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aerouter | Place Holder! |
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 <[email protected]>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 โas isโ, 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. |
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==== |
aerozbot | No description available on PyPI. |
aerpy | No description available on PyPI. |
aert | Place Holder! |
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 |
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) |
aes128 | No description available on PyPI. |
aes128-encrypt-emotibot | #Emotibot Encrypt PackageAES128ๅ ่งฃๅฏ็ๅทฅๅ
ทๅ
|
aes128-NAB | No description available on PyPI. |
aes256gcm-nacl | # aes256gcm-nacl
aes256 patch based on pynacl |
aesahaettr | Failed to fetch description. HTTP Status Code: 404 |
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 NumPyโs. 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. |
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 NumPyโs. 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. |
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)"# ... |
aescalante_nester | Failed to fetch description. HTTP Status Code: 404 |
aeschliman | No description available on PyPI. |
aescipher | AES CipherUse AES to encrypt everything with ease!Hierarchyaescipher
|---- AESCipherCBC()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCBCnoHASH()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCBCwoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCBCnoHASHwoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCTR()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCTRnoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCTRnoHASH()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCTRnoHASHnoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherGCM()
| |---- encrypt_and_digest()
| '---- decrypt_and_verify()
|---- AESCipherGCMnoHASH()
| |---- encrypt_and_digest()
| '---- decrypt_and_verify()
|---- AESCipherGCMwoIV()
| |---- encrypt_and_digest()
| '---- decrypt_and_verify()
|---- AESCipherGCMnoHASHwoIV()
| |---- encrypt_and_digest()
| '---- decrypt_and_verify()
|---- AESCipherGCMSTREAM()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherGCMSTREAMnoHASH()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherGCMSTREAMwoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherGCMSTREAMnoHASHwoIV()
| |---- encrypt()
| '---- decrypt()
|---- AESCipherCTRFileEncReader()
| |---- name
| |---- fp
| |---- fo
| |---- seek()
| |---- read()
| |---- write()
| |---- close()
| '---- tell()
|---- AESCipherCTRFileEncWriter()
| |---- name
| |---- fp
| |---- fo
| |---- seek()
| |---- read()
| |---- write()
| |---- close()
| '---- tell()
|---- AESCipherCTRFileDecReader()
| |---- name
| |---- fp
| |---- fo
| |---- seek()
| |---- read()
| |---- write()
| |---- close()
| '---- tell()
'---- AESCipherCTRFileDecWriter()
|---- name
|---- fp
|---- fo
|---- seek()
|---- read()
|---- write()
|---- close()
'---- tell()ExamplepythonSeetest. |
aes-cipher | AES CipherIntroductionAES cipher is a library to encrypt/decrypt using AES256-CBC. It is possible to encrypt/decrypt both files and raw data (string or bytes).How it worksA master key and IV are derived from the given password and (optionally) salt using a key derivation function. The key derivation function can be chosen among the provided ones:PBKDF2-SHA512ScryptAlternatively, it can also be customized by the user by simply implementing theIKeyDerivatorinterface.Salt is randomly generated (16-byte long) if not specified.A random key and IV are generated and used to encrypt the actual data. In this way, if the same file is encrypted with the same password multiple times, the encrypted file will always be different.The random key and IV are encrypted with the master key and IVHMAC-SHA256 of the encrypted key/IV and file data is computed to ensure integrity, using the master key as keyIt is possible to specify either a single password or a list of passwords.
In the last case, the file will be encrypted multiple times with a different password and salt each time.InstallationThe package requires Python >= 3.7.To install it:Usingsetuptools:python setup.py installUsingpip:pip install aes_cipherTest and CoverageInstall develop dependencies:pip install -r requirements-dev.txtTo run tests:python -m unittest discoverTo run tests with coverage:coverage run -m unittest discover
coverage reportAPIsKey derivationPbkdf2Sha512class: derive keys using PBKDF2-SHA512 algorithmPbkdf2Sha512(itr_num): construct the classitr_num: iterations numberFor default values, thePbkdf2Sha512Defaultclass instance can be used:Pbkdf2Sha512Default = Pbkdf2Sha512(512 * 1024)Scryptclass: derive keys using Scrypt algorithmScrypt(n, p, r): construct the classn: CPU/Memory cost parameterp: block size parameterr: parallelization parameterFor default values, theScryptDefaultclass instance can be used:ScryptDefault = Scrypt(16384, 8, 8)To add a custom key derivation function, theIKeyDerivatorinterface shall be implemented:class IKeyDerivator(ABC):
@abstractmethod
def DeriveKey(self,
password: Union[str, bytes],
salt: Union[str, bytes]) -> bytes:
passThe only requirement is that the output of theDeriveKeymethod is at least 48-byte long.A constructor can be added to customize the algorithm.EncryptionDataEncrypterclass: encrypt bytes or string dataDataEncrypter(key_derivator): construct the classkey_derivator: key derivator to be used for master key and IV generation, it shall be an instance of theIKeyDerivatorinterface. Default value:Pbkdf2Sha512Default.DataEncrypter.Encrypt(data, passwords [, salts]): encrypt data with the specified passwords and saltsdata: input data (string or bytes)passwords: list of passwords (list of strings)salts(optional): list of salts (list of strings). The number of salts shall be the same of the passwords. If not specified, salts will be randomly generated (16-byte long).DataEncrypter.GetEncryptedData(): get encrypted data (bytes)FileEncrypterclass: encrypt a fileFileEncrypter(key_derivator): construct the classkey_derivator: seeDataEncrypterconstructorFileEncrypter.Encrypt(file_in, passwords [, salts]): encrypt file with the specified passwords and saltsfile_in: input filepasswords: seeDataEncrypter.Encryptsalts: seeDataEncrypter.EncryptFileEncrypter.GetEncryptedData(): get encrypted data (bytes)FileEncrypter.SaveTo(file_out): save to filefile_out: output file to be savedDecryptionDataDecrypterclass: decrypt bytes or string dataDataDecrypter(key_derivator): construct the classkey_derivator: key derivator to be used for master key and IV generation, it shall be an instance of theIKeyDerivatorinterface. Default value:Pbkdf2Sha512Default.DataDecrypter.Decrypt(data, passwords): decrypt data with the specified passwordsdata: input data (string or bytes)passwords: seeDataEncrypter.EncryptDataDecrypter.GetDecryptedData(): get decrypted data (bytes)FileDecrypterclass: decrypt a fileFileDecrypter(key_derivator): construct the classkey_derivator: seeDataDecrypterconstructorFileDecrypter.Decrypt(file_in, passwords)file_in: input filepasswords: seeDataDecrypter.DecryptFileDecrypter.GetDecryptedData(): get decrypted data (bytes)FileDecrypter.SaveTo(file_out): save to filefile_out: output file to be savedExamplesData encryption with single password and random salt, using PBKDF2-SHA512 algorithm with default values:data_encrypter = DataEncrypter(Pbkdf2Sha512Default)
data_encrypter.Encrypt(data, "test_pwd")
enc_data = data_encrypter.GetEncryptedData()Data encryption with single password and custom salt, using PBKDF2-SHA512 algorithm with custom values:data_encrypter = DataEncrypter(
Pbkdf2Sha512(1024 * 1024)
)
data_encrypter.Encrypt(data, ["test_pwd"], ["test_salt"])
enc_data = data_encrypter.GetEncryptedData()Data encryption with multiple passwords and custom salts, using Scrypt algorithm with default values:data_encrypter = DataEncrypter(ScryptDefault)
data_encrypter.Encrypt(data, ["test_pwd_1", "test_pwd_2"], ["test_salt_1", "test_salt_2"])
enc_data = data_encrypter.GetEncryptedData()Data decryption with single password and random salt:data_decrypter = DataDecrypter()
data_decrypter.Decrypt(data, ["test_pwd"])
dec_data = data_decrypter.GetDecryptedData()Data decryption with multiple passwords and custom salts:data_decrypter = DataDecrypter()
data_decrypter.Decrypt(data, ["test_pwd_1", "test_pwd_2"], ["test_salt_1", "test_salt_2"])
dec_data = data_decrypter.GetDecryptedData()File encryption with single password and random salt:file_encrypter = FileEncrypter()
file_encrypter.Encrypt(file_in, "test_pwd")
file_encrypter.SaveTo(file_out)Enable logging:data_encrypter = DataEncrypter()
data_encrypter.Logger().SetLevel(logging.INFO)
data_decrypter = DataDecrypter()
data_decrypter.Logger().SetLevel(logging.INFO)Sample ApplicationA sample application based on the library using the PBKDF2-SHA512 algorithm can be found in theappfolder.Basic usage:python aes_cipher_app.py -m <enc:dec> -p <password1,password2,...> -i <input_files_or_folder> -o <output_folder> [-s <salt1,salt2,...>] [-t <itr_num>] [-v] [-h]Parameters description:Short nameLong nameDescription-m--modeOperational mode:encfor encrypting,decfor decrypting-p--passwordPassword used for encrypting/decrypting. It can be a single password or a list of passwords separated by a comma-i--inputInput to be encrypted/decrypted. It can be a single file, a list of files separated by a comma or a folder (in this case all files in the folder will be encrypted/decrypted)-o--outputOutput folder where the encrypted/decrypted files will be saved-s--saltOptional: custom salts for master key and IV derivation, random if not specified`-t--iterationOptional: number of iteration for the PBKDF2-SHA512 algorithm, default value: 524288-v--verboseOptional: enable verbose mode-h--helpOptional: print usage and exitNOTE:the password shall not contain spaces or commas (in this case it will be interpreted as multiple passwords)Examples:Encrypt a file one time with the given password and salt. Ifinput_fileis a folder, all the files inside the folder will be encrypted:python aes_cipher_app.py -m enc -p test_pwd -i input_file -o encrypted -s test_saltDecrypt the previous file:python aes_cipher_app.py -m dec -p test_pwd -i encrypted -o decrypted -s test_saltEncrypt multiple files one time with the given password and salt. If one of the input files is a directory, it will be discarded:python aes_cipher_app.py -m enc -p test_pwd -i input_file1,input_file2,input_file3 -o encrypted -s test_saltEncrypt a file 3 times using 3 passwords with random salts and custom number of iteration:python aes_cipher_app.py -m enc -p test_pwd_1,test_pwd_2,test_pwd_3 -t 131072 -i input_file -o encryptedDecrypt the previous file:python aes_cipher_app.py -m dec -p test_pwd_1,test_pwd_2,test_pwd_3 -t 131072 -i encrypted -o decrypted |
aes-ecb-pkcs5 | READMESimplepython3library built using only thestandard library. This library contains
utility methods for AES/ECB/PKCS5 encryption[to base64] and decryption[from base64].INSTALLATIONInstallaes-ecb-pkcs5via pip:$pipinstallaes-ecb-pkcs5AUTHORRishabh [email protected] forget stuff, this section is for anyone who wants to build the package.$pythonsetup.pysdist
$twineuploaddist/*LICENSEThis code falls under the MIT license which permits the reuse of the proprietary software
provided that all copies of the licensed software include a copy of the MIT License terms
and the copyright notice. Go crazy! |
aesedb | No description available on PyPI. |
aeseg | No description available on PyPI. |
aes-encrypt | No description available on PyPI. |
aesencrypter | AES-256 CBC Encrypter/Decrypter moduleAESEncrypter is a utility package that allows you to encrypt with AES-256 CBC mode, a characters string with an encryption phrase passed as an argument. It also includes the decryption function.RequirementsHave pycryptodome and pycryptodomex installedInstallationRun the following to install:pip install aesencrypterUsagefrom aesencrypter import EncryptString, DecryptStringplain_string = "Hello, World!"secret_key = "9ka87c30-9889-77a4-24-7ce56a47-6718-41a4-8677-52fe438d4f7b"e = EncryptString(plain_string, secret_key)d = DecryptString(e, secret_key)print("Plain String.......: " + plain_string)print("Encrypted String...: " + e)print("Decrypted String...: " + d) |
aes-encryption | AES EncryptionA python aes encryption libraryTable of ContentsInstallationUsageSupportContributingInstallationInstall using pippipinstallaes-encryptionUsagefromaes_encryptionimportAESCipherencryption_key:str='secret key'cipher:AESCipher=AESCipher(encryption_key)# this initializes the cipher with an encryption keymessage:str='secret message'encrypted:str=cipher.encrypt(message)# this is how you encrypt your messagedecrypted:str=cipher.decrypt(encrypted)# this is how you decrypt your messageSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request. |
aes-everywhere | AES Everywhere - Cross Language Encryption LibraryAES Everywhere is Cross Language Encryption Library which provides the ability to encrypt and decrypt data using a single algorithm in different programming languages and on different platforms.This is an implementation of the AES algorithm, specifically CBC mode, with 256 bits key length and PKCS7 padding.
