Saravanan666/pypi-org huggingface dataset:
Package: aerofiles
waypoint, task, tracklog readers and writers for aviationThis is a python library to read and write many important file formats
for aviation. It is compatible with python 3.0 (and newer) and
2.6. Please read the documentation underhttps://aerofiles.readthedocs.iofor further information.FeaturesFlarmconfiguration file writer
(aerofiles.flarmcfg)IGCfile reader and writer (aerofiles.igc)OpenAirfile
reader (aerofiles.openair)SeeYouCUP file reader and
writer (aerofiles.seeyou)WELT2000file reader
(aerofiles.welt2000)XCSoartask file writer (aerofiles.xcsoar)Development EnvironmentIf you want to work on aerofiles you should install the necessary dependencies
using:$ pip install -r requirements-dev.txtYou can run the testsuite with:$ make testBuilding DocsMake sure that you have checked out git submodules:$ git submodule update --initThen build docs using Sphinx and Make:$ cd docs
$ make htmlThe HTML output can be found in the_build/htmldirectory.LicenseThis code is published under the MIT license. See theLICENSEfile
for the full text.
Package: aerofoils
AerofoilsAerofoil Design ToolkitFree software: MIT licenseDocumentation:https://aerofoils.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-05-09)First release on PyPI.
Package: aeroframe
AeroFrame(Aeroelastic Framework) is a modular framework for partitioned aeroelastic analyses. The framework couples separate solvers for structure and CFD. It coordinates the analysis and the exchange of loads and deformations. Currently, AeroFrame supportsstaticaeroelastic analyses.Documentationhttps://aeroframe.readthedocs.io/LicenseLicense:Apache-2.0
Package: aerokit
AeroKitPython packages for compressible flow computationsFeaturesMach dependent functions for isentropic total pressure, temperature and mass flowlocal Rankine-Hugoniot shock wave equations (or 2d planar shocks) and conical shock wavesFanno equations for momentum losses in a ductRayleigh equations for heating/cooling in a ductunsteady compressible 1D flows1D stability equations: Orr-Sommerfeld and linearized compressible inviscidapplications:nozzle flows(Riemann) shock tubeChannel incompressible flow stability (Poiseuille)degree based trigonometric functionsreal gaz and kerozen thermo functionsNewton iterative solve, ODE integration, Chebyshev spectral discretizationInstallation & usagepip install aerokitDocumentation is available onreadthedocs. More examples are available in the repository.Requirementsnumpy,scipyexamples are plotted usingmatplotlib
Package: aeromaps
AeroMAPS: Multidisciplinary Assessment of Prospective Scenarios for air transportAeroMAPS is a framework for performing Multidisciplinary Assessment of Prospective Scenarios for air transport. For
instance, it allows simulating and analyzing scenarios for reducing aviation climate impacts through various levers of
action. It is intended to become a sectoral Integrated Assessment Model (IAM) taking into account technological,
environmental, sociological, economic and other considerations. It aims to assess the sustainability of simulated air
transport transition scenarios on multiple criteria.AeroMAPS is licensed under theGPL-3.0license.Adocumentationis available for more details on AeroMAPS.Quick startFor a quick start in order to discover the different features of AeroMAPS,
a graphical user interface has been developed for facilitating the first uses.
It is available at the following address:https://aeromaps.isae-supaero.fr/Another solution is to use theBinder-hosted graphical user interface.Quick installationThe use of the Python Package Index (PyPI) is the simplest method for installing AeroMAPS.
More details and other solutions are provided in the documentation.Prerequisite: AeroMAPS needs at least Python 3.8.0.You can install the latest version with this command:$ pip install --upgrade aeromapsCitationIf you use AeroMAPS in your work, please cite the following reference. Other references are available in the
documentation.Planès, T., Delbecq, S., Salgas, A. (2023).
AeroMAPS: a framework for performing multidisciplinary assessment of prospective scenarios for air transport.
Submitted to Journal of Open Aviation Science.
Package: aeromet-py
Aeromet-PyInspired from python-metar, a library writed in Python language to parse Meteorological Aviation Weather Reports (METAR and SPECI).This library will parse meteorological information of aeronautical land stations.
Supported report types:METARSPECITAFCurrent METAR reportsThe current report for a station is available at the URLhttp://tgftp.nws.noaa.gov/data/observations/metar/stations/<station>.TXTwherestationis the ICAO station code of the airport. This is a four-letter code.
