Saravanan666/pypi-org huggingface dataset:
Package: h2spacex
H2SpaceXHTTP/2 low level library based on Scapy which can be used for Single Packet Attack (Race Condition on H2)TODOSingle Packet Attack - POST &...implementtest functionality on different H2 frameworksSingle Packet Attack - GETimplementContent-Length: 1 MethodPOST Request with x-override-method: GET headertest functionality on different H2 frameworksResponse Parsingimplementimplement threaded response parserBody DecompressiongzipbrdeflateProxySocks5 ProxyMore ResearchSome following statements are just ideas and not tested or implemented.More Request in a Single PacketIncrease MSS (Idea by James Kettle)Out of Order TCP Packets (Idea by James Kettle)IP FragmentationProxy the Single Packet Request through SOCKSSingle Packet Attack on GET RequestsContent-Length: 1 Method (Idea by James Kettle)x-override-method: GET Method (Idea by James Kettle)Index HPACK Headers to Make GET Requests SmallerHEADERS Frame without END_HEADER FlagHEADERS Frame Without Some Pseudo HeadersInstallationH2SpaceX works with Python 3 (preferred: >=3.10)pip install h2spacexQuick StartYou can import the HTTP/2 TLS Connection and set up the connection. After setting up the connection, you can do other things:fromh2spaceximportH2OnTlsConnectionh2_conn=H2OnTlsConnection(hostname='http2.github.io',port_number=443)h2_conn.setup_connection()...see more examples inWiki PageReferences & ResourcesJames Kettle DEF CON 31 PresentationPortswigger Research PageHTTP/2 in Action BookI also got some ideas from a previous developed libraryh2tinker.Finally, thanks again to James Kettle for directly helping and pointing some other techniques.
Package: h2tinker
H2TinkerH2Tinker is a minimalistic low-level HTTP/2 client implementation in Python.It is based onscapyand also enables directly sending scapy-crafted frames. On top of scapy, h2tinker providesHTTP/2 connection setup and management,TCP and TLS connection setup and management,a user-friendly documented and typed API for creating different frames and requests,documentation and examples on how different attacks can be implemented.QuickstartSeeExamplesto get started.Installationh2tinker is available in thePython Package Indexand can be installed with pip:pip3 install h2tinker
Package: h2-vulnerability-db
IntroductionThis repository contains a vulnerability database and a package search for OSV, NVD, GitHub, and NPM sources.
Data on vulnerabilities is downloaded from the sources and stored in a custom file-based storage system with indexes that enables offline access and quick searches.Installationpipinstallh2-vulnerability-dbUsageThis package is ideal as a vulnerability management library. This is howh2-depscan, a dependency auditing tool, works.
However, a limited cli capability with few features is available for testing this tool directly.Cache vulnerability dataCache from all sourcesvdb--cacheCache from justOSVvdb--cache--only-osvIt is possible to customise the cache behaviour by increasing the historic data period to cache by setting the following environment variables.NVD_START_YEAR - Default: 2016. Supports upto 2002GITHUB_PAGE_COUNT - Default: 5. Supports upto 20Periodic syncTo periodically sync the latest vulnerabilities and update the database cache.vdb--syncBasic searchIt is possible to perform simple search using the cli.vdb--searchandroid:8.0

vdb--searchgoogle:android:8.0

vdb--searchandroid:8.0,simplesamlphp:1.14.11Syntax is package:version,package:version or vendor : package : version (Without space)
Package: h3
h3-py: Uber's H3 Hexagonal Hierarchical Geospatial Indexing System in PythonPython bindings for theH3 core library.Documentation:uber.github.io/h3-pyGitHub repo:github.com/uber/h3-pyInstallationFromPyPI:pip install h3Fromconda:conda config --add channels conda-forgeconda install h3-pyUsage>>>importh3>>>lat,lng=37.769377,-122.388903>>>resolution=9>>>h3.geo_to_h3(lat,lng,resolution)'89283082e73ffff'APIsWe providemultiple APIsinh3-py.All APIs have the same set of functions;
see theAPI reference.The APIs differ only in their input/output formats;
see theAPI comparison page.Example galleryBrowsea collection of example notebooks,
and if you have examples or visualizations of your own, please feel free
to contribute!We also have an introductorywalkthrough of the API.Versioningh3-pywraps theH3 core library,
which is written in C.
