Saravanan666/pypi-org huggingface dataset:
Package: n0s1
🏠 Homepage📖 Documentationn0s1 - Secret Scannern0s1 (pronunciation) is a secret scanner for Jira, Confluence, Asana, Wrike and Linear.app. It scans all tickets/items/issues within the chosen platform in search of any leaked secrets in the titles, bodies, and comments. It is open-source and it can be easily extended to support scanning many others Project Management and Issue Tracker platforms.These secrets are identified by comparing them against an adaptable configuration file namedregex.yaml. Alternative TOML format is also supported:regex.toml. The scanner specifically looks for sensitive information, which includes:Github Personal Access TokensGitLab Personal Access TokensAWS Access TokensPKCS8 private keysRSA private keysSSH private keysnpm access tokensCurrently supported target platforms:JiraConfluenceAsanaWrikeLinearQuick StartCLI:python3-mpipinstalln0s1
n0s1jira_scan--server"https://<YOUR_JIRA_SERVER>.atlassian.net"--api-key"<YOUR_JIRA_API_TOKEN>"Docker:dockerrunspark1security/n0s1jira_scan--server"https://<YOUR_JIRA_SERVER>.atlassian.net"--api-key"<YOUR_JIRA_API_TOKEN>"From source:gitclonehttps://github.com/spark1security/n0s1.gitcdn0s1/src/n0s1
python3-mvenvn0s1_pythonsourcen0s1_python/bin/activate
python3-mpipinstall-r../../requirements.txt
python3n0s1.pyjira_scan--server"https://<YOUR_JIRA_SERVER>.atlassian.net"--api-key"<YOUR_JIRA_API_TOKEN>"deactivateGitHub Actions:jobs:jira_secret_scanning:steps:-uses:spark1security/n0s1-action@mainenv:JIRA_TOKEN:${{ secrets.JIRA_API_TOKEN }}with:scan-target:'jira_scan'user-email:'service_account@<YOUR_COMPANY>.atlassian.net'platform-url:'https://<YOUR_COMPANY>.atlassian.net'GitLab CI - Add the following job to your .gitlab-ci.yml file:jira-scan:stage:testimage:name:spark1security/n0s1entrypoint:[""]script:-n0s1 jira_scan --email "service_account@<YOUR_COMPANY>.atlassian.net" --api-key $JIRA_TOKEN --server "https://<YOUR_COMPANY>.atlassian.net" --report-file gl-dast-report.json --report-format gitlab-apt-get update-apt-get -y install jq-cat gl-dast-report.json | jqartifacts:reports:dast:-gl-dast-report.jsonWant more? Check out Spark 1If you liked n0s1, you will love Spark 1 which builds on top of n0s1 to provide even more enhanced capabilities for a complete security management offering.Don't forget to check out thehttps://spark1.uswebsite for more information about our products and services.If you'd like to contact Spark 1 or request a demo, please use thefree consultation form.Communityn0s1 is aSpark 1open source project.Learn about our open source work and portfoliohere.Contact us about any matter by opening a GitHub Discussionhere
Package: n2cw
# n2cw - Nagios to CloudWatchn2cw is a wrapper for check scripts that implement the[Nagios Plugin API](http://nagios.sourceforge.net/docs/3_0/pluginapi.html). Itruns the specified check command and pushes the results, optionally includingmetrics for all [perfdata](http://nagios.sourceforge.net/docs/3_0/perfdata.html).## Installation```pip install n2cw```## Usage```usage: n2cw [options] namespace base_name -- commandA wrapper for nagios-style checks that pushes check status and perfdata toCloudWatch metrics.positional arguments:namespace             CloudWatch namespacebase_name             Base name for checkscommand               Command (nagios check) to runoptional arguments:-h, --help            show this help message and exit--dimensions key=value,key=valueCloudWatch metric dimensions--noop                Don't push metrics, just log--no-perfdata         Don't send metrics for performance data--no-status           Don't send the check status output````command` is the full command line, with arguments, for the nagios plugin youwish to run.## Example```n2cw --dimensions "Host=webserver1" MySuperWebsite Disk -- ./check_disk -w 10% -c 5% -p / -k```If the output of check_disk was ...```DISK OK - free space: / 7292772 kB (49% inode=88%);| /=7402636kB;15481742;15481741;0;15481840```The following metrics would be pushed to CloudWatch:```Namespace       Metric Name  Value    Dimensions-----------------------------------------------------MySuperWebsite  Disk-Status  0        Host=webserver1MySuperWebsite  Disk-/       7292772  Host=webserver1```The value of the *-Status* metric is the check return code (so OK is 0, WARNINGis 1, etc).