It implements OpenSSL compatible cryptography with random generated saltPythonimplementationPython versions >= 2.7, < 3.8Installationpipinstallaes-everywhereUsagefromAesEverywhereimportaes256# encryptionencrypted=aes256.encrypt('TEXT','PASSWORD')print(encrypted)# decryptionprint(aes256.decrypt(encrypted,'PASSWORD')) |
aes-gcm-rsa-oaep | No description available on PyPI. |
aeshandler | AESHandlerAESHandler is a package that makes AES encryption and decryption easy.Usageimportaeshandler# The aeshandler module comes with a key_from_password() method in the AESHandler class.# This method takes in a password, and runs a KDF function to create a key.password=b'Password1234!'crypto_tuple=aeshandler.AESHandler.key_from_password(password)# Default KDF is Bcrypt'''OUTPUT: (b'password1234!', b'he0bB3t4OZtAqjQlv77QOq1LHg6wSeD9rNskEiAV5LsMYGDXS8rBkEPLDIeQNshf', b'\x14\xe8\xf0\xcf\xf1\x16\x9d\xb6J\x9b\xc0\xfe\xed\xd7\xe9\xd0\x82\x10scip\xea|L&\x81\xacH\xa8O\x0e')First element is the password, second element is the salt used with the KDF, third element is the derived key.'''crypto_key=crypto_tuple[-1]# b'\x14\xe8\xf0\xcf\xf1\x16\x9d\xb6J\x9b\xc0\xfe\xed\xd7\xe9\xd0\x82\x10scip\xea|L&\x81\xacH\xa8O\x0e''''AESHandler supports all AES modes. Such as CBC, CFB, XTS, and so on.Using CBC in this instance.This may require the padding=True argument for PKCS7 padding.use_encoding=True returns ciphertexts in base64 format.'''a=aeshandler.AESHandler(crypto_key,aeshandler.modes.CBC,padding=True,use_encoding=True)ciphertext=a.encrypt('Hello!')# M4ENqqe0m0ys4a7e1fnWHtJD+DbGNY5ckfbJBShxkJ0=print(ciphertext)print(a.decrypt(ciphertext))# b'Hello!' |
ae-sideloading-server | sideloading_server 0.3.11ae namespace module portion sideloading_server: sideloading server.installationexecute the following command to install the
ae.sideloading_server module
in the currently active virtual environment:pipinstallae-sideloading-serverif you want to contribute to this portion then first forkthe ae_sideloading_server repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_sideloading_server):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aesim.simba | SIMBA Python APIThe Simba Python Module (aesim.simba) is a Python package that contains hundreds of functions providing direct access to SIMBA such as creating a circuit, modifying parameters, running a simulation, and retrieving results. aesim.simba is independent and does not require to have SIMBA installed to be used.InstallationThe easiest way to install the Python API is using pip:pip install aesim.simbaRequirementsThe current version ofaesim.simbais compatible with Windows, macOS and Linux (64-bit).ActivationThe deployment key available on youraccount profile pagemust be used to activateaesim.simba. Two methods are available:Using Environment VariableThe easiest way to activate pysimba is to set the environment variableSIMBA_DEPLOYMENT_KEYvalue to your deployment key. To add a new environment variable in Windows:Open the Start Search, type in โenvโ, and choose โEdit the system environment variablesโ:Click the โEnvironment Variablesโฆโ button.Set the environment variables as needed. The New button adds an additional variable.Code-based ActivationTheLicenseAPI can be also used to activateaesim.simba.fromaesim.simbaimportLicenseLicense.Activate('*** YOUR DEPLOYMENT KEY ***')API DocumentationThe API documentation is availablehere.PerformanceRunning a simulation using the Python API is significantly faster than using the SIMBA User Interface because there is no overhead.Quick ExampleThe following example opens the Flyback Converter Example available in SIMBA, runs it, and plots the output voltage.#%% Load modulesfromaesim.simbaimportDesignExamplesimportmatplotlib.pyplotasplt#%% Load projectflybackConverter=DesignExamples.DCDC_Flyback()#%% Get the job object and solve the systemjob=flybackConverter.TransientAnalysis.NewJob()status=job.Run()#%% Get resultst=job.TimePointsVout=job.GetSignalByName('R2 - Instantaneous Voltage').DataPoints#%% Plot Curvefig,ax=plt.subplots()ax.set_title(flybackConverter.Name)ax.set_ylabel('Vout (V)')ax.set_xlabel('time (s)')ax.plot(t,Vout)# %%More ExamplesA collection of simple Python script examples using the SIMBA Python API is available on thisGitHub repositoryCopyright (c) 2019-2020 AESIM.tech |
aesir | AesirPrerequisitespython (3.8+)pipdockerGetting startedYou can useaesirsimply by installing viapipon your Terminal.pipinstallaesirAnd then you can begin deploying local cluster as such:aesirdeployThe initial deployment may take some time at pulling required images from their respective
repositories. Results may look as such:$pipinstallaesir
>...
>Installingcollectedpackages:aesir
>Successfullyinstalledaesir-0.3.5
$aesirdeploy
>Deployspecifiedlocalcluster:โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ100%0:00:01
>Generateaddresses:โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ100%0:00:00
>Mineinitialcapitalforparties:โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ100%0:00:00You will have docker containers running in the backend, ready to be interfaced by your local
environment applications you are developing.Begin local miningIn order to properly test many functionalities, you will need to send mining commands to local
setup. You can achieve completely local and running environment with the following command:$aesirmine
>โญโโโโโcontainersโโโโโโฎโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโณโโโโโโโโโโโโณโโโโโโโโโณโโโโโโโโโณโโโโโโโโโโ
>โaesir-redisโโNameโNodekeyโChannelsโPeersโHeightโSynced?โ
>โaesir-postgresโโกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ
>โaesir-pongโโaesir-pongโ02fabeeaa9dโ2โ1โ216โtrueโ
>โaesir-pingโโโ3da33d3eb12โโโโโ
>โaesir-bitcoindโโโ262e039d9b2โโโโโ
>โโโโ9d591f1b897โโโโโ
>โโโโc0ae6b158d0โโโโโ
>โโโโ5410d97efbcโโโโโ
>โโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโผโโโโโโโโโโโโผโโโโโโโโโผโโโโโโโโโผโโโโโโโโโโค
>โโโaesir-pingโ02ac17a8d64โ2โ1โ216โtrueโ
>โโโโ4194459b8f3โโโโโ
>โโโโdeacf4e1a64โโโโโ
>โโโโ0fcbcdf9fbfโโโโโ
>โโโโ39e8423dfdcโโโโโ
>โโโโ3ffa2f7367fโโโโโ
>โโโโโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโดโโโโโโโโโโโโดโโโโโโโโโดโโโโโโโโโดโโโโโโโโโโ
>โโโญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
>โโโChain:regtestBlocks:216Size:65259Time:1701528030โ
>โฐโโโโโโโโโโโโโโโโโโโโโโโฏโฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏCluster typesCurrently there are two supported cluster-types in this project. Specified by flags,--duo(default), or--unowith the following set-up:TypeDescriptionduoContains two LND nodes namedaesir-pingandaesir-pongunified byone singleaesir-bitcoindservice.unoOnly has one LND node namedaesir-lndconnected toaesir-bitcoind.Peripheral containersThis project also helps you setup peripheral services to make development process easier, too.
For example, if you want to deploy a duo-cluster with attached postgres database, run the following:$aesirdeploy--with-postgres
>...
$aesirmine
>โญโโโโโcontainersโโโโโโฎโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโณโโโโโโโโโโโโณโโโโโโโโโณโโโโโโโโโณโโโโโโโโโโ
>โaesir-postgresโโNameโNodekeyโChannelsโPeersโHeightโSynced?โ
>โaesir-pongโโกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ
>โaesir-pingโโaesir-pongโ3da33d3eb12โ2โ1โ216โtrueโ
>โaesir-bitcoindโโโdeacf4e1a64โโโโโ
>โ...โโ...โ...โ...โ...โ...โ...โOr run an uno-cluster with both attached postgres database and redis solid store cache like this:$aesirdeploy--uno--with-postgres--with-redis
>...
$aesirmine
>โญโโโโโcontainersโโโโโโฎโโโโโโโโโโโโโโโโโณโโโโโโโโโโโโโโโณโโโโโโโโโโโโณโโโโโโโโโณโโโโโโโโโณโโโโโโโโโโ
>โaesir-postgresโโNameโNodekeyโChannelsโPeersโHeightโSynced?โ
>โaesir-redisโโกโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฉ
>โaesir-lndโโaesir-lndโc0ae6b158d0โ0โ0โ202โtrueโ
>โaesir-bitcoindโโโ4194459b8f3โโโโโ
>โ...โโ...โ...โ...โ...โ...โ...โCleanupUse the following command to clean up activeaesir-*containers:aesirclean๐ง This will resets the current test state, so use with care. Example below:$aesirclean
>Removeactivecontainers:โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ100%0:00:01Change-logs0.3.1Addaesir-cashu-mint&aesir-lnd-krubimage setups and deployments w/ shared volumes0.3.2Define classifiers onpyproject.tomlfor PyPI metadata0.3.3Dropblackand useruffformatter and linter0.3.4Simplify deployment workflows and0.3.5Restructure project so that when installed,srcfolder will not be created0.3.6Breakdown "setup" command into "build" and "pull"0.3.7Lightning cluster now withord0.3.8Rename "ord" to "ord-server" to avoid confusion with cli0.3.9Remove intermediate containers0.4.0Resist electricity with "ohm" modeRoadmapMake image versioning a little bit more intuitive.Addaesir-tesla-ballperipheral service usingtesla-ballWriteclicktests.Usejoblibto speed up deployment with parallelization.Addaesir-bitvmperipheral service usingBitVMCreate and add some type ofordapiperipheral service.Implement dashboard walkthrough a lakylepollina/objexploreContributionsThis project usespoetrypackage manager to keep track of dependencies.
You can set up your local environment as such:pipinstall--userpoetryAnd then you can install development dependencies like so:$pipinstall--userpoetry
>...
$poetryinstall--withdev# install with development dependencies>Installingdependenciesfromlockfile
>
>Packageoperations:33installs,0updates,0removals
>
>โข...
>โข...
>โข...
>โข...
