Saravanan666/pypi-org huggingface dataset:
Package: cysignals
Cython and interruptsWhen writingCythoncode, special care must be
taken to ensure that the code can be interrupted withCTRL-C.
Since Cython optimizes for speed, Cython normally does not check for
interrupts. For example, code like the following cannot be interrupted
in Cython:while True:
    passThecysignalspackage provides mechanisms to handle interrupts (and other
signals and errors) in Cython code.RequirementsPython >= 3.6Cython >= 0.28Sphinx >= 1.6 (for building the documentation)PARI/GP (optional; for interfacing with the PARI/GP signal handler)Linkscysignals on the Python package index:https://pypi.org/project/cysignals/cysignals code repository and issue tracker on GitHub:https://github.com/sagemath/cysignalsfull cysignals documentation on Read the Docs:http://cysignals.readthedocs.io/Changelog1.11.4 (2023-10-07)Include generatedconfigurescript in the sdist again.1.11.3 (2023-10-04)Add support for Cython 3. [#174, #176, #182, #187]Add support for Python 3.12.Replacefprintfby calls towrite, which is async-signal-safe according to POSIX. [#162]Introduce a general hook to interface with custom signal handling. [#181]1.11.2 (2021-12-15)Drop assembly code added after 1.10.3 that is not portable.1.11.0 (2021-11-26)Drop Python 2 support; bump minimum Python version to 3.6. [#142]Fixed compilation with glib 3.34. [#151]Improved testing. [#139, #152, #154]1.10.3 (2021-03-16)Improved installation of cysignals withpip install-e. [#130]Fixed compilation of OpenMP modules that also use cysignals. [#128]Fixed segmentation fault that could occur whensig_occurred()is
called recursively during garbage collection. [#127]Improved error reporting of signals that occurred insidesig_on()as
opposed to outside them.Fixed bug in thecysignals_examplepackage. [#113]For changes in previous releases, see the best source available is to
compare git tags:https://github.com/sagemath/cysignals/tags
Package: cyst
This is a metapackage for CYST simulation framework. If installed, it will pull the most commonly used packages.
These include:cyst-core: CYST runtime and APIcyst-templates: Templates of CYST extensionsCYST is a multi-agent discrete-event simulation framework tailored for cybersecurity domain. Its goal is to enable
high-throughput and realistic simulation of cybersecurity interactions in arbitrary infrastructures. The driving force
behind the initial implementation was the need to have an environment for training ML-driven cybersecurity agents; to
have a mean to achieve autonomous cybersecurity.Autonomous cybersecurity is a distant goal which requires many hurdles to be overcome. CYST aspires to be an important
technology in this regard by providing key functionality, which is not available elsewhere (at least not all in one
package):Lightweight simulation of multi-agent cybersecurity scenarios.Streamlined integration with ML toolkits.Integration of different behavioral models for attackers, defender, users, or observers.Hybrid-stochastic simulation.Rapid prototyping of attack and defense strategies.Integration of simulation and emulation (e.g., IDS in the loop).Smooth transition of agents into the real world.Automated generation of realistic cybersecurity scenarios.Extensible transformation of simulation artifacts into flows, etc.Comprehensive visualization of attack progress.Support for stealth and evasive actions.Support for multi-agent collaboration and communication.Naturally, as this is a research project, this functionality is in various state of completion. But we are getting
there. CYST is being developed in the context of several research projects:AI-Dojo (https://www.muni.cz/en/research/projects/62788)BEAST (https://www.muni.cz/en/research/projects/49127)C4e (https://c4e.cz/)The full documentation can be found here:https://muni.cz/go/cyst/
Package: cyst-core
CYST is a multi-agent discrete-event simulation framework tailored for cybersecurity domain. Its goal is to enable
high-throughput and realistic simulation of cybersecurity interactions in arbitrary infrastructures. The driving force
behind the initial implementation was the need to have an environment for training ML-driven cybersecurity agents; to
have a mean to achieve autonomous cybersecurity.Autonomous cybersecurity is a distant goal which requires many hurdles to be overcome. CYST aspires to be an important
technology in this regard by providing key functionality, which is not available elsewhere (at least not all in one
