompt):py -m pip install --pre hhunt
py -m pip install --upgrade hhunt
py -m pip uninstall hhuntOr, if you have downloaded the House hunter source code:py setup.py installMacOSXNote:The following installation procedure has been tested to work with Python
3.5 under MacOSX 10.9 (Mavericks).
It should also work with more recent MacOSX systems.You can install, upgrade, uninstall House hunter with these commands (in a
terminal):pip install --pre hhunt
pip install --upgrade hhunt
pip uninstall hhuntOr, if you have downloaded the House hunter source code:python3 setup.py installHouse hunter requires Qt5 and its Python 3 bindings plus few additional
libraries to run.Bug reportsTo search for bugs or report them, please use the House hunter Bug Tracker at:https://github.com/jeremiedecock/hhunt/issuesLicenseThis project is provided under the terms and conditions of theMIT License.
Package: hi2020
开源pypi包制作1.注册账号https://pypi.org2.添加许可证https://choosealicense.com/licenses/3.定义项目包结构hi2020
├── LICENSE
├── MANIFEST.in
├── README.md
├── setup.py
├── src
│ └── hi2020
│     ├── __init__.py
│     ├── bin
│     │ ├── __init__.py
│     │ └── cli.py
│     ├── calculate.py
│     ├── say.py
│     └── static
│         └── hi.js
└── tests
    └── index.py4.安装打包工具# 安装依赖
pip install setuptools
pip install wheel

# 更新依赖
python -m pip install --upgrade setuptools wheel5.打包代码python setup.py sdist bdist_wheel6.安装上传工具python -m pip install --upgrade twine
或
pip install --upgrade twine7.发布模块python -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
或
twine upload --repository-url https://upload.pypi.org/legacy/ dist/*8.使用模块# 安装模块
pip install hi2020

# 导入模块
from hi2020 import say

# 执行函数
say.hi()9.使用命令行hi -h

hi hello
Package: hiargparse
Hierarchical ArgparseHiargparse is a hierarchical and highly sophisticated variant ofargparse.Minimal CodefromhiargparseimportArgsProvider,Arg,ChildProvider,ArgumentParserchild=ArgsProvider(args=[Arg('baz',default=42)])root=ArgsProvider(args=[Arg('foo',default='bar')],child_providers=[ChildProvider(provider=child,name='child')])parser=ArgumentParser()parser.add_arguments_from_provider(root)print(parser.parse_args())DescriptionHiargparse automatically generates the command-line arguments with your all classes in your tree-like module structures with minimal codings.Suppose you make a large module, and your hierarchically deep class, sayfoo.bar.baz.ham.spam.Egg, requires some arguments, sayheightsandwidths.Foomakes an instance ofBar,Barmakes an instance ofBaz, ..., andSpammakes anEggwhich has the two arguments.
When you want to pass them command-line options, you have to write the two,heightsandwidths, in the constructor ofFoo,Bar, ..., andSpam.
What if theEggis updated and wants to require some new arguments, likedepthorlength?
Hiargparse passes the arguments directly to the classes without any black magics.FeaturesWith this module, you caneasily make hierarchical (tree-like) command-line arguments withargparseEach argument is automatically help-texted and grouped intoargparse.ArgumentGroup.get a more useful Namespace object than the originalAccessing with dict-like key, getting the child Namespace, converting to/from dictionaries, and so onwrite/read the arguments to/from  some configure files with well known formatsCurrently we supportsyamlandtoml.Also, this moduleis almost compatible with original argparse; you can gradually introduce it to your large projects.works without command-line arguments; when you distribute your module with hiargparse,
users still can select whether to feed arguments to it with a command-line or programatic way.This module is inspired by rlpytorch.args_provider inFacebookAIResearch/ELF.InstallationpipinstallhiargparseRequirementspython >= 3.6.0typing_extensions (for typing_extentions.Protocol in python 3.6) >= 3.6.2.1pyyaml >= 3.12 (only if you use yaml write/read)toml >= 0.9.4 (only if you use toml write/read)ExamplesSee/examples/example.pyto easy start.You can use hiargparse with original ArgumentParser. See/examples/example_with_original.py.If you want to use hiargparse without command-line arguments, see/examples/example_without_program_arguments.py./examples/example_write_and_read.pydescribes how to write and read the arguments with a configure file.A lot of things you can do with hiargparse are shown in/examples/complicated_example.py.ContributionAny contribution is welcome!Fork the repo, create a branch, add your awesome changes, and make a new Pull Request.If you find some bugs, please report in issue.AuthorKeigo Kawamura (Department of Electrical Engineering and Information Systems (EEIS), Graduate School of Engineering, The University of Tokyo)kkawamura@logos.t.u-tokyo.ac.jp
Package: hiatus
hiatusHiatus is a python library that uses thethreadingmodule’s “Timer” to
implement analogues to javascript’ssetTimeout/clearTimeoutandsetInterval/clearInterval.These functions may be used as decorators. Also important is
that the python analogues take time arguments inseconds, notmilliseconds, in order to be consistent with the standard library.A Caveat:Much of python does not “play nice” with threading. You have been warned.(I looked into a signal-based approach, but this is limited. Another approach would be to depend on an event loop, such astwisted.reactor.)Examples:>>> from hiatus import set_interval
>>> @set_interval(1.00)
... def dave_grohl():
...     print "THE BEST"
...
>>> THE BEST
THE BEST
THE BEST
THE BEST
THE BEST>>> def note():
...     print "HUGE SUCCESS"...
>>> hiatus.set_timeout(lambda: hiatus.clear_interval(glados), 6.0)
<hiatus.set_timeout object at 0xb770468c>
>>> glados = hiatus.set_interval(note, 1.000)
>>> HUGE SUCCESS
HUGE SUCCESS
HUGE SUCCESS
HUGE SUCCESSFor more:Visit <https://github.com/jesusabdullah/hiatus> .
Package: hibachi
HibachiHibachiis a Python package that implements feature selection methods for
PyTorch.InstallationRun the following command:pip install hibachiReferences@incollection {
    NIPS2017_7270,
    title = {Kernel Feature Selection via Conditional Covariance Minimization},
    author = {Chen, Jianbo and Stern, Mitchell and Wainwright, Martin J and
        Jordan, Michael I},
    booktitle = {Advances in Neural Information Processing Systems 30},
    editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R.
        Fergus and S. Vishwanathan and R. Garnett},
    pages = {6949--6958},
    year = {2017},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/7270-kernel-feature-selection-via-conditional-covariance-minimization.pdf}
}
Package: hibor
easy to use as most of the data returned are pandas DataFrame objectscan be easily saved as csv, excel or json filescan be inserted into MySQL or MongodbTarget Usersfinancial market analyst of Chinalearners of financial data analysis with pandas/NumPypeople who are interested in China financial dataInstallationpip install hiborUpgradepip install hibor –upgradeQuick Startimport hibor as hb