>
>Installingthecurrentproject:aesir(0.4.0)Known issuesYou may run into this setback when first running this project. This is adocker-pyissue widely known as of October 2022.docker.errors.DockerException:ErrorwhilefetchingserverAPIversion:('Connection aborted.',FileNotFoundError(2,'No such file or directory'))See the following issue for Mac OSX troubleshooting.docker from_env and pull is broken on macRecommended fix is to run the following command:sudoln-s"$HOME/.docker/run/docker.sock"/var/run/docker.sockLicenseThis project is licensed under the terms of the MIT license. |
aeskeyschedule | AES key schedule toolThis tool can be used as either a python library or a command line toolThis project is available on pypipip3 install aeskeyschedule --user --upgradeCommand Line Toolusage: aeskeyschedule [-h] [-r AES_ROUND] round_key
Tool to calculate the Rijndael key schedule given any AES-128 round key.
positional arguments:
round_key the round key in hex notation from which the full key
will be derived.
optional arguments:
-h, --help show this help message and exit
-r AES_ROUND, --round AES_ROUND
The AES round of the provided key. Defaults to 0 (base
key).Example UsageView the AES expanded key given the base key$ aeskeyschedule 00000000000000000000000000000000
0: 00000000000000000000000000000000
1: 62636363626363636263636362636363
2: 9b9898c9f9fbfbaa9b9898c9f9fbfbaa
3: 90973450696ccffaf2f457330b0fac99
4: ee06da7b876a1581759e42b27e91ee2b
5: 7f2e2b88f8443e098dda7cbbf34b9290
6: ec614b851425758c99ff09376ab49ba7
7: 217517873550620bacaf6b3cc61bf09b
8: 0ef903333ba9613897060a04511dfa9f
9: b1d4d8e28a7db9da1d7bb3de4c664941
10: b4ef5bcb3e92e21123e951cf6f8f188eReverse the AES-128 key schedule using the last round key$ aeskeyschedule --round 10 002a5e9033d14c1f03ed911164b9be02
0: aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
1: 07060606adacacac07060606adacacac
2: 94979793393b3b3f3e3d3d3993919195
3: 1116bd4f282d86701610bb4985812adc
4: 15f33bd83ddebda82bce06e1ae4f2c3d
5: 81821c3cbc5ca1949792a77539dd8b48
6: 60bf4e2edce3efba4b7148cf72acc387
7: b191596e6d72b6d42603fe1b54af3d9c
8: 48b6874e25c4319a03c7cf815768f21d
9: 163f231533fb128f303cdd0e67542f13
10: 002a5e9033d14c1f03ed911164b9be02Python LibraryThe two main functions arekey_scheduleandreverse_key_scheduleCalculate the AES-128 base key given the last round key:base_key=reverse_key_schedule(b'\xe2K\xbb"~\xe8\xb3\xe6u\x06_\xdb\x9b\xd6\x9bB',10)Calculate the last round key using an AES-128 base key:base_key=b'\x91\xa3\xba\x04\xe3\xdb:\x10\xc7$R\x15|]\xca\x87'expanded_key=key_schedule(base_key)assertexpanded_key[0]==base_keylast_round_key=expanded_key[10] |
aes-keywrap | AES keywrapimplementation of RFC 3394 AES key wrapping/unwrappinghttp://www.ietf.org/rfc/rfc3394.txtalso, alternative IV per RFC 5649http://www.ietf.org/rfc/rfc5649.txtThis is a symmetric key-encryption algorithm. It should only be used
to encrypt keys (short and globally unique strings.)In documentation, the key used for this kind of algorithm is
often called the KEK (Key-Encryption-Key), to distinguish
it from data encryption keys.usageimportbinasciifromaes_keywrapimportaes_wrap_key,aes_unwrap_keyKEK=binascii.unhexlify("000102030405060708090A0B0C0D0E0F")CIPHER=binascii.unhexlify("1FA68B0A8112B447AEF34BD8FB5A7B829D3E862371D2CFE5")PLAIN=binascii.unhexlify("00112233445566778899AABBCCDDEEFF")assertaes_unwrap_key(KEK,CIPHER)==PLAINassertaes_wrap_key(KEK,PLAIN)==CIPHERWhy a special key-encryption algorithm?In a word: size. By assuming keys are high enough
entropy to be globally unique, and small enough
not to require streaming encryption, aes-keywrap is able to avoid
an IV (initial value) or nonce that increases the size
of the ciphertext. This can be a significant
savings โ if the data being encrypted is a 32
byte AES-256 key, AES-GCM would result in a
60 byte ciphertext (87% overhead), AES-CTR or AES-CBC would result
in a 48 byte ciphertext (50% overhead) and would also not provide
authenticated encryption, but aes-keywrap
would result in a 32 byte ciphertext (no overhead).In an application where there are many keys being generated
and encrypted (e.g. a separate data encryption key for
each row in a database), this overhead can be significant.Another important use case is compatibility with
existing systems. |
aes-metadata-collector | Failed to fetch description. HTTP Status Code: 404 |
aesmix | DescriptionThis directory contains the python wrapper based on cffi and the command
line tool to use Mix&Slice on your files.The C implementation has been built with performance in mind, whereas
the python wrapper and the CLI tool has been implemented to offer a more
widespread access of the Mix&Slice capabilities. The mixing and slicing
phases use the C implementation, but the python conversion adds a big
overhead since it has to materialize all the buffers in memory.Since the tool materializes all the buffers in memory and has to perform
both the mixing and the slicing phases, you should only use the CLI tool
on files that are at maximum as large as a third of your available
memory.Please check the fileexample.pyto understand how to use the
library.RequirementsBefore proceeding please install theopenssl/cryptolibrary source.
In ubuntu you can proceed as follows:sudoaptinstalllibssl-devInstallationThe package has been uploaded toPyPIso, after installing the
requirements, you can install the latest released version using pip:pipinstallaesmixTo install the version from this repository, you can use the commands:makebuildsudomakeinstallTo install the package in a virtual environment, use:pythonsetup.pyinstallThe python wrapper will also compile thelibaesmixlibrary.Command Line InterfaceThis package also installs themixslicetool that can be used as
follows.To encrypt a file:$mixsliceencryptsample.txtINFO:[*]Encryptingfilesample.txt...INFO:Outputfragdir:sample.txt.encINFO:Publickeyfile:sample.txt.publicINFO:Privatekeyfile:sample.txt.privateTo perform a policy update:$mixsliceupdatesample.txt.encINFO:[*]Performingpolicyupdateonsample.txt.enc...INFO:Encryptingfragment#68INFO:DoneTo decrypt a file:$mixslicedecryptsample.txt.encINFO:[*]Decryptingfragdirsample.txt.encusingkeysample.txt.public...INFO:Decryptingfragment#68INFO:Decryptedfile:sample.txt.enc.dec$sha1sumsample.txtsample.txt.enc.decd3e92d3c3bf278e533f75818ee94d472347fa32asample.txtd3e92d3c3bf278e533f75818ee94d472347fa32asample.txt.enc.decKey regression mechanismThe key regression mechanism implementation is based onโKey
Regression: Enabling Efficient Key Distribution for Secure Distributed
Storageโ.ExampleThe key regression library can be used as follows.fromaesmix.keyregimportKeyRegRSAiters=5stp=KeyRegRSA()print("== WINDING ==")foriinrange(iters):stp,stm=stp.wind()print("k%i:%r"%(i,stm.keyder()))print("\n== UNWINDING ==")foriinrange(iters-1,-1,-1):print("k%i:%r"%(i,stm.keyder()))stm=stm.unwind() |
aesop | AESOP(A)nalysis of (E)lectrostatic (S)tructures (o)f (P)roteinsAuthors: Reed Harrison, Rohith Mohan, and Dimitrios MorikisFrameworkAESOP is a computational framework to explore electrostatic structures within proteins. The library depends on external tools including: APBS, PDB2PQR, Modeller, and ProDyAtomic SelectionsAll selection strings must be made according to the style of ProDy (http://prody.csb.pitt.edu/manual/reference/atomic/select.html)ExamplesAll materials for example cases are provided in the tests folderDocumentationHTML documentation provided within the docs folderDependenciesAPBS and PDB2PQRRequired Python libraries: numpy, scipy, prody, matplotlib, modeller, griddataformatsOptional Python libraries: multiprocessingMethodsAlascanPerform a computational alanine scan on a provided protein structure using a side-chain truncation schemeAssociation free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are providedUsers may restrict mutations to some region of the protein structureDirectedMutagenesisPerform a directed mutagenesis scan on a provided protein structure using Modeller to swap amino acidsAssociation free energies for mutatants (relative to the parent) may be predicted if 2 or more selection strings are providedMutations must be specifiedElecSimilarityCompare electrostatic potentials of multiple protein structuresIf structures are very dissimilar, the user should superpose coordinates for each protein structure according to their desired methodGeneral Utilitiesaesop.plotScan()Show bargraph summary of results from computational mutagenesis methods (Alascan, DirectedMutagenesis)aesop.plotESD()Show heatmap summary of results from methods exploring electrostatic similarity (ElecSimilarity)aesop.plotDend()Show dendrogram summary of results from methods exploring electrostatic similarity (ElecSimilarity)NotesWe recommend using Anaconda to aid in installation of Python scientific librariesDepending on your platform, ProDy may need to be installed with an executable |
aes-ops-review-report | Failed to fetch description. HTTP Status Code: 404 |
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aes-pipe | AES PipeThis application/library is for encrypting piped data and was mainly developed to be
used withlimittarfor space efficient
data encryption using pipes to remove the need for temporarily storing the
potentially large archives and encrypted data.RequirementsPython 3.4+PyCryptoInstallationFrom the Python Package Index:pip install aes-pipeOr download and run:python3 setup.py installUsageUse the-hargument for help:aes-pipe -hEncrypting dataIf no key command is specified, the user is prompted for a passphrase.cat something.tar | aes-pipe > encrypted_tarEncrypting files spanned across multiple Blu-Ray discsfind /path/photos/ -print0 > files
limittar -0 -i files -l remaining1 -s 25025314784 | aes-pipe | cdrskin -v driveropts=burnfree -tao dev=/dev/sr0 -
limittar -0 -i remaining1 -l remaining2 -s 25025314784 | aes-pipe | cdrskin -v driveropts=burnfree -tao dev=/dev/sr0 -
...As the remaining file list is output before the encryption ends, multiple
discs can be written to at once.Note that aes-pipe.py prepends a 32 byte nonce to the encrypted output data in
this case which needs to be calculated into the size limit of the tar.Decrypting files from discsUsing the Blu-Ray discs created in the example above, the following line can be
run for each disc.cat /dev/sr0 | aes-pipe -d | tar -xf -Files will be output with their original paths.Decrypt only specific files and directoriesThis is useful for recovering deleted items from a backup.
Until the items are found, this will need to be run on each storage area
across which the encrypted data was spanned.cat /dev/sdX | aes-pipe -d | tar -C path/to/output/dir/ -xf - "path/of/dir in archive/" path/of/a_file.pngEncryption with a GPG public keyOutput encrypted key filecat something.tar | aes-pipe -c "gpg --output encrypted_key.gpg --encrypt --recipient [email protected]" > encrypted_tarThis pipes the encryption key and nonce to the gpg application. This also means
that the nonce is not prepended to the encrypted output which means the output
data size is the same as the input data size.Use encrypted key filecat encrypted_tar | aes-pipe -d -c "gpg --decrypt encrypted_key.gpg" > something.tar |
aes-pkcs5 | AESPKCS5Python implementation of AES with CBC/ECB mode and padding scheme PKCS5.RequirementsPython 3.8+InstallYou can install AESPKCS5 from PyPI with:pip install aes-pkcs5DocumentationDocumentation is available online at:https://aes-pkcs5.readthedocs.io/ |
aesplot | AestheticsTo use only the aesthetics and not the framework simply use create a class instance and call for PlotLikeR().plot_cfg() and/or PlotLikeR().plot_cfg_grid() methods.In the matplotlib documentation it is possible to find a description of each individual parameter. The majority of which, in the Aesplot library, are set as the default. For more check this link:https://matplotlib.org/stable/tutorials/introductory/customizing.html#the-default-matplotlibrc-file.FrameworkUse a separate python file to create the basic framework structure.