package):Lightweight simulation of multi-agent cybersecurity scenarios.Streamlined integration with ML toolkits.Integration of different behavioral models for attackers, defender, users, or observers.Hybrid-stochastic simulation.Rapid prototyping of attack and defense strategies.Integration of simulation and emulation (e.g., IDS in the loop).Smooth transition of agents into the real world.Automated generation of realistic cybersecurity scenarios.Extensible transformation of simulation artifacts into flows, etc.Comprehensive visualization of attack progress.Support for stealth and evasive actions.Support for multi-agent collaboration and communication.Naturally, as this is a research project, this functionality is in various state of completion. But we are getting
there. CYST is being developed in the context of several research projects:AI-Dojo (https://www.muni.cz/en/research/projects/62788)BEAST (https://www.muni.cz/en/research/projects/49127)C4e (https://c4e.cz/)The full documentation can be found here:https://muni.cz/go/cyst/
Package: cyte
<body style="padding: 1in;"><header style="text-align: center"><address style="font-size: 2em">cyte</address><p>measurement tools (System International, US Customary, etc.)</p></header><article 1 style="background: #eee; padding: 1in; border-radius: .1in; "><section><address>the health license</address></section><section><p>May you metaphorically recognize this asa reservoir of pure water molecules:</p><code id="water-molecule" python3 style="white-space: pre-wrap;"></code><p>You discovered it in aplace where you haveundisputable claim to theentire span of every dimension(including time).<br/><br/>However, this pure water, may onlybe used to promote veganism.</p></section></article><article><p>The documentation is can be read by installing and running "revenue"</p><code>pip install revenue</code><code># from the "cyte" module or the git repo[tcsh] revenue</code></article><script>var water = JSON.stringify ({"structures": {"1": "hydrogen","2": "hydrogen","3": "oxygen"},"links": [[ 1, 3 ],[ 2, 3 ]]}, null, 4)document.getElementById ("water-molecule").textContent = water;</script></body>
Package: cythereal-dashboard
The API used exclusively by the MAGIC Dashboard for populating charts, graphs, tables, etc… on the dashboard.  # API ConventionsAll responsesMUST be of type &#x60;APIResponse&#x60; and contain the following fields:  * &#x60;api_version&#x60; |  The current api version * &#x60;success&#x60; | Boolean value indicating if the operation succeeded. * &#x60;code&#x60; | Status code. Typically corresponds to the HTTP status code.  * &#x60;message&#x60; | A human readable message providing more details about the operation. Can be null or empty.Successful operationsMUST return a &#x60;SuccessResponse&#x60;, which extends &#x60;APIResponse&#x60; by adding:  * &#x60;data&#x60; | Properties containing the response object. * &#x60;success&#x60; | MUST equal True  When returning objects from a successful response, the &#x60;data&#x60; object SHOULD contain a property named after the requested object type. For example, the &#x60;/alerts&#x60; endpoint should return a response object with &#x60;data.alerts&#x60;. This property SHOULD  contain a list of the returned objects. For the &#x60;/alerts&#x60; endpoint, the &#x60;data.alerts&#x60; property contains a list of MagicAlerts objects. See the &#x60;/alerts&#x60; endpoint documentation for an example.Failed OperationsMUST return an &#x60;ErrorResponse&#x60;, which extends &#x60;APIResponse&#x60; by adding:  * &#x60;success&#x60; | MUST equal False.   # noqa: E501
Package: cythereal-neomodel
NOTE: This is a customized version of neomodel for personal use. Use theofficial versioninstead.An Object Graph Mapper (OGM) for theneo4jgraph database, built on the awesomeneo4j_driverFamiliar Django model style definitions.Powerful query API.Enforce your schema through cardinality restrictions.Full transaction support.Thread safe.pre/post save/delete hooks.Django integration viadjango_neomodelDocumentationAvailable onreadthedocs.RequirementsPython 2.7, 3.4+neo4j 3.0, 3.1, 3.2, 3.3InstallationInstall from pypi (recommended):$ pip install neomodelTo install from github:$ pip install git+git://github.com/robinedwards/neomodel.git@HEAD#egg=neomodel-devUpgrading 2.x to 3.xNow utilises neo4j_driver as the backend which uses bolt so neo4j 3 is requiredConnection now set through config.DATABASE_URL (see getting started docs)The deprecated category() method on