Package: n2w-it
N2W-ITThis library convert numbers to italian words.For example:73->settantatré.It currently only supports converting numbers tosingular malewords.InstallationThe easy way is to install from PyPI:pip install n2w-it.Otherwise, you can download source package e manually install with:pip install .(desirable way),or with:python setup.py install.TestYou can run:python setup.py test.Otherwise you can directly test with:unittest.UsageCommand lineThe easy way to use, is to launch directly from terminal:$ n2w-it 73
settantatréImportYou can import the libray in your code:from n2w_it import N2W_IT
instance = N2W_IT()
try:
  result = instance.number_to_words("73")
  print(result) #settantatré
except Exception as exception:
  print(exception)APIsThe library supports number conversion in italian from:Integer to cardinal:int_to_cardinal(73)->settantatré.Float to cardinal:float_to_cardinal(73.37)->settantatré virgola trentasette.Integer to ordinal:int_to_ordinal(73)->settantatreesimo.Roman to ordinal:roman_to_ordinal("LXXIII")->settantatreesimo.And a heuristic version:(If theargumentpassed isNone or Empty, or the number formatis not found or valid, anExceptionis raised.)number_to_words("73")->settantatré.number_to_words("73.37")->settantatré virgola trentasette.number_to_words("73°")->settantatreesimo.number_to_words("LXXIII")->settantatreesimo.number_to_words("LXXIII°")->settantatreesimo.History1.0.1 (2021-03-30)Updated documentation.1.0.0 (2021-03-30)First release on PyPI.
Package: n2yo
N2YO Python APIThis python package is a wrapper to access the N2YO REST API.For each transaction, the caller must be identified and authorized by a license key (API key). To generate a unique license key, users are requested first to register an account at n2yo.com. After login, please visit the profile page and scroll down to access the button that generates the API key. The new API key will be accessible in the profile and cannot be changed. If you need a new key, please contact us and provide an explanation. The keys issued for the SOAP Web Services API are not valid for REST API.The following functions are currently available:get_TLE:
Retrieve the Two Line Elements (TLE) for a satellite identified by NORAD id.get_positions: Retrieve the future positions of any satellite as footprints (latitude, longitude) to display orbits on maps. Also return the satellite's azimuth and elevation with respect to the observer location. Each element in the response array is one second of calculation. First element is calculated for current UTC time.get_visualpasses: Get predicted visual passes for any satellite relative to a location on Earth. A "visual pass" is a pass that should be optically visible on the entire (or partial) duration of crossing the sky. For that to happen, the satellite must be above the horizon, illumintaed by Sun (not in Earth shadow), and the sky dark enough to allow visual satellite observation.get_radiopasses: The "radio passes" are similar to "visual passes", the only difference being the requirement for the objects to be optically visible for observers. This function is useful mainly for predicting satellite passes to be used for radio communications. The quality of the pass depends essentially on the highest elevation value during the pass, which is one of the input parameters.get_above: The "above" function will return all objects within a given search radius above observer's location. The radius (θ), expressed in degrees, is measured relative to the point in the sky directly above an observer (azimuth).
Package: n2yo-api
N2YO Python APIThis python package is a wrapper to access the N2YO REST API.For each transaction, the caller must be identified and authorized by a license key (API key). To generate a unique license key, users are requested first to register an account atn2yo. After login, please visit the profile page and scroll down to access the button that generates the API key. The new API key will be accessible in the profile and cannot be changed. If you need a new key, please contact us and provide an explanation.Available functionsget_tleRetrieve the Two Line Elements (TLE) for a satellite identified by NORAD id.get_satellite_positionsRetrieve the future positions of any satellite as footprints (latitude, longitude) to display orbits on maps. Also return the satellite's azimuth and elevation with respect to the observer location. Each element in the response array is one second of calculation. First element is calculated for current UTC time.get_visual_passesGet predicted visual passes for any satellite relative to a location on Earth. A "visual pass" is a pass that should be optically visible on the entire (or partial) duration of crossing the sky. For that to happen, the satellite must be above the horizon, illumintaed by Sun (not in Earth shadow), and the sky dark enough to allow visual satellite observation.get_radio_passesThe "radio passes" are similar to "visual passes", the only difference being the requirement for the objects to be optically visible for observers. This function is useful mainly for predicting satellite passes to be used for radio communications. The quality of the pass depends essentially on the highest elevation value during the pass, which is one of the input parameters.get_aboveThe "above" function will return all objects within a given search radius above observer's location. The radius (θ), expressed in degrees, is measured relative to the point in the sky directly above an observer (azimuth).