The C and Python projects each employsemantic versioning,
where versions take the formX.Y.Z.Theh3-pyversion string is guaranteed to match the C library string
in bothmajorandminornumbers (X.Y), but may differ on thepatch(Z) number.
This convention provides users with information on breaking changes and
feature additions, while providing downstream bindings (like this one!)
with the versioning freedom to fix bugs.Useh3.versions()to see the version numbers for bothh3-pyand the C library. For example,>>>importh3>>>h3.versions(){'c':'3.6.3','python':'3.6.1'}
Package: h3converter
Convert H3This package helps you convert h3 hec code directly to geojson and a GeoJSON entity to h3 hex code.Sample:H3 to GeoJSON:from h3converter import h3converterh3converter.h3_to_geojson(['89be455030bffff','89be455030cffff'])GeoJSON to H3:from h3converter import h3converterh3converter.polyfill({'type':'Polygon','coordinates':[[[144.244589, -36.810689], [144.244246, -36.808741], [144.241989, -36.807999], [144.240075, -36.809205], [144.240418, -36.811153], [144.242676, -36.811895], [144.244589, -36.810689]]]},9)where polyfill has (geojson, precision).
Package: h3daemon
Welcome to h3daemon 👋Command-line for running HMMER server on arm64 and amd64 machines.🏠Homepage⚡️ RequirementsPython >= 3.9PipHomebrewon MacOS (recommended)Pipxfor Python package management (recommended)MacOSInstall Python and Pipx:brewupdate&&brewinstallpythonpipxEnsure that yourPATHenvironment variable is all set:pipxensurepath💡 You might need to close your terminal and reopen it for the changes to take effect.Ubuntu (and Debian-based distros)Install Python and Pipx:sudoaptupdate&&\sudoaptinstallpython3python3-pippython3-venv--yes&&\python3-mpipinstall--userpipxEnsure that yourPATHenvironment variable is all set:python3-mpipxensurepath💡 You might need to close your terminal and reopen it for the changes to take effect.📦 Installpipxinstallh3daemonUsageUsage: h3daemon [OPTIONS] COMMAND [ARGS]...

╭─ Options ─────────────────────────────────────────────────────╮
│ --version                                                     │
│ --help             Show this message and exit.                │
╰───────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────╮
│ start                 Start daemon.                           │
│ stop                  Stop daemon.                            │
╰───────────────────────────────────────────────────────────────╯ExampleDownloadminifam.hmmdatabase:pipxrunblxget\fe305d9c09e123f987f49b9056e34c374e085d8831f815cc73d8ea4cdec84960\minifam.hmmPress it:pipxrun--spechmmerhmmpressminifam.hmmStart the daemon to listen on a random (available) port:h3daemonstartminifam.hmmAnd stop it:h3daemonstopminifam.hmm👤 AuthorDanilo HortaShow your supportGive a ⭐️ if this project helped you!