StructuredNode has been removedThe deprecated index property on StructuredNode has been removedThe streaming=True flag is now irrelevant with bolt and produces a deprecation warningBatch operations must now be wrapped in a transaction in order to be atomicIndexing NodeSets returns a single node now as opposed to a listContributingIdeas, bugs, tests and pull requests always welcome.Running the test suiteMake sure you have a fresh virtualenv andnoseinstalled:$ pip install noseAlso a Neo4j database version 3 or higher to run the tests on. (it will wipe this database for each test run):$ export NEO4J_BOLT_URL=bolt://neo4j:neo4j@localhost:7687 # (the default)Setup a virtual environment, install neomodel for development and run the test suite:$ virtualenv venv
$ source venv/bin/activate
$ python setup.py develop
$ nosetests -sIf your running a neo4j database for the first time the test suite will set the password to ‘test’.If you havedocker-composeinstalled, you can run the test suite against all supported Python
interpreters and neo4j versions:# in the project's root folder:
$ ./tests-with-docker-compose.sh
Package: cythereal-vbsdk
The VirusBattle SDK (vbsdk) is a library to access Cythereal’s
Malware Genomic Correlation (MAGIC) Search and Cythereal’s VirusBattle
Automated Malware Analysis webservice.Installing this package also creates thevbclientcommand that can be used
from the command line.You can download just theLinux clientor theWindows clientif you don’t
need access to the full sdk library.Source:https://bitbucket.org/cythereal/virusbattle-sdk/srcMore Info:https://bitbucket.org/cythereal/virusbattle-sdk/wiki/Home
Package: cython-bbox
cython_bboxcython_bbox is widely used in object detection tasks. To my best knowledge, it was first implemented inFaster-RCNN. Since then, almost all object detection projects use the source code directly.In order to use it in standalone code snippets or small projects, I make it a pypi module. Thecython_bbox.pyxis totally borrowed fromFaster-RCNN. ThanksRBG!installpip install cython_bboxusagefrom cython_bbox import bbox_overlaps
import numpy as np

gt = np.random.random((5, 4))
dt = np.random.random((10, 4))

overlaps = bbox_overlaps(
        np.ascontiguousarray(dt, dtype=np.float),
        np.ascontiguousarray(gt, dtype=np.float)
    )
Package: cython-bbox-fix
cython_bboxcython_bbox is widely used in object detection tasks. To my best knowledge, it was first implemented inFaster-RCNN. Since then, almost all object detection projects use the source code directly.In order to use it in standalone code snippets or small projects, I make it a pypi module. Thecython_bbox.pyxis totally borrowed fromFaster-RCNN. ThanksRBG!installpip install cython_bboxusagefrom cython_bbox import bbox_overlaps
overlaps = bbox_overlaps(
        np.ascontiguousarray(dt, dtype=np.float32),
        np.ascontiguousarray(gt, dtype=np.float32)
    )
Package: cython-bbox-nguyenchien
cython_bboxcython_bbox is widely used in object detection tasks. To my best knowledge, it was first implemented inFaster-RCNN. Since then, almost all object detection projects use the source code directly.In order to use it in standalone code snippets or small projects, I make it a pypi module. Thecython_bbox.pyxis totally borrowed fromFaster-RCNN. ThanksRBG!installpip install cython_bboxusagefrom cython_bbox import bbox_overlaps
overlaps = bbox_overlaps(
        np.ascontiguousarray(dt, dtype=np.float32),
        np.ascontiguousarray(gt, dtype=np.float32)
    )
Package: cython-bbox-og
cython_bboxCOPIED FROM THE ORIGINALcython-bboxCredits:samson-wangYyuk-Liaoa-j-paulPhilip Fu (cython_bbox_fix)Modifications have been made from the original code to update for new numpy versionsThe original README is repeated belowcython_bbox is widely used in object detection tasks. To my best knowledge, it was first implemented inFaster-RCNN. Since then, almost all object detection projects use the source code directly.In order to use it in standalone code snippets or small projects, I make it a pypi module. Thecython_bbox.pyxis totally borrowed fromFaster-RCNN. ThanksRBG!installpip install cython_bboxusagefrom cython_bbox import bbox_overlaps
overlaps = bbox_overlaps(
        np.ascontiguousarray(dt, dtype=np.float32),
        np.ascontiguousarray(gt, dtype=np.float32)
    )
Package: cythonbiogeme
CythonBiogemeBiogeme is an open source freeware designed for the maximum likelihood estimation of parametric models in general, with a special emphasis on discrete choice models.