hb.get_hist_data('600848')return:open    high   close     low     volume    p_change  ma5
date
2012-01-11   6.880   7.380   7.060   6.880   14129.96     2.62   7.060
2012-01-12   7.050   7.100   6.980   6.900    7895.19    -1.13   7.020
2012-01-13   6.950   7.000   6.700   6.690    6611.87    -4.01   6.913
2012-01-16   6.680   6.750   6.510   6.480    2941.63    -2.84   6.813
2012-01-17   6.660   6.880   6.860   6.460    8642.57     5.38   6.822
2012-01-18   7.000   7.300   6.890   6.880   13075.40     0.44   6.788
2012-01-19   6.690   6.950   6.890   6.680    6117.32     0.00   6.770
2012-01-20   6.870   7.080   7.010   6.870    6813.09     1.74   6.832
Package: hibp
# Have I Been Pwned?Python interface to Have I Been Pwned API## What is Have I Been Pwned?[Have I Been Pwned](https://haveibeenpwned.com/) is a free resource to quickly assess if an account or domain has been compromised or “pwned” in a data breach. By aggregating the data here the project helps victims be aware of account compromises, and highlights the severity of the risks of Internet-wide attacks. For more information on who, what, and why, click [here](https://haveibeenpwned.com/About).## Dependencies` requests gevent `## SetupThis library runs onPython3.x+To install, run:` $ pip install hibp `## To runYou can query breach data on individual accounts/domains as well as data on full breaches. Each service request object contains a response attribute that holds the raw data output in JSON format. To perform a query, just setup a service request object, and then execute:`python >> req =HIBP.get_account_breaches("pegasos1")>> req.execute() >> req.response `If you want to query on multiple accounts or domains at once, you can use theAsyncHIBPobject, which performs queries concurrently via gevent.`python >> names =['adobe','ashleymadison','myspace'] >> breaches = [HIBP.get_breach(x) for x in names] >> async_reqs =AsyncHIBP().map(breaches)>> [async_req.response for async_req in async_reqs] `In addition to a canonicalmapmethod,AsyncHIBPalso supports theimapmethod, which returns a generator object for lazy queries.`python >> domains =['twitter.com','facebook.com','adobe.com'] >> breaches = [HIBP.get_domain_breaches(x) for x in domains] >> async_reqs =AsyncHIBP().imap(breaches)>> next(async_reqs) `