Package: n2yoasync
N2YO Python APIThis python package is an async wrapper to access the N2YO REST API.For each transaction, the caller must be identified and authorized by a license key (API key). To generate a unique license key, users are requested first to register an account at n2yo.com. After login, please visit the profile page and scroll down to access the button that generates the API key. The new API key will be accessible in the profile and cannot be changed. If you need a new key, please contact us and provide an explanation. The keys issued for the SOAP Web Services API are not valid for REST API.The following functions are currently available:get_TLE:
Retrieve the Two Line Elements (TLE) for a satellite identified by NORAD id.get_positions: Retrieve the future positions of any satellite as footprints (latitude, longitude) to display orbits on maps. Also return the satellite's azimuth and elevation with respect to the observer location. Each element in the response array is one second of calculation. First element is calculated for current UTC time.get_visualpasses: Get predicted visual passes for any satellite relative to a location on Earth. A "visual pass" is a pass that should be optically visible on the entire (or partial) duration of crossing the sky. For that to happen, the satellite must be above the horizon, illumintaed by Sun (not in Earth shadow), and the sky dark enough to allow visual satellite observation.get_radiopasses: The "radio passes" are similar to "visual passes", the only difference being the requirement for the objects to be optically visible for observers. This function is useful mainly for predicting satellite passes to be used for radio communications. The quality of the pass depends essentially on the highest elevation value during the pass, which is one of the input parameters.get_above: The "above" function will return all objects within a given search radius above observer's location. The radius (θ), expressed in degrees, is measured relative to the point in the sky directly above an observer (azimuth).Based on the original API wrapper fromhttps://github.com/circuitqed/n2yo
Package: n3
N3: Neural Network NotationThis project is WIP. Please be aware of using it.node LeNet5:
    let K: kernel size = int 5

    let W: width = int 28
    let H: height = int 28

    with Conv2D:
        set kernel size = K
        set padding = K / 2
        set stride = 2

    node MyConv:

        1. Conv2D
        2. Relu

    0. Input                   =  1, W  , H
    1. MyConv                  = 32, W/2, H/2
    2. MyConv                  = 64, W/4, H/4
    3. Transform               = 64* W/4* H/4
    4. Linear + Relu + Dropout = 1024
    5. Linear + Softmax(D=-1)  = 10UsageServer$sudosystemctlstartn3-torchdClientTraining$n3trainimage_classification--modelLeNet5--dataMNIST--devicescuda:0cpuEvaluating$n3evalimage_classification--modelLeNet5--dataMNIST--devicescuda:0cpuPublishing$n3publishimage_classification--modelLeNet5--targetandroid:javaandroid: java, flutterios: flutteruniversal: c++, pythonMonitoring using Tensorboard$n3monitor# or, browse http://localhost::xxxx/Distributed Training$n3trainimage_classification--modelLeNet5--dataMNIST--devicesw:180:cuda:0w:192.168.0.181cpu"w:180:cuda:0": the "cuda:0" device in "xxx.xxx.xxx.180" (local)"w:192.168.0.181": automatically choose devices in "192.168.0.181"These can be defined as environment variables (N3_MACHINES)
Package: n3ml-python
n3mlNeuromorphic Neural Network Machine Learning (N3ML) is designed to provide the following features.Construct spiking neural networksSimulate spiking neural networksTransform spiking neural networksVisualize spiking neural networksN3ML is implemented by wrapping some features in PyTorch[7]. N3ML wrapstorch.nn.Moduleclass to track the structure of the built neural network easily and provide the compatiability with operators in PyTorch. We can construct a spiking neural network consisting of both the operators provided in N3ML and the ones in PyTorch. We can track the architecture of a spiking neural network using the methods provided bytorch.nn.Moduleclass. It is helpful to transform a spiking neura network to other simulators/frameworks or neuromorphic hardwares.N3ML usestorch.autograd.Functionto implemet surrogate derviative-based learning algorithms.N3ML wrapstorch.Tensorto get GPU accelerations.Constructing spiking neural networksSimulating spiking neural networksTransforming spiking neural networksVisualizing spiking neural networksSupporting learning algorithmsn3ml provides some learning algorithms as examples that can be simulated. If you want to simulate them, you can go to test directory.