Modify the new file or string to make your analysis.UtilsFixing the SVG export from matplotlibWhen saving the matplotlib figure as SVG, the syntax used doesnโt contain any <text></text>. Instead, matplotlib opts for creating there own letters as mathematical curves. This can be an issue due to the โlow resolutionโ aspect of these curves. To solve this, an utility tool was developed to find and replace the curves for letters.Additionally, when using LaTex equations in labels and/or legends, some elements can also be converted into HTML syntax.Converting LaTex to HTMLSome mathematical notation is usually used when plotting graphs such as square root, superscript, index and many others. And, since matplotlib supports LaTex interpretation/compilation, it is useful to have a translation from one markup language to another.Using utils.SVGText() replaces markups, such as /textbf{}, into an equivalent <tspan></tspan> around the tag. More LaTex markups will be added in the future.DependenciesNumpy:https://numpy.orgMatplotlib:https://matplotlib.orgPandas:https://pandas.pydata.orgScipy:https://scipy.orgMiktex:https://miktex.orgor other latex compilerTo-dosExpand the latex functions that can be interpreted and translatedDevelop testsCreate data examples and plot examples |
aespy | aespy is an ultra-lightweight, pure-python library for doing AES
encryption. It draws heavily on the popular crypto library, simplifying
AES encryption and decryption of files to a single function each.UsageSimple usage follows this pattern:importaespyencrypt_me="my_transcript.pdf"output_file=easy-aes.encrypt_file(encrypt_me)output_file is a string with the new file name.WARNING: IF YOU FORGET YOUR PASSWORD AND DELETE
THE ORIGINAL DATA, YOUR FILE WILL NOT BE ABLE TO BE RETRIEVED!importaespyencrypted_file="my_encrypted_filename.aes"binary_data=easy-aes.decrypt_file(encrypted_file)withopen('my_new_file.aaa','wb')asnew_file:new_file.write(decrypted_data)decrypt_file returns a Python object containing
the now-decrypted data.Installationaespy works Python 3.3+. To install it,
use:$pipinstallaespy0.1Released on September 10, 2016 |
aesqlapius | aeSQLAPIusSummarySo you don't want to use ORM, but want to organize your SQL queries
in a convenient way. Don't mix them with your python code, don't
writeexecuteandfetchrows by hand for each query. Withaesqlapius:Store your SQL queries separately from the code, in a dedicated
file or directory hierarchyAnnotate each query with python-like function definition specifying
input arguments and output types and patternsaesqlapiusbuilds a class out of this, where you can call your
queries as plain methods. It handles arguments (pass positional
or keyword arguments as you like, default values are also handled) and
output types and patterns (you may specify whether a method returns
iterator, list, dict of rows, or a single row, where row may
be represented as a tuple, list, dict, single value or a custom
type such as a dataclass).Examplequeries.sql:-- specify arguments to queries in python format, including-- type annotations and support for default values-- def add_city(name: str, population: int = None) -> None: ...INSERTINTOcitiesVALUES(%(name)s,%(population)s);-- specify return value format out of wide range of formats-- (iterator, list, dict, or single instance of tuples, dicts-- or simple values)-- def list_cities() -> List[Value]: ...SELECTnameFROMcitiesORDERBYname;-- def get_population(city: str) -> Single[Value]: ...SELECTpopulationFROMcitiesWHEREname=%(city)s;-- def get_populations() -> Dict[-'name', Value]: ...SELECTname,populationFROMcitiesWHEREpopulationISNOTNONEORDERBYname;-- def iter_cities()() -> Iterator[Tuple] ...SELECT*FROMcitiesORDERBYname;script.py:fromaesqlapiusimportgenerate_apidb=psycopg2.connect('...')api=generate_api('queries.sql','psycopg2',db)# pass arguments to queries in either positional and kw formapi.add_city('Moscow',12500000)api.add_city('Paris')api.add_city(population=3800000,name='Berlin')# get query results in the desired formatassertapi.list_cities()==['Berlin','Moscow','Paris']assertapi.get_population('Moscow')==12500000assertapi.get_populations()=={'Berlin':3800000,'Moscow':12500000}assertnext(api.iter_cities())==('Berlin',3800000)ReferencePython APIThe module has a single entry point in form of a function:defgenerate_api(path,driver,db=None,*,target=None,extension='.sql',namespace_mode='dirs',namespace_root='__init__')This loads SQL queries frompath(a file or directory) and returns an API class to use with specified databasedriver(psycopg2,sqlite3,mysql,aiopg,asyncpg).Ifdbis specified, all generated methods are bound to the given database connection object:db=psycopg2.connect('...')api=generate_api('queries.sql','psycopg2',db)api.my_method('arg1','arg2')otherwise caller is expected to pass database connection object to each call:db=psycopg2.connect('...')api=generate_api('queries.sql','psycopg2')api.my_method(db,'arg1','arg2')Iftargetis specified, methods are injected into the given object (which is also returned fromgenerate_api):db=psycopg2.connect('...')generate_api('queries.sql','psycopg2',db,target=db)db.my_method('arg1','arg2')extension(by default.sql) specifies which files are loaded from the queries directory.namespace_modecontrols how hierarchy of files is converted into hierarchy of objects when constructing the API class. There are 3 modes supported:dirs(the default), which maps each subdirectory to a nested method namespace ignoring file names:path under query dirfunction definitionresulting APIroot.sql-- def a(): ...api.a()subdir/foo.sql-- def b(): ...api.subdir.b()subdir/bar.sql-- def c(): ...api.subdir.c()fileswhich uses file names (after stripping the extension) as an extra nesting level:path under query dirfunctionresulting APIroot.sql-- def a(): ...api.root.a()subdir/foo.sql-- def b(): ...api.subdir.foo.b()subdir/bar.sql-- def c(): ...api.subdir.bar.c()In this mode,namespace_rootallows to specify a special file name which circumvents this behavior, allowing to mimic how Python handles module namespaces. For example, whennamespace_root="__init__"(the default):path under query dirfunctionresulting API__init__.sql-- def a(): ...api.a()foo.sql-- def b(): ...api.foo.b()subdir/__init__.sql-- def c(): ...api.subdir.c()subdir/bar.sql-- def d(): ...api.subdir.bar.d()flatmode which ignores hierarchy:path under query dirfunctionresulting APIroot.sql-- def a(): ...api.a()subdir/foo.sql-- def b(): ...api.b()subdir/bar.sql-- def c(): ...api.c()Query annotationsEach query managed byaesqlapiusmust be preceded with a--(SQL comment) followed by a Python-style function definition:-- def function_name(parameters, ...) -> return_type: ......someSQLcode...ParametersParameters allow optional literal default values and optional type annotations (which are currently ignored) and may be specified in both positional, keyword or mixed style in the resulting API:-- def myfunc(foo, bar: str, baz=123) -> None: ...`...someSQLcode...api.myfunc(1, bar="sometext") # foo=1, bar="sometext", baz=123Return valueReturn value annotation is required and may either beNone(when query does not return anything) or a nested type annotation with specific structureRowsFormat[RowFormat].OuterRowsFormatspecifies how multiple rows returned by the query are handled. Allowed values are:Iterator[RowFormat]- return a row iterator.List[RowFormat]- return a list of rows.Single[RowFormat]- return a single row.Dict[KeyColumn, RowFormat]- return a dictionary of rows. The column to be used as a dictionary key is specified in the first argument, e.g.Dict[0, ...]uses first returned column as key and `Dict['colname', ...] uses column namedcolname. Precede column index or name with unary minus to make it removed from the row contents.InnerRowFormatspecifies how data for each row is presented:Tuple- return row as a tuple of values.Dict- return row as a dict, where keys are set to the column names returned by the query.Value- return single value from the row. If the query returns multiple fields, the first one is returned.Examples:-- def example1() -> List[Tuple]: ...SELECT1,'foo'UNIONSELECT2,'bar';-- def example2() -> Single[Value]: ...SELECT2*2;-- def example3() -> Iterator[Dict]: ...SELECT1ASn,'foo'ASsUNIONSELECT2ASn,'bar'ASs;-- def example4() -> Dict['key', Dict]: ...SELECT'foo'ASkey,1ASa,2ASb;-- def example5() -> Dict[-'key', Dict]: ...SELECT'foo'ASkey,1ASa,2ASb;>>> api.example1()
[(1, 'foo'), (2, 'bar')]
>>> api.example2()
4
>>> it = api.example3()
>>> next(it)
{'n': 1, 's': 'foo'}
>>> next(it)
{'n': 2, 's': 'bar'}
>>> api.example4()
{'foo': {'key': 'foo', 'a': 1, 'b': 2}}
>>> api.example5()
{'foo': {'a': 1, 'b': 2}}BodyFunction body of the annotationis required to contain a single ellipsis.Driverspsycopg2Use withpsycopg2connections:importaesqlapius,psycopg2dbconn=psycopg2.connect('dname=... user=... password=...')api=aesqlapius.generate_api('queries.sql','psycopg2',dbconn)api.some_method(arg1=1,arg2=2)sqlite3Use withsqlite3connections:importaesqlapius,sqlite3dbconn=sqlite3.connect('path_to_database.sqlite')api=aesqlapius.generate_api('queries.sql','sqlite3',dbconn)api.some_method(arg1=1,arg2=2)mysqlUse withmysql.connectorconnections:importaesqlapius,mysql.connectordbconn=mysql.connector.connect(database=...,user=...,password=...)api=aesqlapius.generate_api('queries.sql','mysql',dbconn)api.some_method(arg1=1,arg2=2)Notes:The driver usesbuffered=Trueparameter when creating cursor.aiopgUse withaiopgmodule. This driver generates asynchronous APIs, and accepts both connection and pool objects (in the latter case, connection is automatically acquired from the pool).importaesqlapius,aiopgasyncdefpool_example():asyncwithaiopg.create_pool('dname=... user=... password=...')aspool:api=aesqlapius.generate_api('queries.sql','aiopg',pool)awaitapi.some_method(arg1=1,arg2=2)asyncdefconnection_example():api=aesqlapius.generate_api('queries.sql','aiopg')asyncwithaiopg.create_pool('dname=... user=... password=...')aspool:asyncwithpool.acquire()asconn:awaitapi.some_method(conn,arg1=1,arg2=2)asyncpgUse withasyncpgmodule. This driver generates asynchronous APIs, and accepts both connection and pool objects (in the latter case, connection is automatically acquired from the pool).importaesqlapius,asyncpgasyncdefpool_example():asyncwithasyncpg.create_pool(database=...,user=...,password=...)aspool:api=aesqlapius.generate_api('queries.sql','asyncpg',pool)awaitapi.some_method(arg1=1,arg2=2)asyncdefconnection_example():conn=awaitasyncpg.connect(database=...,user=...,password=...)api=aesqlapius.generate_api('queries.sql','asyncpg',conn)awaitapi.some_method(arg1=1,arg2=2)asyncdefanother_connection_example():api=aesqlapius.generate_api('queries.sql','asyncpg')asyncwithasyncpg.create_pool('dname=... user=... password=...')aspool:asyncwithpool.acquire()asconn:awaitapi.some_method(conn,arg1=1,arg2=2)Notes:Methods withIteratorrows format use asyncpg cursors under the hood which are only available in transaction. The driver automatically wraps such methods in a transaction if they are called outside of one.LicenseMIT license, copyright (c) 2020 Dmitry [email protected]. |
aes-sha1prng | Used by AES encrypt or decrypt.
AES/ECB/PKCS5Padding same as aes in java default. |
aes-sid | Authenticated deterministic encryption for 64-bit integers based on the AES-SIV MRAE construction |
aestar | No description available on PyPI. |
aestate-json | Aestate-Json ไธๆฌพๅผบๅคง็json่งฃๆๅจไป็ปไปAestate Frameworkๅ็ฆปๅบๆฅ็Json่งฃๆๅจ๏ผๅ
ถๅผบๅคง็จๅบฆๅฏไปฅ่ฎฉไฝ ๆ ้ๅฅๅจใๅ่ฝ๏ผๅฐobjectๅฏน่ฑก่ฝฌๆขๆJsonๅญ็ฌฆไธฒ็ๅฝขๅผใไธบ่พพๅฐ่ฝๅคๅmybatis่ฟๆ ท็็ฅไป็บงๆไฝๆไธ็ดๅจไธๆญๅฐๅชๅ๏ผ ไนๆๅพๅค็ไธ่ถณไนๅค ๏ผ ๅธๆๅไฝๅคๅคๆ้ฎ้ข๏ผๆไผ่ฎค็็ๅฎๆฏไธไธชissuesๅฎ่ฃ
pip ๅฝไปค๏ผpip install aestate-jsonanaconda ๅฎ่ฃ
:conda install aestate-jsonไฝฟ็จๆ็จไธๅ็็ๆ็จไธ่ด๏ผไฝ ๅฏไปฅไฝฟ็จfromajson.ajsonimportJson# ๅ ่ฝฝjsonๅญ็ฌฆไธฒไธบdictๅญๅ
ธๆ ผๅผJson.loads()# ๅ ่ฝฝๆฐๆฎไธบjsonๅญ็ฌฆไธฒJson.dumps()ๆๅฑ็ๆนๆณ๏ผfromajsonimportaj# ๅฐไปปๆๅฏน่ฑก่ฝฌๆขๆjsonๅญ็ฌฆไธฒa=aj.parse(obj='ไปปๆๅฏน่ฑก',bf='ๆฏๅฆ็พๅjson',end_load='ๆฏๅฆ่ฝฌๆjsonๅๅ่ฝฌไธบdictๅญๅ
ธ')# ๅฐjsonๅญ็ฌฆไธฒ่ฝฌๅญๅ
ธa=aj.load(a)ไฝฟ็จ็คบไพ็ไปฃ็ ไฝ ไผๅ็ฐ๏ผ่ฟไธชๅทฅๅ
ท็ฑปๅฏไปฅๆ ้ๅฅๅจCACode Development TeamLast edit time:2021/06/01 05:30 Asia/Shanghai๐ Go to canotf`s homepage on Gitee ๐ |
aestene-lib | No description available on PyPI. |
aesthema | AesthemaAesthema provides modern & visually appealing colormaps for Matplotlib.Matplotlib is a great package for visualizations, but we've all been there when you start creating your own colormaps because the default ones look not really appealing or you need to stick to your already existing theme.