Package: h3pandas
H3-Pandas ⬢ 🐼IntegratesH3withGeoPandasandPandas.⬢Try it out⬢Installationpippipinstallh3pandascondacondainstall-cconda-forgeh3pandasUsage examplesH3 APIh3pandasautomatically applies H3 functions to both Pandas Dataframes and GeoPandas Geodataframes# Prepare data>>>importpandasaspd>>>importh3pandas>>>df=pd.DataFrame({'lat':[50,51],'lng':[14,15]})>>>resolution=10>>>df=df.h3.geo_to_h3(resolution)>>>df|h3_10|lat|lng||:----------------|------:|------:||8a1e30973807fff|50|14||8a1e2659c2c7fff|51|15|>>>df=df.h3.h3_to_geo_boundary()>>>df|h3_10|lat|lng|geometry||:----------------|------:|------:|:----------------||8a1e30973807fff|50|14|POLYGON((...))||8a1e2659c2c7fff|51|15|POLYGON((...))|H3-Pandas Extended APIh3pandasalso provides some extended functionality out-of-the-box,
often simplifying common workflows into a single command.# Set up data>>>importnumpyasnp>>>importpandasaspd>>>np.random.seed(1729)>>>df=pd.DataFrame({>>>'lat':np.random.uniform(50,51,100),>>>'lng':np.random.uniform(14,15,100),>>>'value':np.random.poisson(100,100)})>>>})# Aggregate values by their location and sum>>>df=df.h3.geo_to_h3_aggregate(3)>>>df|h3_03|value|geometry||:----------------|--------:|:----------------||831e30fffffffff|102|POLYGON((...))||831e34fffffffff|189|POLYGON((...))||831e35fffffffff|8744|POLYGON((...))||831f1bfffffffff|1040|POLYGON((...))|# Aggregate to a lower H3 resolution>>>df.h3.h3_to_parent_aggregate(2)|h3_02|value|geometry||:----------------|--------:|:----------------||821e37fffffffff|9035|POLYGON((...))||821f1ffffffffff|1040|POLYGON((...))|Further examplesFor more examples, see theexample notebooks.APIFor a full API documentation and more usage examples, see thedocumentation.DevelopmentH3-Pandas cover the basics of the H3 API, but there are still many possible improvements.Any suggestions and contributions are very welcome!In particular, the next steps are:Improvements & stability of the "Extended API", e.g.k_ring_smoothing.Additional possible directionsAllow for alternate h3-py APIs such asmemview_intPerformance improvements throughCythonized h3-pyDaskintegration throughdask-geopandas(experimental as of now)Seeissuesfor more.
Package: h3ronpy
A data science toolkit for theH3 geospatial grid.This library is not a substitute for the officialpython h3 library- instead it provides more
high-level functions on top of H3 and integrations into common dataframe libraries.Documentation is available onhttps://h3ronpy.readthedocs.io/.FeaturesH3 algorithms provided using the performanth3olibrary.Build onApache Arrowandpyarrowfor efficient data handling.Dedicated APIs for the thepandasandpolarsdataframe libraries. Thepandassupport includesgeopandas.Multi-threaded conversion of raster data to the H3 grid usingnumpy arrays.Multi-threaded conversion of vector data, includinggeopandasGeoDataFramesand any object which supports the python__geo_interface__protocol (shapely,geojson, …).Most parts of this library aim to be well-performing. Benchmarking the conversion of 1000 uint64 cell
values to strings usinga simplistic list comprehension callingh3-pyh3_to_stringa numpy vectorized (numpy.vectorize) variant ofh3-pyh3_to_stringthecells_to_stringfunction of this library (release build)leads to the following result on a standard laptop:---------------------------------------------------------------------------------------------- benchmark: 3 tests ---------------------------------------------------------------------------------------------
Name (time in us)                           Min                 Max                Mean            StdDev              Median               IQR            Outliers  OPS (Kops/s)            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_cells_to_string                    48.4710 (1.0)       75.5000 (1.0)       52.4252 (1.0)      1.5461 (1.0)       52.0330 (1.0)      0.4890 (1.0)       307;448       19.0748 (1.0)        4090           1
test_h3_to_string_python_list          290.5460 (5.99)     325.8180 (4.32)     297.5644 (5.68)     4.8769 (3.15)     296.1350 (5.69)     8.2420 (16.85)       806;4        3.3606 (0.18)       2863           1
test_h3_to_string_numpy_vectorized     352.9870 (7.28)     393.8450 (5.22)     360.1159 (6.87)     3.7195 (2.41)     359.4820 (6.91)     3.8420 (7.86)      447;131        2.7769 (0.15)       2334           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Legend:
  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