CythonBiogeme is the part of the package that involves C++ code, to be installed separately.InstallBiogeme requires Python >= 3.8 and can be installedfrom PyPI.pip install cythonbiogemeInstalling CythonBiogeme from source can be done after cloning the repository with the following command executed in the directory where the filesetup.pyis located:pip install -ve .DocumentationFurther documentation is available onhttps://biogeme.epfl.ch/
Package: cythonbuilder
CythonBuilderCythonBuilder; automated compiling and packaging of Cython codeVersionPackageStatusStatsSocialInstallationpip install cythonbuilderHow to useCythonBuilder makes it easy to use Cython in your Python project by automating the building process.
You can use CythonBuilder from the commandline or import it as a package in Python.
Generated files can be imported in Python directlyWith CommandlineAdd-v(verbose) for more informationListing files with and without filtercybuilder list
cybuilder list --files file1 file2.pyxBuild with and without optional build argumentscybuilder build
cybuilder build --include-numpy --no-annotation --no-cleanupCleancybuilder clean 
cybuilder clean --no-cleanupWith pythonListing files with and without filterfromcythonbuilderrimportcythonbuilderascybuilderprint(cybuilder.cy_list())# without a filterprint(cybuilder.cy_list(target_files=['some_name.pyx']))# with a filterBuild with and without optional build arguments  (cleans automatically afterwards)fromsrcimportcythonbuilderascybuildercybuilder.cy_build()found_files=cybuilder.cy_build(target_files=['some_name'])cybuilder.cy_build(target_files=found_files,include_numpy=False,create_annotations=False)Cleanfromsrcimportcythonbuilderascybuildercybuilder.cy_clean()found_files=cybuilder.cy_build(target_files=['some_name'])cybuilder.cy_clean(target_files=['some_name'])Setting debug level for verbose loggingfromcythonbuilderrimportloggerfromcythonbuilderrimportset_logger_debug_modeset_logger_debug_mode(logger=logger)In-depth, step by step ExplanationI've written a few articles that explain why Python is slow, why Cython can be a solution and how CythonBuilder helps us develop fast code easily:Why Python is so slow and how to speed it upGetting started with Cython; how to perform >1.7 billion calculations per second with PythonCython for data science: 6 steps to make this Pandas dataframe operation over 100x faster
Package: cythoncartesian
Cartesian Product - 6x faster than itertools.product - 10x less memorypip install cythoncartesianTested against Windows / Python 3.11 / AnacondaCython (and a C/C++ compiler) must be installedGeneratetheCartesianproductofinputiterables.Parameters:-----------*args:listofiterableInputiterablesforwhichtheCartesianproductwillbecomputed.outputdtype(optional):dtypeofoutputarrayReturns:--------numpy.ndarray2DarraycontainingtheCartesianproductoftheinputiterables.Notes:------ThisfunctionefficientlycomputestheCartesianproductoftheinputiterablesusingCythonimplementation.Itoutperformstheequivalentfunctionalityprovidedbyitertools.product,andreturnsaNumPyarray(notalistoftupleslikeitertools.product).Examples:---------fromcythoncartesianimportcartesian_product# Mem usage 2 GB# Out[4]:# array([[0, 0, 0, ..., 0, 0, 0],#        [1, 0, 0, ..., 0, 0, 0],#        [2, 0, 0, ..., 0, 0, 0],#        ...,#        [5, 7, 7, ..., 7, 7, 7],#        [6, 7, 7, ..., 7, 7, 7],#        [7, 7, 7, ..., 7, 7, 7]], dtype=uint8)# %timeit dataresults=cartesian_product(*args2)# 2.65 s ± 163 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# dataresults.shape# Out[6]: (134217728, 9)# itertools.product# Mem usage 16 GB# import itertools# %timeit (list(itertools.product(*args2)))# 11.5 s ± 203 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# --------------------------------------------------------------------------# Mem usage 1.2 GB# args = [[411, 231.33, 4342, 12341, 1.142, 1.33, 13],#         [34, 231.33, 4132, 1231],#          [14, 44, 23454.1, .1, 23, 1],#          [9, 12, 1, 3, 32, 23, 21, 31],#          [1114, 44, 23454.1, .1, 23, 1],#         ]+[list(range(6)),list(range(3)),list(range(3)),list(range(3))#     ,list(range(3))]+[list(range(6))]# %timeit dataresults=cartesian_product(*args)# 621 ms ± 46.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# Mem usage 4 GB# import itertools# %timeit (list(itertools.product(*args)))# 2.13 s ± 26.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Package: cythoncartesian2