This is where this small package comes into play. Instead of creating your own colormaps all the time, you can use the provided colormaps or you can also easily create your own colormaps either by using the provided colors or any RGB color.Currently available colors:Currently available colormaps:QuickstartYou can install the package usingpip:pip install aesthemaLet's take a look at some examples:importnumpyasnpimportmatplotlib.pyplotaspltfromaesthemaimportuse_colormapuse_colormap()x=np.linspace(0,2*np.pi,100)plt.figure()plt.plot(x,np.sin(x))plt.plot(x,np.cos(x))plt.plot(x,np.sin(x+np.pi/4))plt.plot(x,np.cos(x+np.pi/4))plt.plot(x,np.sin(2*x))plt.plot(x,np.cos(2*x))plt.title("Sine and Cosine")plt.xlabel("x")plt.ylabel("Amplitude")plt.show()In this example, we use the default colormap. If you want to use a different colormap, just specify it when callinguse_colormap, e.g.:fromaesthemaimportuse_colormap,Colormapsuse_colormap(Colormaps.PASTEL)You can also create your own custom colormap using either the colors provided in this package or any RGB color:fromaesthemaimportcreate_colormap,use_colormap,Colorsmy_colormap=create_colormap([Colors.RED,Colors.ORANGE,Colors.LEMON,(141,215,127),Colors.LIGHT_OCEAN,(47,72,88)])use_colormap(my_colormap)ContributingYou created a nice colormap that you want to share with others? Awesome! Create a pull request and we will add it. |
aesthetic | a e s t h e t i cStyle-related tools that I re-use across projects in astronomy.Most relevant are the style sheets, which produce default plots as in the/results/directory.installOption 1:pip install aestheticOption 2: Clone +python setup.py installfrom the repo. (or develop!)contentsaesthetic.plotset_styleset_style_scattersavefigformat_axaesthetic.paperabbreviate_the_bibliographyusage examplesfor plot styles, see thetest driver.
the general syntax follows:from aesthetic.plot import set_style
set_style("science")set_style("clean") |
aesthetic-art | Aesthetic ASCIIGenerateA E S T H E T I CASCII art.InstructionsInstall:pip install aesthetic-artGenerate an aesthetic ASCII visual:fromaesthetic_artimportsynthesize# initialize drive object (to generate visuals)drive=synthesize.Drive()# generate a ASCII visual (dark_mode optional)drive.generate(dark_mode=True)# save to pngdrive.to_png('aesthetic.png')Ride the space skyway home to 80s Miami |
aesthetic-ascii | Aesthetic ASCIIGenerateA E S T H E T I CASCII art.InstructionsInstall:pip install aesthetic-asciiGenerate an aesthetic ASCII visual:fromaesthetic_asciiimportsynthesize# initialize drive object (to generate visuals)drive=synthesize.Drive()# generate a ASCII visual (dark_mode optional)drive.generate(dark_mode=True)# save to pngdrive.to_png('aesthetic.png')Ride the space skyway home to 80s Miami |
aesthetic-ascii2 | Aesthetic ASCIIGenerate A E S T H E T I C ASCII art. |
aesthetic-asciihammer | Aesthetic ASCIIGenerateA E S T H E T I CASCII art.InstructionsInstall:pip install aesthetic-asciiGenerate an aesthetic ASCII visual:fromaesthetic_asciiimportsynthesize# initialize drive object (to generate visuals)drive=synthesize.Drive()# generate a ASCII visual (dark_mode optional)drive.generate(dark_mode=True)# save to pngdrive.to_png('aesthetic.png')Ride the space skyway home to 80s Miami |
aesthetic-ascii-mindaug | Aesthetic ASCIIGenerateA E S T H E T I CASCII art. |
aesthetic-ascii-mindaugas | Aesthetic ASCIIGenerateA E S T H E T I CASCII art. |
aesthetic-predictor | aesthetic-predictorAbstractA python package of aesthetic quality of pictures a.k.aaesthic-predictor.
See details:https://github.com/LAION-AI/aesthetic-predictorInstallationpipinstallaesthetic_predictorHow to usefromaesthetic_predictorimportpredict_aestheticfromPILimportImageprint(predict_aesthetic(Image.open("path/to/image"))) |
aesthetic-predictor-flask-api | No description available on PyPI. |
aesthetic-predictor-flask-api-cribin | No description available on PyPI. |
aesthetic-text | Aesthetic TEXTGive a beautiful look of your text in the terminal.link to access to the pypi page :https://pypi.org/project/aesthetic-text/0.2.0/Installation$pipinstallaesthetic_textUsagefromaesthetic_textimportaesthetic_textasaesthetic# print a text with a style# style: bold, italic, underline, strikethrough, reverse, conceal, crossedprint(f"{aesthetic.style.bold}{aesthetic.style.underline}"+"Hello World"+f"{aesthetic.reset}")# print a text with a color# colors: black, red, green, yellow, blue, magenta, cyan, white...print(f"{aesthetic.color.red}"+"Hello World"+f"{aesthetic.reset}")# print a text with style and colorprint(f"{aesthetic.style.bold}{aesthetic.color.red}"+"Hello World"+f"{aesthetic.reset}# print a text with iconprint(f"{aesthetic.icon.check}"+"the task has been completed successfully"+f"{aesthetic.reset}") |
aesthetix | AesthetixThis is a python package which facilitates aesthetics in output when using custom training pipelines in machine learning.Usage Exampleimporttimeimportaesthetixasatcost=3000acc=0.9foriinrange(1000):at.progress_bar("Progress",i,1000,num_bars=40,output_vals={"Cost":cost,"Accuracy":acc})cost/=3acc*=1.001time.sleep(0.01)OutputProgress:[========================================](100.00%) Cost : 0.00 Accuracy : 2.44 |
aestimo | Aestimo 1D Self-consistent Schrรถdinger-Poisson SolverAestimo is a simple 1-dimensional (1-D) simulator for educational and research. Please do not hesitate to contact us in case of any bugs found. |
aestoolbox | AES-ToolboxAn AES Toolbox for computing Rijndael key schedule given a 128, 192, or 256-bit key.Documentation:https://aestoolbox.readthedocs.io.Github:https://github.com/emdneto/aestoolbox.FeaturesEncryption/Decryption Key SchedulingAES Encrypt/Decrypt (work in progress)InstallationStable release via pipTo install AES-Toolbox, run this command in your terminal:$pipinstallaestoolboxIf you donโt havepipinstalled, thisPython installation guidecan guide
you through the process.Using AES-ToolboxUsage (via CLI)$aes-schedule[-h][-v][-i]key$aes-schedule0x0101010102020202030303030404040-i-vThe above command should output:{'xk':{0:'0x01010101020202020303030304040404',1:'0xf2f3f3f3f0f1f1f1f3f2f2f2f7f6f6f6',2:'0xb2b1b19b4240406ab1b2b2984644446e',3:'0xadaa2ec1efea6eab5e58dc33181c985d',4:'0x39ec626cd6060cc7885ed0f4904248a9',5:'0x05beb10cd3b8bdcb5be66d3fcba42596',6:'0x6c812113bf399cd8e4dff1e72f7bd471',7:'0x0dc98206b2f01ede562fef3979543b48',8:'0xad2bd0b01fdbce6e49f4215730a01a1f',9:'0x568910b44952deda00a6ff8d3006e592',10:'0x0f505fb04602816a46a47ee776a29b75'},'xki':{0:'0x01010101020202020303030304040404',1:'0xfdfafef8fff8fcfafcfbfff9f8fffbfd',2:'0xc263931b3d9b6fe1c1609018399f6be5',3:'0x70e738474d7c57a68c1cc7beb583ac5b',4:'0xa68450a9ebf8070f67e4c0b1d2676cea',5:'0xb86800d6539007d93474c768e613ab82',6:'0xffd917eeac491037983dd75f7e2e7cdd',7:'0xe238ed774e71fd40d64c2a1fa86256c2',8:'0xc20b68478c7a95075a36bf18f254e9da',9:'0x7edace11f2a05b16a896e40e5ac20dd4',10:'0x0f505fb04602816a46a47ee776a29b75'}}DisclaimerAES-Toolbox implementations should not be used in security software or production environments. The AES-Toolbox is for research purposes. |
aestools | aestools check KEYTest for weak AES key when used in Galois Counter Mode with a block size
of 128 bits, as explained in the paper โCycling Attacks on GCM, GHASH and
Other Polynomial MACs and Hashesโ:https://eprint.iacr.org/2011/202.pdfaestools generate BITSGenerate safe key of BITS length which is above a specific strength threshold. |
aestream | AEStream sends event-based data from A to B.
AEStream is both a command-line tool an a C++/Python library with built-in GPU-acceleration for use withPyTorch, andJax.
We support reading and writing from files,event cameras, network protocols, and visualization tools.Read more about the inner workings of the library inthe AEStream publication.InstallationRead more in ourinstallation guideThe fastest way to install AEStream is by using pip:pip install aestream.SourceInstallationDescriptionpippip install aestreampip install aestream --no-binaryStandard installationCompilation with support forevent-camerasand CUDA kernels*nixnix run github:aestream/aestreamnix develop github:aestream/aestreamCommand-line interfacePython environmentdockerSeeInstallation documentationContributions to support AEStream on additional platforms are always welcome.Usage (Python): Load event filesRead more in ourPython usage guideAEStream can process.csv,.dat,.evt3, and.aedat4files like so.
You can either directly load the file into memoryFileInput("file.aedat4",(640,480)).load()or stream the file in real-time to PyTorch, Jax, or NumpywithFileInput("file.aedat4",(640,480))asstream:whileTrue:frame=stream.read("torch")# Or "jax" or "numpy"...Usage (Python): stream data from camera or networkStreaming data is particularly useful in real-time scenarios.
We currently supportInivation,Prophesee, andSynSensedevices over USB, as well as theSPIFprotocol over UDP.
Note: requires local installation of drivers and/or SDKs (seeinstallation guide).# Stream events from a DVS camera over USBwithUSBInput((640,480))asstream:whileTrue:frame=stream.read()# A (640, 480) Numpy tensor...# Stream events from UDP port 3333 (default)withUDPInput((640,480),port=3333)asstream:whileTrue:frame=stream.read("torch")# A (640, 480) Pytorch tensor...More examples can be found inour example folder.
Please note the examples may require additional dependencies (such asNorsefor spiking networks orPySDLfor rendering). To install all the requirements, simply stand in theaestreamroot directory and runpip install -r example/requirements.txtExample: real-time edge detection with spiking neural networksWe stream events from a camera connected via USB and process them on a GPU in real-time using thespiking neural network library, Norseusing fewer than 50 lines of Python.
The left panel in the video shows the raw signal, while the middle and right panels show horizontal and vertical edge detection respectively.
The full example can be found inexample/usb_edgedetection.pyUsage (CLI)Read more in ourCLI usage documentation pageInstalling AEStream also gives access to the command-line interface (CLI)aestream.