  OPS: Operations Per Second, computed as 1 / MeanThe benchmark implementation can be found intests/polars/test_benches.pyand usespytest-benchmark.LimitationsNot all functionalities of the H3 grid are wrapped by this library, the current feature-set was implemented
when there was a need and the time for it. As a opensource library new features can be requested in the form of github issues
or contributed using pull requests.LicenseMIT
Package: h4
LLMs that are Helpful, Honest, Harmless, and Huggy 🤗InstallationDownload and installh4by running:python-mpipinstallh4If you want the bleeding-edge version, install from source by running:python-mpipinstallgit+https://github.com/huggingface/h4.gitDeveloper installationTo contribute code to this project, first create a Python virtual environment using e.g. Conda:condacreate-nh4python=3.8&&condaactivateh4Then install the base requirements with:python-mpipinstall-e'.[dev]'This will install core packages likeblackandisortthat we use to ensure consistent code formatting.Formatting your codeWe useblackandisortto ensure consistent code formatting. After following the installation steps, you can check your code locally by running:make style && make quality
Package: h5browse
h5browse (formerly dataviz) is a utility for browsing HDF5 files.It was developed for analyzing neuronal simulation data saved in a
custom format. But the interface is generic enough to display simple
HDF5 files. The tools are totally for neuronal data analysis. But the
plan is to make it a more general tool.As of 2015-12-21, it supports displaying the tree structure of an HDF5
file, node attributes and dataset contents. You can select arbitrary
fields in a dataset for displaying X-Y plots.Implementation of editing HDF5 is incomplete.Note that being based on h5py library it inherits the limitations of
the same.This is a rewrite of the old code. Replaced PyQwt with
PyQtGraph. Written from scratch in Python 3. Compatible with
Python2.7.
Package: h5config
H5configH5config is a Python package for conviniently saving and loading
configuration classes from disk.  After populating base configuration
classes with parameters, h5config automatically generates HDF5 and
YAML backends for saving and loading that class.  Yes, all this
information could go into a single database, but if you like the
flexibility of having a bunch of individual files that you can peek
into one at a time, this module is for you.I wrote the module to support my experiment-control infrastructure,
and one of the key goals was to easily embed control parameters in the
saved data:experiment.h5
|-- raw/
|   |-- thermocouple: [32757, 32731, 32796, 32789, 32809, ...]
|   `-- deflection: [29808, 29793, 29782, 29817, 29775, ...]
|-- timeseries-config/
|   |-- frequency: 0.1
|   |-- run-time: 100
|   |-- thermocouple-channel/
|   |   |-- conversion-coefficients: [-10.0, 0.000305180437934]
|   |   `-- conversion-origin: [0.0]
|   |-- deflection-channel/
|   |   |-- conversion-coefficients: [-10.0, 0.000305180437934]
|   |   `-- conversion-origin: [0.0]
|   `-- thermocouple-calibration: [25.08355e3, 7.860106e4]
`-- environment-config/
    |-- temperature: 20
    `-- timestamp: 1311851980.750180Storing all of the calibration and conversion factors can get
complicated quickly.  h5config will keep you organized, and allow you
to focus on defining the config options without getting bogged down in
the bookkeeping.As a side effect, h5config also provides some tools to support system-
and user-wide configuration files.  For example, thepypiezopackage
uses h5config to automatically detect and load the following config
file:$ cat ~/.config/pypiezo.yaml
log-level: debug
matplotlib: noInstallationPackagesGentooI’ve packaged h5config for Gentoo.  You needlaymanand mywtk
overlay.  Install with:# emerge -av app-portage/layman
# layman --add wtk
# emerge -av dev-python/h5configDependenciesIf you’re installing by hand or packaging h5config for another
distribution, you’ll need the following dependencies:PackageDebianGentooH5Pypython-h5pydev-python/h5pyNosepython-nosedev-python/nosePyYAMLpython-pyyamldev-python/pyyamlInstalling by handH5config is available as aGitrepository:$ git clone git://tremily.us/See thehomepagefor details.  To install the checkout, run the
standard:$ python setup.py installUsageSee the module docstrings for simple examples.TestingRun internal unit tests with:$ nosetests --with-doctest --doctest-tests h5configLicenceThis project is distributed under theGNU General Public License
Version 3or greater.AuthorW. Trevor Kingwking@tremily.us
Package: h5cross
H5CROSSThis package provides basic utilities allowing the comparison of hdf5 type files.