For all stations at any cycle (i.e., hour) in the last  hours the reports are available
at the URLhttp://tgftp.nws.noaa.gov/data/observations/metar/cycles/<cycle>Z.TXTwherecycleis the 2-digit cycle number (00to23).UsageA simple usage example:fromaeromet_pyimportMetarcode='METAR MROC 071200Z 10018KT 3000 R07/P2000N BR VV003 17/09 A2994 RESHRA NOSIG'metar=Metar(code)# Get the type of the reportprint(f"{metar.type}")# Meteorological Aerodrome Report# Get the wind speed in knots and direction in degreesprint(f"{metar.wind.speed_in_knot}kt")# 18.0 ktprint(f"{metar.wind.direction_in_degrees}°")# 100.0°# Get the pressure in hecto pascalsprint(f"{metar.pressure.in_hPa}hPa")# 1014.0 hPa# Get the temperature in Celsiusprint(f"{metar.temperatures.temperature_in_celsius}°C")# 17.0°CContributingContributions are what make the open source community such an amazing place to learn, inspire, and create.
Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request.
You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestRoadmapAdd parsers for TAF and METAR reportsAdd functions to verificate the TAF with the observationsAdd a CLI API to interact with the verification functionsAdd parser for SYNOPTIC reportsAdd functions to verificate reports with rules ofAnnex 3Multi-language SupportPortugueseSpanishFeatures and bugsPlease file feature requests and bugs at theissue tracker.Current SourcesThe most recent version of this package is always available via git, only run the
following command on your terminal:git clone https://github.com/TAF-Verification/aeromet-py.gitAuthorsThepython-metarlibrary was originaly authored byTom Pollardin january 2005.
This packageaeromet-pyfor is inspired from his work in 2021 byDiego Garro.VersioningThis project usesBump2versiontool for versioning, so, if you fork this
repository remember install it in your environment.pip install bump2versionLicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: aeromiko
Aeromiko is a middle-man script to simplify extracting data from Aerohive APs using NetmikoInstallationTo install Aeromiko, simply use pip:$ pip install AeromikoAeromiko has the following requirements (which pip will install for you)netmiko >= 2.4.0Documentationhttps://andytruett.github.io/aeromiko/Usageimport aeromiko

ip = 127.0.0.1
username = "admin"
password = "password"

access_point = aeromiko.AP(ip, username, password)

access_point.connect()

hostname = access_point.get_hostname()
print(hostname)-or-see example script
Package: aeroport
*This preview is pre-beta, pre-alpha, pre-anything, so its completely unsuseful in real projects.
More work is on the way.*Organize hub for data arrival / departuresIdea is to build server for running scraping jobs from multiple sources and input formats (web sites,
data feeds, etc…), adapt data and deliver it further to destinations (e. g. processing queues).So it’s like an airport where each airline “declares” its own arrivals, destinations and transport,
while using common hub insfrastructure.
Package: aeropress
aeropressis a CLI program for deploying Docker images to AWS ECS. It receives a folder path that includes
ECS task and service definitions and then does the jobs respectively;Register ECS task definitionsCreate Cloudwatch metrics for scaling policiesCreate or update scaling policies for ECS servicesCreate or update alarms on CloudwatchCreate or update ECS servicesInstallationaeropressworks with Python3.pip3 install aeropressUsage$ aeropress --help
usage: cli.py [-h] [--logging-level {debug,info,warning,error}] [--version]
              {deploy,clean} ...

aeropress AWS ECS deployment helper

positional arguments:
  {deploy,clean}        sub-command help
    deploy              Deploy docker image to ECS.
    clean               Clean commands for stale entitites on AWS.