To useaestraem, simply provide aninputsource and an optionaloutputsink (defaulting to STDOUT):aestreaminput<inputsource>[output<outputsink>]Supported Inputs and OutputsInputDescriptionExample usageDAVIS, DVXPlorerInivationDVS Camera over USBinput inivationEVK CamerasPropheseeDVS camera over USBinput propheseeFileReads.aedat,.aedat4,.csv,.dat, or.rawfilesinput file x.aedat4SynSense SpeckStream events via ZMQinput speckUDP networkReceives stream of events via theSPIF protocolinput udpOutputDescriptionExample usageSTDOUTStandard output (default output)output stdoutEthernet over UDPOutputs to a given IP and port using theSPIF protocoloutput udp 10.0.0.1 1234File:.aedat4Output to.aedat4formatoutput file my_file.aedat4File:.csvOutput to comma-separated-value (CSV) file formatoutput file my_file.csvViewerView live event streamoutput viewCLI examplesExampleSyntaxView live stream of Inivation camera (requires Inivation drivers)aestream input inivation output viewStream Prophesee camera over the network to 10.0.0.1 (requires Metavision SDK)aestream input output udp 10.0.0.1Convert.datfile to.aedat4aestream input example/sample.dat output file converted.aedat4AcknowledgmentsAEStream is developed by (in alphabetical order):Cameron Barker (@GitHubcameron-git)Juan Pablo Romero Bermudez(@GitHubjpromerob)Alexander Hadjivanov (@Githubcantordust)Emil Jansson (@GitHubemijan-kth)Jens E. Pedersen(@GitHubjegp)Christian Pehle(@GitHubcpehle)The work has received funding from the EC Horizon 2020 Framework Programme under Grant Agreements 785907 and 945539 (HBP) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Fundation) under Germany's Excellence Strategy EXC 2181/1 - 390900948 (the Heidelberg STRUCTURES Excellence Cluster).Thanks toPhilipp Mondorffor interfacing with Metavision SDK and preliminary network code.CitationPlease citeaestreamif you use it in your work:@misc{aestream,doi={10.48550/ARXIV.2212.10719},url={https://arxiv.org/abs/2212.10719},author={Pedersen, Jens Egholm and Conradt, Jรถrg},title={AEStream: Accelerated event-based processing with coroutines},publisher={arXiv},year={2022},} |
aes-vial | Vial is a simple wrapper for AES CTR mode. You have to set the key toan appropriate length:AES-128bit (16 bytes/characters)
AES-192bit (24 bytes/characters)
AES-256bit (32 bytes/characters)or use a hashing package thatโll provide more security and ease your
pain oftyping exactly the 16, 24 or 32 characters.Usage:import vial
vial = vial.Vial(key)
vial.encrypt(plaintext, output_counter_file)
vial.decrypt(plaintext, output_counter_file)
vial.encrypt_stream(input, output, block_size)
vial.decrypt_stream(input, output, block_size)encrypt(text, counter_path)This function needs as an extra argument a path where you wantthe file with initial Counter value stored. The file is important,you need to use it to decrypt.Returns:encrypted text (bytes)decrypt(text, counter_path)This function needs as an extra argument the path where you savedthe file from Vial.encrypt(). Without this file the text wonโt bedecrypted.Returns:decrypted text (bytes)encrypt_stream(input, output, block_size=4096)This function needs as extra arguments only input file and a pathfor the output file. A file with Counterโs initial value will becreated automatically in the same location and with the same name asoutput file.Args:input, output: Expects a file object ( f = open(...) )
block_size (optional): The max block_size to encrypt with the same
AES encrypt()Example:vial = Vial(key)
input = open('encrypt_me.png', 'rb')
output = open('im_encrypted.png', 'wb')
vial.encrypt_stream(input, output)
input.close()
output.close()Result:root/
encrypt_me.png
im_encrypted.png
im_encrypted.ctrdecrypt_stream(input, output, block_size=4096)This function needs as extra arguments only input file and a pathfor the output file. The file with Counterโs initial value has to beplaced in the same folder as the input file as the functionautomatically checks for it and gets the value to decrypt it.Args:input, output: Expects a file object ( f = open(...) )
block_size (optional): The max block_size to decrypt with the same
AES decrypt()Example:vial = Vial(key)
input = open('im_encrypted.png', 'rb')
output = open('im_decrypted.png', 'wb')
vial.encrypt_stream(input, output)
input.close()
output.close()Result:root/
im_encrypted.png
im_encrypted.ctr
im_decrypted.png |
ae-sys-core | sys_core 0.3.23ae namespace module portion sys_core: dynamic system configuration, initialization and connection.installationexecute the following command to install the
ae.sys_core module
in the currently active virtual environment:pipinstallae-sys-coreif you want to contribute to this portion then first forkthe ae_sys_core repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_sys_core):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-sys-core-sh | sys_core_sh 0.3.6ae namespace module portion sys_core_sh: SiHOT PMS system core xml interface.installationexecute the following command to install the
ae.sys_core_sh module
in the currently active virtual environment:pipinstallae-sys-core-shif you want to contribute to this portion then first forkthe ae_sys_core_sh repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_sys_core_sh):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-sys-data | sys_data 0.3.11ae namespace module portion sys_data: external system data structures.installationexecute the following command to install the
ae.sys_data module
in the currently active virtual environment:pipinstallae-sys-dataif you want to contribute to this portion then first forkthe ae_sys_data repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_sys_data):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-sys-data-sh | sys_data_sh 0.3.8ae namespace module portion sys_data_sh: Sihot system data interface.installationexecute the following command to install the
ae.sys_data_sh module
in the currently active virtual environment:pipinstallae-sys-data-shif you want to contribute to this portion then first forkthe ae_sys_data_sh repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_sys_data_sh):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aet | No description available on PyPI. |
aetacsdcscsdcawaca | No description available on PyPI. |
aetacsdcscsdcawacab | No description available on PyPI. |
aetacsdcscsdcawacacszxcwqf | Failed to fetch description. HTTP Status Code: 404 |
aetcd | Installation$python-mpipinstallaetcdBasic usageRunasyncioREPL:$python-masyncioTest the client:importaetcdasyncwithaetcd.Client()asclient:awaitclient.put(b'foo',b'bar')awaitclient.get(b'foo')awaitclient.delete(b'foo')AcknowledgementsThis project is a fork ofetcd3aio, which itself is a fork
ofpython-etcd3.python-etcd3was originally written bykragniz.asynciosuppport
was contributed byhronand based on the previous work bygjcarneiro. Many thanks to all
thepeopleinvolved in the project. |
aetcd3 | NOTICE: Please useaetcdpackage instead, as this is now a canonical name for the project.aetcd3is now outdated and not maintained anymore.Installation$python3-mpipinstallaetcd3Basic usageimportaetcd3client=aetcd3.client()awaitclient.put('foo','bar')awaitclient.get('foo')awaitclient.delete('foo')AcknowledgementsThis project is a fork ofetcd3aio, which itself is a fork
ofpython-etcd3.python-etcd3was originally written bykragniz.asynciosuppport
was contributed byhronand based on the previous work bygjcarneiro. Kudos to all
the people involved in the project. |
aet.consumer | No description available on PyPI. |
ae-telemetry | No description available on PyPI. |
aetf | aetf: autoencoder functions using TensorFlow \\*********************************************To use aetf's functions, do:>>> import aetf>>> print(aetf.__doc__) |
aether | # Welcome to AetherThe Aether platform is a system of applications and utilities for developers to rapidly and easily build algorithms
that use satellite and geospatial data. The Aether platform is accessible by REST API and python, but operates
entirely in the cloud using deferred graphs. This allows developers to build and execute applications with processing
abstracted away and minimal data transfer. An important consequence of this design choice is that the same algorithm
code developers use during exploration can be repackaged and deployed as mobile or web applications. These
applications are entirely portable, and can be published to users or other developers through a simple URL key.
In that regard, the Aether platform is an SDK for satellite analytics and framework for mobile end user applications.The platform currently supports search of three publicly available Resources: The LandSat Archive (LandSat 4
through 8), Sentinel-2, and the USDA Cropland Data Layer, a 30m map of the US categorizing the agricultural land use
annually.The platform is designed to rapidly add new data layers, making them available through the same interface. Resources
can be hosted by Aether, or made accessible via owner API, and restricted to a subset of users. The usage of each
Resource and its geographic usage is tracked as well.The Aether platform is designed to allow user-developers to compile and publish their applications as easily as they
prototype, often with the same code. This tutorial demonstrates a developer creating an application that generates
an NDVI map from LandSat imagery for an arbitrary polygon, which does not need to be specified until runtime by
the end user (or mobile device). |
aether-chart-generator | No description available on PyPI. |
aether-client-sdk | No description available on PyPI. |
aether-grpc | No description available on PyPI. |
aether-helm-generator | No description available on PyPI. |
aetherling | AetherlingOverviewAetherling is a library for creating statically scheduled, data-parallel pipelines in hardware.
This is the Python backend of Aetherling.
Thefronted is in Haskell.The current overview of Aetherling is:https://github.com/David-Durst/embeddedHaskellAetherling/tree/master/theory.InstallationInstall the following components in the proscribed order:CoreIRHow to install:https://github.com/rdaly525/coreir/blob/master/INSTALL.mdFaultHow to install:[email protected]:leonardt/fault.gitcdfaultpipinstall-e.MantleThis is a library of useful Magma components.How to install:https://github.com/phanrahan/mantle#[email protected]:leonardt/fault.gitcdfaultpipinstall-e.Install Aetherling:gitclonehttps://github.com/David-Durst/aetherling/cdaetherling
pipinstall-e.Run the unit tests:pytest-stests |
aether.python | Aether Python LibraryThis is the official Python Library with Aether functions.Table of contentsRequirementsInstallationDistributionTestsUsageRedis toolsRequirementsThis library requiresPython 3.6and above.Python libraries:eha-jsonpathProvides new Extensions to the jsonpath_ng python library to provide commonly requested functions.jsonschemaAn implementation of JSON Schema validation for Python.redisPython client for Redis key-value store.requestsHTTP for Humans.spavroAn Avro library, Spavro is a (sp)eedier avro implementation using Cython.Extra dependencies (based on settings):testbirdisleA modified version of redis that runs as a library inside another process.coverageA tool for measuring code coverage of Python programs.flake8Tool For Style Guide Enforcement.flake8-quotesFlake8 extension for checking quotes in python.tblibTraceback serialization library.Return to TOCInstallation# standalonepip3installaether.pythonReturn to TOCDistributionHow to create the package distributionExecute the following command:python3setup.pybdist_wheelor./scripts/build.shReturn to TOCTestsDepending on your preference you can either use virtualenv or pipenv to test the library locally.Virtual EnvFirst install dependencies (execute it only once):./scripts/install.shAfter that execute the following command:source./venv/bin/activate
./scripts/test.shPipenvIn the root folder run:pipenvinstall.Then to test run:pipenvrunscripts/test.shThe filescripts/test.inicontains the environment variables used in the tests.Return to TOCUsageRedis ToolsThis provides an interface to a Redis server via supplied redis parameters.It makes available a number ofCRUDredis operation which include but not limited to:
- Formats document keys into_{type}:{tenant}:{id}before being cached on redis.
- Retrieves documents based on preformated keys.
- Removes documents based on preformated keys.