Now, n3ml supports the following learning algorithmsSpikeProp [1]STDP [2]Soft LIF [3]BP-STDP [4]STBP [5]InstallationNow, n3ml is tested in Python 3.7.7.Install dependenciesIf you are installing from pip, some dependencies will be installed automatially that are registered in setup.py. Also, others can be found in requirements.txt.python -r requirements.txtYou can install n3ml using pip.pip install n3ml-pythonGPU accelerationWe follows BindsNET[6] to supprot GPU acceleration in N3ML. Bascially, N3ML is implemented by wrapping torch.Tensor similar to BindsNET.How to Contribute?Feel free to create issues/pull-requests if you have any issues.Please readCONTRIBUTING.mdif you are interested in contributing the project.PyPIpython setup.py bdist_wheelpython -m twine upload dist/*ContributorsReferences[1] Bohte, S. M., J. N. Kok, and H. L. Poutre, Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing, 48(1-4), 17-37 (2002)[2] Diehl, P. U. and M. Cook, Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Frontiers in computational neuroscience, 9, 99 (2015)[3] Hunsberger, E. and C. Eliasmith, Spiking Deep Networks with LIF Neurons, arXiv preprint arXiv:1510.08829 (2015)[4] Tavanaei, A. and A. Maida, BP-STDP: Approximating backpropagation using spike timing dependent plasticity, Neurocomputing, 330, 39-47 (2019)[5] Wu, Y., L. Deng, G. Li, J. Zhu, and L. Shi, Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks, Frontiers in neuroscience, 12, 331 (2018)[6] Hazan, H., D. J. Saunders, H. Khan, D. Patel, D. T. Sanghavi, H. T. Siegelmann, and R. Kozma, BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python, Frontiers in neuroinformatics, 12, 89 (2018)
Package: n3th0us3
N3th0us3N3th0us3is unofficial python package fordomains.nethouse.ru/domainsservice.DescriptionServicedomains.nethouse.ru/domainslets you check domain name across multiple top-level domains (citation: "checking the domain for occupancy, check whether the domain is occupied in all zones").But this checking is sequential, one by one. Ohhh...N3th0us3:implements domain name checking functionlets to make it faster with multiprocess usage, not sequentiallets to make it at a time for multiple domains namesgives you interface for pythonYou can useN3th0us3for print result data in console or saving it at file or database.
Just realize this business logic at your callback function.Install$pipinstalln3th0us3ExampleNative usage:fromn3th0us3importpooled_domains_infodefyour_callback_for_each_sub_request(data):print(data)if__name__=='__main__':pooled_domains_info(['first-domain','second-domain',],processes_count=16,callback_method=your_callback_for_each_sub_request)or low-level usage ofN3th0us3in your script with command line usage:python3your_script.pyfirst-domainsecond-domainthird-domain-p10-tldrusucom# file: your_script.pyimportmultiprocessingfromn3th0us3.domainsimport(TLD,formatted_print,get_domain_info)if__name__=='__main__':fromargparseimportArgumentParserparser=ArgumentParser()parser.add_argument("domain_name",nargs='+',help="space separated domains names list")parser.add_argument("-p",dest="processes_count",default=4,type=int,help="count of processes")parser.add_argument("-tld",dest="tld",nargs='+',default=TLD,help="space separated top-level domains")args=parser.parse_args()pool=multiprocessing.Pool(processes=args.processes_count)fordomain_nameinargs.domain_name:fortldinargs.tld:pool.apply_async(get_domain_info,args=(domain_name,tld),callback=formatted_print)pool.close()pool.join()result is:third-domain.su {'result': {'error': 'empty', 'domain': 'third-domain.su', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
third-domain.com {'result': {'error': 'empty', 'domain': 'third-domain.com', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
first-domain.com {'result': {'error': 'occupy', 'domain': 'first-domain.com', 'errrr': '', 'error_message': 'Домен занят', 'type_error': 'busy'}}
first-domain.su {'result': {'error': 'empty', 'domain': 'first-domain.su', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
third-domain.ru {'result': {'error': 'empty', 'domain': 'third-domain.ru', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
second-domain.com {'result': {'error': 'occupy', 'domain': 'second-domain.com', 'errrr': '', 'error_message': 'Домен занят', 'type_error': 'busy'}}
first-domain.ru {'result': {'error': 'empty', 'domain': 'first-domain.ru', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
second-domain.ru {'result': {'error': 'empty', 'domain': 'second-domain.ru', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}
second-domain.su {'result': {'error': 'empty', 'domain': 'second-domain.su', 'errrr': '', 'is_premium_name': '', 'premium_price': ''}}To be continue...