optional arguments:
  -h, --help            show this help message and exit
  --logging-level {debug,info,warning,error}
                        Print debug logs
  --version             show program's version number and exitExampleYou must have defined an ECS cluster first. Then, you can define ECS tasks and services in a yaml file and runaeropresswith required arguments.aeropress deploy --path 'example/foo.yaml' --image-url 'registry.hub.docker.com/library/python' --service-name service-foo
Package: aerospike-rest
Python interface to the Aerospike REST Client.Provides a simple convenience wrapper aroundrequestsfor using theAerospike REST Clientin Python.Enable/disable compressionEnable/disable authentication (via Authorization header)Override default user-agent headerOverride default connect and read timeoutsMake use of keep-alive (for lifetime of object)Raise exceptions with Aerospike error codesSimple Examplefromaerospike_rest.apiimportAerospikeRestApiapi=AerospikeRestApi('http://localhost:8080/v1')bins={'mybin':"Hello World!"}api.post('/kvs/mynamespace/myset/mykey',bins)Advanced Examplefromaerospike_rest.apiimportAerospikeRestApifromaerospike_rest.exceptionsimportAerospikeRestApiErrorapi=AerospikeRestApi('http://localhost:8080/v1')api.http_compression=Falseapi.client_compression=Trueapi.authorization='Authorization: Basic YWRtaW46YWRtaW4='bins={'mybin':"Hello World!"}params={'recordExistsAction':"CREATE_ONLY"}headers={'X-Custom-Header':'hello'}try:api.post('/kvs/mynamespace/myset/mykey',bins,params,headers,timeout=10)exceptAerospikeRestApiErroraserr:iferr.code==KEY_EXISTS_ERROR:passelse:raiseerrTestRun unit tests from the root directory:python -m unittest -v -bView test coverage from root directory:coverage run --source=aerospike_rest/ -m unittest -v -b && coverage reportReleaseCreate version branch:git checkout -b version/v1.0.0)Bump version inaerospike_rest/__init__.pyand commit the changeTag the commit:git tag -a v1.0.0 -m 'Release v1.0.0Submit PR
Package: aerostat
aerostatexports tenant data from an OpenStack cloud to create a set
of Ansible playbooks for importing the data into another cloud.Installing and UsingThe project is in a very very early prototyping stage.aerostat usesos-client-configfor settings related to accessing
the cloud. Fill in yourclouds.yamlor use the environment
variables or command line arguments provided.Withtoxinstalled, experiment via:$ tox -e venv -- aerostatFree software: Apache licenseSource:http://git.openstack.org/cgit/openstack/aerostat
Package: aerostat-launcher
AerostatA simple CLI tool to deploy your Machine Learning models to cloud, with public API and template connections ready to go.Get startedInstallationThe nameAerostathas been used by another PyPI project, please install this package with:pipinstallaerostat-launcherOnce installed, it can be used directly viaaerostat. If it doesn't work, addpython -mprefix to all commands,
i.e.python -m aerostat deploy.Only three commands needed for deploying your model:install,login, anddeploy.SetupRun the following command to install all the dependencies needed to run Aerostat. Please allow installation in the pop-up window to
continue.aerostatinstallTo login to Aerostat, you need to run the following command:aerostatloginYou will be prompted to choose an existing AWS credentials, or enter a new one. The AWS account used needs to haveAdministratorAccess.DeployTo deploy your model, you need to dump your model to a file with pickle, and run the following command:aerostatdeployYou will be prompted to enter:the path to your model filethe input columns of your modelthe ML library used for your modelthe name of your projectOr you can provide these information as command line options like:aerostatdeploy--model-path/path/to/model--input-columns"['col1','col2','col3']"--python-dependenciesscikit-learn--project-namemy-projectConnectionsAerostat provides connection templates to use your model in various applications once it is deployed. Currently, it includes templates for:Microsoft ExcelGoogle SheetsPython / Jupyter NotebookVisit the URL produced by theaerostat deploycommand to test your model on cloud, and get the connection templates.Other CommandsListTo list all the projects you have deployed, run:aerostatlsInfoTo find deployment information of a specific project, such as API endpoint, run:aerostatinfothen choose the project from the list. You can also provide the project name as a command line option like:aerostatinfomy-projectFuture RoadmapImprove user interface, including rewrite prompts with Rich, use more colors and emojisAdd unit testsAdoptSemantic Versioningonce reach v0.1.0 and add
CI/CDSupport SSO loginSupport deploying to GCP
Package: aeroutils
AboutThis package was rebranded in April 2022 toflightconditionand is nowDEPRECATED.
All functionality is pulled from the newer package, so it is better to useflightconditionexplicitly.AuthorMatthew C. Jones <matt.c.jones.aoe@gmail.com>InstallationInstall CommandsInstall using thepippackage-management system.  The easiest method is to
open the terminal and run:pipinstallaeroutilsAlternatively, manually download thesource code, unpack, and run:pipinstall<path/to/aeroutils>Dependenciesnumpy: widely-used package for scientific computing.pint: package for dealing with units.UsageSeeflightconditionfor
up-to-date documentation, since this package is nowDEPRECATED.fromaeroutilsimport*Licenseaeroutilsis licensed under the MIT LICENSE. See theLICENSEdocument.DisclaimerThe software is provided “as is”, without warranty of any kind, express or
implied, including but not limited to the warranties of merchantability,
fitness for a particular purpose and noninfringement. In no event shall the
authors or copyright holders be liable for any claim, damages or other
liability, whether in an action of contract, tort or otherwise, arising from,
out of or in connection with the software or the use or other dealings in the
software.