- Subscribes to key based channels with a callback function.Usagefromaether.python.redis.taskimportTaskHelperREDIS_TASK=TaskHelper(settings,redis_instance)# Settings must have the following properties:# REDIS_HOST str - Redis server host,# REDIS_PORT int - Redis server port,# REDIS_PASSWORD str - Redis server password,# REDIS_DB str - Redis database name# redis_instance (Optional) - Pass an existing redis connection# (If provided, ignores all settings and uses redis_instance)document={'id':'document_id','name':'document name'}document_type='test_document'aether_tenant='prod'# add document to redisREDIS_TASK.add(task=document,type=document_type,tenant=aether_tenant)# retrieve document from redisREDIS_TASK.get(_id=document['id'],type=document_type,tenant=aether_tenant)# subcribe to a key based channelCHANNEL='_test_document*'# listens for messages published to all channels starting with '_test_document'defhandle_callback(msg):print(msg)# handle returned messageREDIS_TASK.subscribe(callback=handle_callback,pattern=CHANNEL,keep_alive=True)# publish documentREDIS_TASK.publish(task=document,type=document_type,tenant=aether_tenant)# this will trigger the 'handle_callback' function with the published document to all subscribed clients |
aether.sdk | Aether Django SDK LibraryThis library contains the most common features used by the different Aether django modules.Table of contentsRequirementsInstallationDistributionTestsUsageQuick startEnvironment variablesManagement commandsRequirementsThis library requiresPython 3.6and above.Python libraries:djangoAs web framework. (Above 2.2)django-cors-headersfor handling the server headers required for Cross-Origin Resource Sharing (CORS).django-debug-toolbarA configurable set of panels that display various debug information about the current request/response.django_postgrespool2Postgres Connection Pooling for Django, powered by SQLAlchemy.django-prometheusTo monitor the application with Prometheus.io.django-silkA live profiling and inspection tool for the Django framework.django-uwsgiDjango related examples/tricks/modules for uWSGI.djangorestframeworkA powerful and flexible toolkit for building Web APIs. (Above 3.8)drf-dynamic-fieldsDynamically select only a subset of fields per DRF resource,
either using a whitelist or a blacklist.markdownThis is a Python implementation of John Gruber's Markdown.psycopg2-binaryPython-PostgreSQL Database Adapter.pygmentsA syntax highlighting package written in Python.python-json-loggerA python library adding a json log formatter.requestsHTTP for Humans.uwsgiThe Python Web Server Gateway Interface.Extra dependencies (based on settings):cachedjango-cacheopsA slick ORM cache with automatic granular event-driven invalidation.django-redisFull featured redis cache backend for Django.schedulerdjango-rqA simple app that provides django integration for RQ (Redis Queue).redisThe Python interface to the Redis key-value store.rqSimple, lightweight, library for creating background jobs, and processing them.rq-schedulerSmall package that adds job scheduling capabilities to RQ.serversentry-sdkPython client for Sentry.storagedjango-minio-storageA django storage driver for minio.django-storagesA collection of custom storage backends for Django.
Enabled forboto3andgoogle-cloud-storage.django-cleanupAutomatically deletes old file for FileField and ImageField.
It also deletes files on models instance deletion.testcoverageA tool for measuring code coverage of Python programs.flake8Tool For Style Guide Enforcement.flake8-quotesFlake8 extension for checking quotes in python.tblibTraceback serialization library.webpackdjango-webpack-loaderTransparently use webpack with django.Return to TOCInstallation# standalonepip3installaether.sdk# with extra dependenciespip3installaether.sdk[scheduler,server,storage,test,webpack]Return to TOCDistributionHow to create the package distributionExecute the following command:python3setup.pybdist_wheelor./scripts/build.shReturn to TOCTestsHow to test the libraryFirst install dependencies (execute it only once):./scripts/install.shAfter that execute the following command:source./venv/bin/activate
./scripts/test.shThe filescripts/test.inicontains the environment variables used in the tests.Return to TOCUsageQuick startAdd this snippet in thesettings.pyfile to have the build the django application
settings based on the environment variables.# if it's an aether modulefromaether.sdk.conf.settings_aetherimport*# noqa# if it's an external aether productfromaether.sdk.conf.settingsimport*# noqa# continue with the application specific settings# and re-import the settings variables you need to reuse# from aether.sdk.conf.settings[_aether] import WHATEVER YOU NEED TO...Add this snippet in theurls.pyfile to generate defaulturlpatternsbased on the application settings.fromaether.sdk.conf.urlsimportgenerate_urlpatternsurlpatterns=generate_urlpatterns(token=[True|False],app=[# include here the application/module specific URLs])Default URLs included:The health endpoints:the/healthURL. Always responds with200status and an empty content.
Usesaether.sdk.health.views.healthview.the/check-dbURL. Responds with500status if the database is not available.
Usesaether.sdk.health.views.check_dbview.the/check-appURL. Responds with current application version and more.
Usesaether.sdk.health.views.check_appview.the/adminsection URLs (ADMIN_URLsetting).the/admin/~prometheus/metricsURL. Displays the raw monitoring data.the/admin/~uwsgi/URL. If uWSGI is running displays the server uWSGI settings.the/admin/~purge-cacheURL. Purges django cache.
Available if django cache is enabled.the/admin/~realmsURL. Returns the list of realms with linked data.
TheDEFAULT_REALMis always included even if it has no linked data.
If MULTITENANCY is not enabled returns the fake realmsettings.NO_MULTITENANCY_REALM.the/accountsURLs (AUTH_URLsetting), checks if the REST Framework ones,
using the templates indicated inLOGIN_TEMPLATEandLOGGED_OUT_TEMPLATEsettings, or the Keycloak ones.Based on the arguments:token: indicates if the application should be able to create and return
user tokens via POST request and activates the URL.
The URL endpoint is indicated in theTOKEN_URLsetting.
Defaults to/token.
Usesaether.sdk.auth.views.auth_tokenview.If the current user is not an admin user then creates and returns the authorization
token for himself, otherwise creates a token for theusernamecontained
in the request payload.Based on the application settings:IfDEBUGis enabled:thedebug toolbarURLs.IfPROFILING_ENABLEDis set:the/admin/~silk/URL. Displays the profiling data.IfEXTERNAL_APPSis set and valid:the/check-app/{name}URL. Checks if the external application is reachable
with the URL and token indicated in the settings.
Usesaether.sdk.health.views.check_externalview.
For/check-app/app-namechecks if an external application serverAPP_NAMEis reachable with the provided environment variablesAPP_NAME_URLandAPP_NAME_TOKEN.Possible responses:500-Always Look on the Bright Side of Life!!!โ200-Brought to you by eHealth Africa - good tech for hard placesโthe/check-tokensURL. Redirects to the user tokens page if any of the
external applications is not reachable with the URL indicated in the settings
and the linked current user token.
Usesaether.sdk.health.views.healthview and theaether.sdk.auth.apptoken.decorators.app_token_requireddecorator.the/check-user-tokensURL (CHECK_TOKEN_URLsetting). Displays the external
application tokens for the current user.
Usesaether.sdk.auth.apptoken.views.user_app_token_viewview and
the templateeha/tokens.html.IfAPP_URLsetting is different than/, then the URL pattern for all
endpoints is like:/{app-url}/{endpoint}.IfGATEWAY_ENABLEDisTrue:The application endpoints are also reachable with a prefixed regular expresion
that includes the realm value and the the gateway id for this application.The URL pattern is like:/{app-url}/{current-realm}/{gateway-id}/{endpoint}.The authorization and admin endpoints never depend on any realm so the URLs
use always the public realm.Something like:/{app-url}/{public-realm}/{gateway-id}/accountsand/{app-url}/{public-realm}/{gateway-id}/admin.Return to TOCEnvironment variablesThe following environment variables are used to build the application django settings.
Take a look at thedjango settings.Take a look ataether/sdk/conf.settings.pyfile to check the list of all
the expected environment variables.App specificAPP_LINK:https://www.ehealthafrica.org. The link that appears in the DRF web pages.APP_NAME:eha. The application name displayed in the web pages.APP_NAME_HTML: The HTML expression for the application name.
Defaults to the application name.APP_MODULE: The django module that refers to this application to be included
in theINSTALLED_APPSlist.APP_FAVICON:eha/images/eHA-icon.svg. The application favicon.APP_LOGO:eha/images/eHA-icon.svg. The application logo.APP_URL:/. The application URL in the server.
If host ishttp://my-serverand the application URL is/my-module,
the application enpoints will be accessible athttp://my-server/my-module/{endpoint}.Return to TOCGenericDEBUG: Enables debug mode. Isfalseif unset or set to empty string,
anything else is consideredtrue.TESTING: Indicates if the application executes under test conditions.
Isfalseif unset or set to empty string, anything else is consideredtrue.LOGGING_FORMATTER:json. The application messages format.
Possible values:verboseorjson.LOGGING_LEVEL:info. Logging level for application messages.https://docs.python.org/3/library/logging.html#levelsSENTRY_DSN: Sentry DSN (error reporting tool).https://docs.sentry.ioPRETTIFIED_CUTOFF:10000. Indicates the maximum length of a prettified JSON value.
See:aether.sdk.utils.json_prettified(value, indent=2)method.DjangoDJANGO_SECRET_KEY: Django secret key for this installation (mandatory).https://docs.djangoproject.com/en/3.2/ref/settings/#std:setting-SECRET_KEYLANGUAGE_CODE:en-us. Language code for this installation.https://docs.djangoproject.com/en/3.2/ref/settings/#language-codeTIME_ZONE:UTC. Time zone for this installation.https://docs.djangoproject.com/en/3.2/ref/settings/#std:setting-TIME_ZONESTATIC_URL:/static/. Provides a base URL for the static assets to be served from.STATIC_ROOT:/var/www/static/. Provides the local folder for the static assets to be served from.Django Rest Framework (DRF)PAGE_SIZE:10. Default page size for the REST API.MAX_PAGE_SIZE:5000. Maximum page size for the REST API.HTML_SELECT_CUTOFF:100. Options size for the REST API Form select fields.DatabaseMore information inhttps://docs.djangoproject.com/en/3.2/ref/settings/#databasesPGHOST: Postgres host name (mandatory).PGPORT: Postgres port (mandatory).DB_NAME: Postgres database name (mandatory).PGUSER: Postgres user (mandatory).PGPASSWORD: Postgres user password (mandatory).DB_CONN_MAX_AGE: The lifetime of a database connection, in seconds.
Defaults to0. Value0means non persistent connections butNonemeans persistent.ENABLE_CONNECTION_POOL: Used to indicate if a connection pooler is enabled.
Isfalseif unset or set to empty string, anything else is consideredtrue.
The expected pooler ispgbouncerthat might run
as an external service.DB_POOL_INTERNAL: Used to indicate that an internal connection pooler is used.
Isfalseif unset or set to empty string, anything else is consideredtrue.SQLAlchemylibrary is used to handle internally the connections.
Optional environment variables:DB_POOL_INITIAL_SIZE:20, the initial number of open connections.DB_POOL_MAX_OVERFLOW:80, the number of connections that can be created.DB_POOL_RECYCLE_SECONDS:3600(1 hour), the maximum age, in seconds,
for a connection before discarding it.DB_POOL_USE_LIFO: Used to indicate that an internal connection pooler uses LIFO.
Isfalseif unset or set to empty string, anything else is consideredtrue.Using FIFO vs. LIFOEndpointsADMIN_URL:admin. Admin section endpoint.AUTH_URL:accounts. Authorization endpoints (login and logout URLs)LOGIN_URL,/{AUTH_URL}/login. Login URL.TOKEN_URL:token. Get authorization token endpoint.CHECK_TOKEN_URL:check-user-tokens. Check authorization tokens endpoint.TemplatesLOGIN_TEMPLATE:eha/login.html. Template used in the login page.LOGGED_OUT_TEMPLATE:eha/logged_out.html. Template used in the logged out page.DRF_API_RENDERER_TEMPLATE:eha/api.html. Template used in the DRF browsable
API renderer page.DRF_ADMIN_RENDERER_TEMPLATE:eha/admin.html. Template used in the DRF API
admin renderer page.KEYCLOAK_TEMPLATE:eha/login_realm.html. Template used in the login step
to get the realm and redirect to keycloak login page.KEYCLOAK_BEHIND_TEMPLATE:eha/login_keycloak.html. Template used in the
login page when keycloak is enabled behind the scenes.ProfilingPROFILING_ENABLED: Used to indicate if the profiling tool (Silk) is enabled.
Isfalseif unset or set to empty string, anything else is consideredtrue.SILKY_PYTHON_PROFILER. Used to indicate if uses Python's built-in cProfile profiler.
Isfalseif unset or set to empty string, anything else is consideredtrue.SILKY_PYTHON_PROFILER_BINARY. Used to indicate if generates a binary.proffile.