Cartesian Product for NumPy - 40x faster than NumPy + itertools.productpip install cythoncartesian2Tested against Windows / Python 3.11 / AnacondaCython (and a C/C++ compiler) must be installedcartesian_product(*args,outputdtype=np.uint32,dtype=np.uint32):CalculatetheCartesianproductofinputarrays.Parameters:-*args:Variablenumberofinputarrays.-outputdtype(numpy.dtype):Datatypeoftheoutputarray.-dtype(numpy.dtype):Datatypeusedforintermediatecalculations.# be careful!Returns:-numpy.ndarray:Cartesianproductofinputarrays.importrandomfromcythoncartesian2importcartesian_productimportnumpyasnp# Strings are NOT supported!args=[[h*random.uniform(1,4)forhin(range(random.randint(2,9)))]forxinrange(9)]q=cartesian_product(*args,outputdtype=np.float32,dtype=np.uint32)# array([[0.       , 0.       , 0.       , ..., 0.       , 0.       ,#         0.       ],#        [3.529998 , 0.       , 0.       , ..., 0.       , 0.       ,#         0.       ],#        [0.       , 3.715651 , 0.       , ..., 0.       , 0.       ,#         0.       ],#        ...,#        [3.529998 , 7.956308 , 5.9014587, ..., 1.0379078, 7.9018135,#         8.816498 ],#        [0.       , 9.456019 , 5.9014587, ..., 1.0379078, 7.9018135,#         8.816498 ],#        [3.529998 , 9.456019 , 5.9014587, ..., 1.0379078, 7.9018135,#         8.816498 ]], dtype=float32)args=[[hforhin(range(8))]forxinrange(9)]q=cartesian_product(*args,outputdtype=np.uint8,dtype=np.uint32)# %timeit q=cartesian_product(*args,outputdtype=np.uint8,dtype=np.uint32)# 1.63 s ± 36.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# %timeit (list(itertools.product(*args)))# 11.3 s ± 180 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# %timeit q=np.array(list(itertools.product(*args)),dtype=np.uint8)# 1min 6s ± 282 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)# q# Out[3]:# array([[0, 0, 0, ..., 0, 0, 0],#        [1, 0, 0, ..., 0, 0, 0],#        [2, 0, 0, ..., 0, 0, 0],#        ...,#        [5, 7, 7, ..., 7, 7, 7],#        [6, 7, 7, ..., 7, 7, 7],#        [7, 7, 7, ..., 7, 7, 7]], dtype=uint8)
Package: cython-compiler
Cython CompilerA simple script that cythonizes your python project, handles import statements, so you still can use normal packaging tools like PyInstaller to bundle your project.Installationpip install cython-compileror the manual way:git clonehttps://github.com/JessicaTegner/cython-compiler.gitcd cython-compilerpython setup.py installQuick Usage (see below for a detailed tutorial)usage: cython-compiler [-h] [-c] [-r] [-e ENTRY]Cythonize your entire project while keeping the easyness of packaging with
PyInstaller and testing with an interpreted language.optional arguments:optional arguments:
-h, --help            show this help message and exit
-c, --cythonize       Cythonize your project and assemble imports.
-r, --remove          Remove the .pyd files, so you can test with python.
-e ENTRY, --entry ENTRY
The entry file that starts the program when ran with python. This file will not be cythonized.Detailed tutorialInstall the Cython compiler.Go to the project you want to cythonize.If not done already, change the main file of your program, so i t contains a main method that starts the application.Make sure your entrypoint looks something like this.import __required_imports__ # this will be generated by the cython cimpiler later.
import main_application # this is where your def main function is located, the method, that actually starts your application.

if __name__ == "__main__":
	main_application.main() # asuming that your method is called main.run "cython-compiler -c -e program.py", where program.py is the entry of your application (look at the template above).After the cython compiler is done, test to see if it works with python program.py (if everything goes as planned, your program should start as normal).LicenseGNU LESSER GENERAL PUBLIC LICENSE  Version 3
For more information see the license file found in this repository.