This includes the computation of standard statistics on data arrays such as the mean,
min, max, median and standard deviation.
An interactive mode is enabled through the use of nobvisual (https://pypi.org/project/nobvisual/).
Scatter plot comparative capability is enabled through seaborn and matplotlib.InstallationInstallation using Pypi:>pipinstallh5crossUsageUse the CLI for a basic terminal usage.---------------H5CROSS--------------------YouarenowusingtheCommandlineinterfaceofh5crossaPython3helpertoexploreandcomparehdf5files,createdatCERFACS(https://cerfacs.fr).Thisisapythonpackagecurrentlyinstalledinyourpythonenvironement.

Options:--helpShowthismessageandexit.

Commands:convertConversiontohdf5ofcertainfileformats.diffComparethecontentoftwohdf5filesandviewinteractively...dumpWritethecontentofanhdf5fileintoaYAMLfile.scatterScatterplotcomparisonoftwohdf5files.statsComputestatisticsofarraysfromhdf5file.treePrintthecontentofanhdf5fileinterminal.viewShowthecontentofanhdf5fileinteractivelywithnobvisual.PossibilitiesConsole file structure visualizationThe command>h5cross treeprints the structure of an HDF5 file in the console.Statistical information of fileThe command>h5cross statscomputes the mean, min, max, median and standard deviation of every data set for a given HDF5 file.  It is then output into the console (can be optionally deactivated). The result can be optionally saved in a YAML format (.yml). A pretty table output is set to default and can be optionally be deactivated in which case a tree output is given. In a pretty table, the full nested path is shown but can be deactivated.Save file structureThe command>h5cross dumpoutputs the structure of an HDF5 file in a YAML format (.yml).View file structureThe command>h5cross viewallows an interactive view of the structure of an HDF5 file. It relies on thenobvisualpackage designed for the visualisation of nested objects.  A temporary .yml file is generated to allow the interactive use.Compare file structuresThe command>h5cross diffcompares the structure of two HDF5 files. Similarily to>h5cross viewit relies on thenobvisualpackage.  It is optionally possible to add the statistical information of both files in this representation.Compare field dataThe command>h5cross scatterallows the scatter plot comparison of selected data fields from two HDF5 files. If the data array lengths of each file differ an histogram representation will be given instead.
A minimum requirement is the matplotlib package. By default it uses the seaborn package if available but this setting can be optionally deactivated. The density colormap requires the scipy package, if not available a single color will be used.Converting file types to HDF5 fileIt is possible to convert certain file formats to HDF5 with>h5cross convert.
Currently supported: nek5000, pvtu, vtu, vtk.Acknowledgementh5cross is a service created in theEXCELLERAT Center Of Excellenceand is continued as part of theCOEC Center Of Excellence. Both projects are funded by the European community.