Package: aerovane
**This is a very early development version. While it technically works, much work needs to be done to betterorganize the code and implement additional features. See "To-do" below.**About Aerovane==============_**Aerovane:**_ _A device commonly used at many weather stations and airports to measure both wind direction andspeed._Aerovane is a simple CLI for generating a weather report in your terminal. Aerovane requires (free) API keys fromOpenWeatherMap and ipstack. If you do not currently have the API keys, you can register for them here:* **OpenWeatherMap:** https://openweathermap.org/* **ipstack:** https://ipstack.com/Installation============$ pip install aerovaneUsage=====$ aerovane$ aerovane --location 'Los Angeles, US'$ aerovane --units imperialTo-do====
Package: aertb
AER-toolboxThis library intends to be a minimal tool for loading events from files with common event-camera file extensions into
Python.See the project onPyPIor dopip3 install aertbUsagefromaertb.coreimportFileLoaderdatLoader=FileLoader('dat')# 'bin', or 'aedat'datLoader.load_events('../example_data/dat/cars/obj_004414_td.dat')Supported extensions:.dat: N-Cars / Prophesee Cameras.bin: N-MNIST, N-Caltech101.aedat: PokerDVS.mat: DVS-BarrelIt also make the process of loading and iterating HDF5 files easier.fromaertb.coreimportHDF5Filedataset_train=HDF5File('TRAIN.h5')train_iterator=dataset_train.iterator(n_samples_group=10,rand=23)forsampleintqdm(train_iterator):# do something with sample.events, sample.label or sample.nameExample: making a GIFfromaertb.coreimportHDF5File,make_giffile=HDF5File('../DVS_Barrel.hdf5')sample=file.load_events(group='moving',name='11')make_gif(sample,filename='sample_moving.gif',camera_size=(128,128),n_frames=480,gtype='std')The library also includes a command line interface for converting files from a given extension to hdf5, as well as gif
making capabilities for easy visualisation of the files.Opening the CLIIf the install with pip worked perfectly, you can now typeaertbin a terminal window and the CLI will open.If you are installing it from Github: download you should download the project from github and follow the following
instructions:a)git clone ...b)  Create a virual environment, if venv is not installed runpip install virtualenv,
thenpython3 -m venv aertb_envc)  Runsource aertb_env/bin/activated)  Run the following command:pip install -r requirements.txte)  Open the cli withpython3 .or with the__main__.pyfileUsing the CLIOnce the CLI is open you get a a similar output on your terminal:typehelpto see supported commands andhelp <topic>to get more info of the commandExamples:Creating an HDF5 out of a directorytohdf5 -f 'example_data/dat' -e 'dat' -o 'mytest.h5'The recommended directory shape is  :|--Parent (given as parameter)
      |-- LabelClass1
          |-- SampleName1
          |-- SampleName2
          |-- ....
      |-- LabelClass2
          |-- SampleName1
          |-- SampleName2
          |-- ....
      |-- ...And we suggest that train and test are kept as separate folders so they translate
to two different filesCreating an HDF5 out of a single filetohdf5 -f 'example_data/bin/one/03263.bin' -o 'mytest2.h5'Creating a gif out of a given filemakegif -f 'example_data/prophesee_dat/test_23l_td.dat' -o 'myGif.gif' -nfr 240 -g 'std'Exiting the CLI:typequitExit virtual environment:$ deactivate
Package: aes
AES; Advanced Encryption StandardA simple package for Advanced Encryption Standard(AES) Block Cipher [pdf]Version 1.2.0 is available. In this version, AES-128, 192, 256 with ECB, CBC, CTR mode are now supported!InstallYou can easily install from PyPI.$pipinstallaesAfter installation, open your python console and typefromaesimportaesc=aes(0)print(c.dec_once(c.enc_once(0)))# print(c.decrypt(c.encrypt(0))) # for old versionIf you get list of zeros, you are now ready to useaespackage!Out[1]:[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]Get StartedWhen a mode of operation is not necessary, just use enc_once/dec_once like:importaesmk=0x000102030405060708090a0b0c0d0e0fpt=0x00112233445566778899aabbccddeeffcipher=aes.aes(mk,128)ct=cipher.enc_once(pt)print(ct)print("0x"+hex(aes.utils.arr8bit2int(ct))[2:].zfill(32))pr=cipher.dec_once(ct)print(pr)print("0x"+hex(aes.utils.arr8bit2int(pr))[2:].zfill(32))Out[1]:[105,196,224,216,106,123,4,48,216,205,183,128,112,180,197,90]Out[2]:0x69c4e0d86a7b0430d8cdb78070b4c55a