Package: n4s
When you feel the need...for an awesome Python package!Documentation•Installation•ContributingInstallationpipinstalln4sWhat is n4s?This library isn't going to revolutionize how you use Python, but it will save you some money on hair loss treatments and ibuprofen. This package is an amalgamation of years spent writing Python code and trying to simplify certain common tasks to make them far more readable.Whether you're new to Python or a veteran snake charmer, n4s is here to make your code cleaner, more intuative and easier to write.Why use n4s?Because writing Python code shouldn't be hard or tedious - regardless of experience and skill level.
This library expedites basic Python functionality, so you can focus on the more complex modules of your applications. This will help you save dev time, make your code cleaner and elminiate redundancy.ContributingIf you would like to contribute, please feel free to open an issue or pull request. Or, if you have any feedback or suggestions, pleasecontact medirectly. I would be happy to hear from you!
Package: n64tex
N64Tex-PythonAn image converter for N64 image formats written in PythonDescriptionThis library is a Python implementation ofIsotarge's N64Tex ToolThe goal is to have an OS-agnostic tool for converting between N64 image formats, fill in the
missing CI image formats, and have a Python-importable library to function with myDK64-Lib ProjectInstallationThis package can be installed from PyPi usingpippipinstall--upgraden64texUsageThe package can be used in one of two ways. Either with the bundle CLI tool, or through
a Python script itself.CLIFrom ImageConvert from an RGBA image to RGBA5551n64texrgba_image.pngrgba5551-orgba5551_image.png# Produces rgba5551_image.pngOptionally, you can also write a raw bytes filen64texrgba_image.pngrgba5551-orgba5551_image.png--write_bytes# Produces rgba5551_image.png and rgba5551_imageConverting to a CI format will provide an accompanying palette fileFrom bytesYou can also give a byte-like file to convert to an image, but the format must be specifiedConverting from RGBA5551 to RGBAn64texrgba5551_bytesrgba5551rgba-orgba_image.png# Produces rgba_image.pngConverting from CI bytes requires a bytes-like palette to be providedn64texci8_bytesci8rgba-orgba_image.png--palettepalette_ci8_bytesPythonOpen an image and convert it to other formatsfromPILimportImagefromn64tex.formatsimportRGBAImageimage=Image.open('rgba_image.png')rgba_image=RGBAImage.from_image(image)rgba5551_image=rgba_image.to_rgba5551()i8a_image=rgba_image.to_i8a()ci8_image=rgba_image.to_ci8()# Any of these objects can be saves to an image file using the .save() methodrgba5551_image.save('rgba5551_image.png')i8a_image.save('i8a_image.png')ci8_image.save('ci8_image.png')Open a bytes-like image and convert it to other formats or save it as an image filefromn64tex.formatsimportRGBA5551Image,CI8Imagewithopen('rgba5551_bytes','rb')asfil:rgba5551_image=RGBA5551Image.from_bytes(fil.read(),width=32,height=32)rgba5551_image.save('rgba5551_image.png')# CI file formats require a palettewithopen('ci8_bytes','rb')asfil,open('palette_ci8_bytes','rb')aspalette_fil:ci8_image=CI8Image.from_bytes(fil.read(),width=32,height=32,palette_bytes=palette_fil.read())ci8_image.save('ci8_image.png')
Package: n6py
n6py⚡ AI Power ToolsPython Power Tools for Scientific Computing, Machine Learning and Deep Learning.Read the Docs to Learn More.Try OnlineClick one of the icons to start with a fresh copy ofn6pyin an online environment.ColabBinderKaggleFeatures🐍 Python - Utility library for AI Python projects.📃 Jupyter Notebooks - Designed for use inside Jupyter Notebooks but also works in regular .py files.☁️ Cloud - Notebook Cloud Environment compatible e.g. Google Colab.👶 Simple - Dive right in by adding n6py package to your project via pip.📦 Modular - Pick and choose which modules to import to suit your needs.🎲 Framework Agnostic - Use n6py with many common frameworks - NumPy, Pandas, etc ...Installationpipinstalln6pyImportimportn6pyasn6ContributionSeeContributing Guide.LicenseMIT
Package: n7gg
n7ggURL service, you probably don't want it.Listens on :4242, and vends redirects or text responses for a static set
of keys, and 404's for everything else. Updating the set of known keys
currently requires updatingapp.pyand restarting the service.May someday read keys from a config file at startup maybe. /shrugUsageUse the script:$ ./run.shAlternately, edit and installn7gg.servicewith systemd to execrun.sh.Licensen7gg is copyrightJohn Reese, and licensed under
the MIT license.  I am providing code in this repository to you under an open
source license.  This is my personal repository; the license you receive to
my code is from me and not from my employer. See theLICENSEfile for details.