Package: h5cube
Compression/decompression command-line tool and Python package for
Gaussian CUBE files, exploiting the capabilities of theHDF5binary format viah5py.Branches named with the prefixv.are volatile. The history
there may be rewritten dramatically, without warning.Available onPyPI(pip install h5cube).Source onGitHub(v0.3 CHANGELOG).Documentation at Read the Docs:
Package: h5cv
h5cvshow hdf5 file in image file.get start$ls-lhevaluation/
total2936-rw-r--r--@1hiroshi.toyama152273951537K732019car1.jpg
-rw-r--r--@1hiroshi.toyama1522739515188K732019cat1.jpg
-rw-r--r--1hiroshi.toyama1522739515137K732019dog1.jpg
-rw-r--r--1hiroshi.toyama152273951551K732019dog2.jpg
-rw-r--r--1hiroshi.toyama152273951577K732019dog3.jpg
-rw-r--r--1hiroshi.toyama15227395158.8K732019dog4.jpg
-rw-r--r--@1hiroshi.toyama152273951542K732019monitor1.jpg
-rw-r--r--1hiroshi.toyama1522739515267K732019person1.jpg
-rw-r--r--1hiroshi.toyama1522739515170K732019person2.jpg
-rw-r--r--1hiroshi.toyama1522739515467K732019room1.jpg

$h5cv-Htest.h5--storenumpywrite-g'evaluation/*.jpg'-a2020-03-0619:13:35,972INFO-compression=>None2020-03-0619:13:35,973INFO-generatenumpy:evaluation/dog4.jpg=>test.h5:evaluation/dog4.jpg2020-03-0619:13:35,977INFO-generatenumpy:evaluation/car1.jpg=>test.h5:evaluation/car1.jpg2020-03-0619:13:35,978INFO-generatenumpy:evaluation/dog3.jpg=>test.h5:evaluation/dog3.jpg2020-03-0619:13:35,980INFO-generatenumpy:evaluation/monitor1.jpg=>test.h5:evaluation/monitor1.jpg2020-03-0619:13:35,982INFO-generatenumpy:evaluation/dog2.jpg=>test.h5:evaluation/dog2.jpg2020-03-0619:13:35,983INFO-generatenumpy:evaluation/dog1.jpg=>test.h5:evaluation/dog1.jpg2020-03-0619:13:35,985INFO-generatenumpy:evaluation/cat1.jpg=>test.h5:evaluation/cat1.jpg2020-03-0619:13:35,987INFO-generatenumpy:evaluation/person1.jpg=>test.h5:evaluation/person1.jpg2020-03-0619:13:35,989INFO-generatenumpy:evaluation/person2.jpg=>test.h5:evaluation/person2.jpg2020-03-0619:13:35,991INFO-generatenumpy:evaluation/room1.jpg=>test.h5:evaluation/room1.jpg

$h5cv-Htest.h5ls-r
evaluation
evaluation/car1.jpg
evaluation/cat1.jpg
evaluation/dog1.jpg
evaluation/dog2.jpg
evaluation/dog3.jpg
evaluation/dog4.jpg
evaluation/monitor1.jpg
evaluation/person1.jpg
evaluation/person2.jpg
evaluation/room1.jpgh5cv commandlist keys$h5cv-Htest.h5ls[-r][key]show dataset in hdf5.$h5cv-Htest.h5show/some_group/test.jpg.npimgcat in hdf5.$h5cv-Htest.h5imgcat/some_group/test.jpgwrite in hdf5.$h5cv-Htest.h5\[--store[numpy|binary]]\write\-g'images/*.jpg'\[--output-groupmygroup]\[--compressiongzip]\[--append]delete dataset or group in hdf5.$h5cv-Htest.h5delete/some_group/test.jpgcustom generatorfrom h5cv.core import Core
from h5cv.generator import Generator
from PIL import Image


class MyGenerator(Generator):
    def __getitem__(self, path):
        print(f"custom generate {path}")
        return Image.open(path)

Core(
    hdf5="test.h5"
).write(
    "evaluation/*.jpg",
    generator=MyGenerator(),
    compression="gzip"
)Installationpipinstallh5cvInstallation from githubpipinstallgit+https://github.com/toyama0919/h5cv