Out[3]:[0,17,34,51,68,85,102,119,136,153,170,187,204,221,238,255]Out[4]:0x00112233445566778899aabbccddeeffJust want to use core functions:# example of using aes core functionmk_arr=aes.utils.int2arr8bit(mk,16)pt_arr=aes.utils.int2arr8bit(mk,16)rk_arr=aes.core.key_expansion(mk_arr,128)ct_arr=aes.core.encryption(pt_arr,rk_arr)print("0x"+hex(aes.utils.arr8bit2int(ct_arr))[2:].zfill(32))pr_arr=aes.core.decryption(ct_arr,rk_arr)print("0x"+hex(aes.utils.arr8bit2int(pr_arr))[2:].zfill(32))Out[1]:0x0a940bb5416ef045f1c39458c653ea5a
Out[2]:0x000102030405060708090a0b0c0d0e0fWith the mode of opearation:# example of using mode of operationmk=0x000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1fmk_arr=aes.utils.int2arr8bit(mk,32)pt=0x00112233445566778899aabbccddeeffpt_arr=aes.utils.int2arr8bit(pt,16)cipher=aes.aes(mk,256,mode='CTR',padding='PKCS#7')# notice: enc/dec can only 'list'  !!ct_arr=cipher.enc(pt_arr)print("0x"+hex(aes.utils.arr8bit2int(ct_arr))[2:].zfill(32))pr_arr=cipher.dec(ct_arr)print("0x"+hex(aes.utils.arr8bit2int(pr_arr))[2:].zfill(32))Out[1]:0xf235e46425db35cb300a528fbbe62697a55ca80972eb579044d786243219d7af
Out[2]:0x00112233445566778899aabbccddeeffIt is great! But, if you didn't input the initial vector for 'CBC', 'CTR' mode, you getWarning:/usr/local/lib/python3.7/dist-packages/aes/utils/_check_tools.py:59:UserWarning:InitailVectorisrandomlyselected:[23,202,118,211,113,65,4,46,115,56,211,200,177,24,127,186]warnings.warn("Initail Vector is randomly selected: "+str(iv))Don't forget to take the IV.print(cipher.iv)# save it!Version Summaryv1.0.0v1.0.1Bug reported "ModuleNotFoundError", and fixed in this version.v1.2.0Added AES-192, 256 and CBC, CTR mode.Report a bug toDonggeun Kwon (email)
Package: aesara
Aesara is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top ofNumPy. Aesara features:tight integration with NumPy:a similar interface to NumPy’s. numpy.ndarrays are also used internally in Aesara-compiled functions.efficient symbolic differentiation:Aesara can compute derivatives for functions of one or many inputs.speed and stability optimizations:avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.dynamic C code generation:evaluate expressions faster.extensive unit-testing and self-verification:includes tools for detecting and diagnosing bugs and/or potential problems.
Package: aesara-nightly
Aesara is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top ofNumPy. Aesara features:tight integration with NumPy:a similar interface to NumPy’s. numpy.ndarrays are also used internally in Aesara-compiled functions.efficient symbolic differentiation:Aesara can compute derivatives for functions of one or many inputs.speed and stability optimizations:avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.dynamic C code generation:evaluate expressions faster.extensive unit-testing and self-verification:includes tools for detecting and diagnosing bugs and/or potential problems.
Package: aesara-theano-fallback
aesara-theano-fallbackStriving towards backwards compatibility asTheanois replaced byAesaraby thePyMC3
project. The idea is to provide a nearly drop in
replacement for importingaesarathat will fall back ontotheanowhenaesarais not installed. This was specifically designed to support theexoplanetandstarryprojects so it might not support
all of the features that you need. If you find something that isn't supported,
please submit a pull request!InstallaionThis library can be installed using pip:python-mpipinstallaesara-theano-fallbackUsageThe syntax is designed to mostly followaesara, so things like the following will often work:importaesara_theano_fallback.tensorasaetFor top-level access, usefromaesara_theano_fallbackimportaesaraOne place where the syntax has changed significantly between Theano and Aesara
is thetheano.gofmodule was re-named toaesara.graphand the contents were
moved around a little bit. For exoplanet and starry, we define a few customOps
and you can use this library to do that as follows:fromaesara_theano_fallback.graphimportbasic,opclassMyPythonOp(op.Op):defmake_node(self,*args):# ...returnbasic.Apply(self,in_args,out_args)classMyCOp(op.ExternalCOp):func_file="./cpp_impl.cc"func_name="APPLY_SPECIFIC(my_op_name)"# ...