Isfalseif unset or set to empty string, anything else is consideredtrue.SILKY_PYTHON_PROFILER_RESULT_PATH:/tmp/. Local directory where the*.proffiles are stored.SILKY_META. To see what effect Silk is having on the request/response time.
Isfalseif unset or set to empty string, anything else is consideredtrue.SILKY_MAX_REQUEST_BODY_SIZE:-1. Silk saves the request body if its size (in bytes)
is less than the indicated value. Any value less than0means no limit.SILKY_MAX_RESPONSE_BODY_SIZE:-1. Silk saves the response body if its size (in bytes)
is less than the indicated value. Any value less than0means no limit.SILKY_INTERCEPT_PERCENT:100. Indicates the percentage of requests that are recorded.SILKY_MAX_RECORDED_REQUESTS:10000. The number of request/responses stored.SILKY_MAX_RECORDED_REQUESTS_CHECK_PERCENT:10.The/admin/~silk/URL displays the profiling data (accessible to admin users only).See more inhttps://github.com/jazzband/django-silkReturn to TOCFile Storage SystemSTORAGE_REQUIRED: Used to indicate if the file storage system is required.
Isfalseif unset or set to empty string, anything else is consideredtrue.DJANGO_STORAGE_BACKEND: Used to specify aDefault file storage system.
Available options:minio,s3,gcs.COLLECT_STATIC_FILES_ON_STORAGE: Used to indicate if static files should be collected on the specified cloud-based storage service (minio,s3orgcs)
Isfalseif unset or set to empty string, anything else is consideredtrue.CDN_URL: Used to indicate the public cdn base url to access webpack static files.More information inhttps://django-storages.readthedocs.io/en/latest/index.htmlMinio (DJANGO_STORAGE_BACKEND=minio)BUCKET_NAME: Name of the bucket that will act as MEDIA folder (mandatory).STATIC_BUCKET_NAME: Name of the bucket to collect static files (mandatoryifCOLLECT_STATIC_FILES_ON_STORAGEis set totrue)MINIO_STORAGE_ACCESS_KEY: Minio Access Key.MINIO_STORAGE_SECRET_KEY: Minio Secret Access Key.MINIO_STORAGE_ENDPOINT: Minio server URL endpoint (without scheme).MINIO_STORAGE_USE_HTTPS: Whether to use TLS or not. Determines the scheme.MINIO_STORAGE_AUTO_CREATE_MEDIA_BUCKET: Whether to create the bucket if it does not already exist.MINIO_STORAGE_MEDIA_USE_PRESIGNED: Determines if the media file URLs should be pre-signed.See more inhttps://django-minio-storage.readthedocs.io/en/latest/usageS3 (DJANGO_STORAGE_BACKEND=s3)BUCKET_NAME: Name of the bucket to use on s3 (mandatory). Must be unique on s3.STATIC_BUCKET_NAME: Name of the bucket to collect static files (mandatoryifCOLLECT_STATIC_FILES_ON_STORAGEis set totrue)AWS_ACCESS_KEY_ID: AWS Access Key to your s3 account.AWS_SECRET_ACCESS_KEY: AWS Secret Access Key to your s3 account.Google Cloud Storage (DJANGO_STORAGE_BACKEND=gcs)BUCKET_NAME: Name of the bucket to use on gcs (mandatory).
Create bucket usingGoogle Cloud Consoleand set appropriate permissions.STATIC_BUCKET_NAME: Name of the bucket to collect static files (mandatoryifCOLLECT_STATIC_FILES_ON_STORAGEis set totrue)GS_ACCESS_KEY_ID: Google Cloud Access Key.GS_SECRET_ACCESS_KEY: Google Cloud Secret Access Key.How to create Access Keys on Google Cloud StorageReturn to TOCSchedulerSCHEDULER_REQUIRED: Used to indicate if the RQ platform is required.
Isfalseif unset or set to empty string, anything else is consideredtrue.REDIS_HOST: The redis host name (mandatory).REDIS_PORT: The redis port (mandatory).REDIS_DB: The redis database. Defaults to0.REDIS_PASSWORD: The redis password.Return to TOCCacheDJANGO_USE_CACHE: Used to indicate if the cache is enabled.
Isfalseif unset or set to empty string, anything else is consideredtrue.DJANGO_CACHE_TIMEOUT: Cache timeout in seconds. Defaults to300(5 minutes).REDIS_HOST: The redis host name (mandatory).REDIS_PORT: The redis port (mandatory).REDIS_PASSWORD: The redis password.REDIS_DB_CACHEOPS: The django ORM Redis database. Defaults to1.REDIS_DJANGO_CACHE: Used to indicated if the django cache is handled by Redis.
Isfalseif unset or set to empty string, anything else is consideredtrue.REDIS_DB_DJANGO: The django platform Redis database. Defaults to2.REDIS_SESSION_CACHE: Used to indicated if the django session cache is handled by Redis.
Isfalseif unset or set to empty string, anything else is consideredtrue.REDIS_DB_SESSION: The django session Redis database. Defaults to3.See more indjango-cacheopsReturn to TOCSecurityDJANGO_ALLOWED_HOSTS:*. SetALLOWED_HOSTSDjango setting.https://docs.djangoproject.com/en/3.2/ref/settings/#allowed-hostsCSRF_COOKIE_DOMAIN:.ehealthafrica.org. SetCSRF_COOKIE_DOMAINDjango setting.https://docs.djangoproject.com/en/3.2/ref/settings/#csrf-cookie-domainCSRF_TRUSTED_ORIGINS. SetCSRF_TRUSTED_ORIGINSDjango setting.https://docs.djangoproject.com/en/3.2/ref/settings/#csrf-trusted-originsDJANGO_USE_X_FORWARDED_HOST:False. SetUSE_X_FORWARDED_HOSTDjango setting.https://docs.djangoproject.com/en/3.2/ref/settings/#use-x-forwarded-hostDJANGO_USE_X_FORWARDED_PORT:False. SetUSE_X_FORWARDED_PORTDjango setting.https://docs.djangoproject.com/en/3.2/ref/settings/#use-x-forwarded-portDJANGO_HTTP_X_FORWARDED_PROTO:False. If present setsSECURE_PROXY_SSL_HEADERDjango setting to('HTTP_X_FORWARDED_PROTO', 'https').https://docs.djangoproject.com/en/3.2/ref/settings/#std:setting-SECURE_PROXY_SSL_HEADERReturn to TOCWebpackWEBPACK_REQUIRED: Used to indicate if the assets are served via webpack.
Isfalseif unset or set to empty string, anything else is consideredtrue.WEBPACK_STATS_FILE:{STATIC_ROOT}webpack-stats.json, indicates the file path
that webpack uses to serve the different assets.Return to TOCUsers & AuthenticationStandardThe standard options are to log in via token authentication, via basic authentication
or via the standard django authentication.Return to TOCKeycloak ServerSet theKEYCLOAK_SERVER_URLandKEYCLOAK_CLIENT_IDenvironment variables if
you want to use Keycloak as authentication server.KEYCLOAK_CLIENT_ID(defaults toeha) is the public client that allows
the aether module to authenticate using the Keycloak REST API.
This client id must be added to all the realms used by the aether module.
TheKEYCLOAK_SERVER_URLmust include all the path until the realm is indicated,
usually until/auth/realms.There are two ways of setting up keycloak:a) In this case the authentication process happens in the server side without
any further user interaction.# .env fileKEYCLOAK_SERVER_URL=http://my-keycloak-server/auth/realmsKEYCLOAK_BEHIND_SCENES=trueb) In this case the user is redirected to the keycloak server to finish the
sign in step.# .env fileKEYCLOAK_SERVER_URL=http://my-keycloak-server/auth/realmsKEYCLOAK_BEHIND_SCENES=Read more inKeycloak.Note: Multi-tenancy is automatically enabled if the authentication server
is keycloak.Return to TOCGateway AuthenticationSetGATEWAY_SERVICE_IDto enable gateway authentication with keycloak.
This means that the authentication is handled by a third party system
(likeKong) that includes in each request the JSON Web
Token (JWT) in theGATEWAY_HEADER_TOKENheader (defaults toX-Oauth-Token).
TheGATEWAY_SERVICE_IDindicates the gateway service.In this case the application URLs can be reached in several ways:Trying to access the health endpoint/health:http://my-server/healthusing the internal URLhttp://my-gateway-server/my-realm/my-module/healthusing the gateway URL
(beingmy-moduletheGATEWAY_SERVICE_IDvalue)For those endpoints that don't depend on the realm and must also be available
"unprotected" we need one more environment variable:GATEWAY_PUBLIC_REALM:-This represents the fake realm that is not protected
by the gateway server. In this case the authentication is handled by the other
available options, i.e., basic, token...The authorization and admin endpoints never depend on any realm so the final URLs
use always the public realm.http://my-gateway-server/-/my-module/accounts/http://my-gateway-server/-/my-module/admin/Return to TOCMulti-tenancyThe technical implementation is explained inMulti-tenancy README.
Follow the instructions to enable multi-tenancy option in your application.MULTITENANCY, Enables or disables the feature, isfalseif unset or set
to empty string, anything else is consideredtrue.DEFAULT_REALM,ehaThe default realm for artefacts created
while multi-tenancy was not enabled.REALM_COOKIE,eha-realmThe name of the cookie that keeps the current
tenant id in the request headers.Return to TOCExternal applicationsEXTERNAL_APPS: comma separated list with the external apps that the
current instance must be able to connect to and interact with.
For each value there should be the correspondent environment variables:<<EXTERNAL_APP>>_URL: External application server URL (mandatory).<<EXTERNAL_APP>>_TOKEN: External application authorization token (mandatory).<<EXTERNAL_APP>>_URL_TEST: External application server URL used in tests.
Defaults to the external application server URL.<<EXTERNAL_APP>>_TOKEN_TEST: External application authorization token used in tests.
Defaults to the external application authorization token.If theEXTERNAL_APPSequals toapp-1,mod-ule-2,pro-d-uct-3the expected and
mandatory environment variables are:APP_1_URL,APP_1_TOKENMOD_ULE_2_URL,MOD_ULE_2_TOKENPRO_D_UCT_3_URL,PRO_D_UCT_3_TOKENIf the Gateway authentication is enabled instead of using the given token the
application will use the providedGATEWAY_HEADER_TOKENvalue to communicate
with the external application when possible.Return to TOCManagement commandsTo check if an URL is reachable via command line# arguments:# -u | --url required# -t | --token optional./manage.pycheck_url-u=http://my-server/url/to/checkTo create "admin" users via command line# arguments:# -u | --username optional (defaults to "admin")# -p | --password required# -e | --email optional# -t | --token optional./manage.pysetup_admin-u=admin-p=password-t=auth_tokenTo create "standard" users via command line# arguments:# -u | --username required# -p | --password required# -e | --realm optional (required if MULTITENANCY enabled)# -t | --token optional./manage.pycreate_user-u=user-p=password-t=auth_tokenTo publish webpack assets to CDN via command line# arguments:# -u | --cdn-url required# -w | --webpack-dir required# -s | --storage-path optional./manage.pycdn_publish\-u=http://cdn-server/path/to/assets\-w=path/to/assets/bundles\-s=static/content/by/webpack/Return to TOC |
aether-sphinx | The Aether theme for sphinx documentation. |
aether-utils | Contains various useful utilities for our projects. |
aethos | Aethos"A collection of tools for Data Scientists and ML Engineers to automate their workflow of performing analysis to deploying models and pipelines."To track development of the project, you can view theTrello board.What's new in Aethos 2.0?For a summary of new features and changes to Aethos in v2.0 you can read thisblog post. Alternatively, there is a Google Collab notebook availablehere.Table of ContentsIntroductionUsageInstallationDevelopment PhasesFeedbackContributorsSponsorsAcknowledgmentsFor DevelopersIntroductionAethos is a library/platform that automates your data science and analytical tasks at any stage in the pipeline. Aethos is, at its core, a uniform API that helps automate analytical techniques from various libaries such as pandas, sci-kit learn, gensim, etc.Aethos provides:Automated data science cleaning, preprocessing, feature engineering and modelling techniques through one line of codeAutomated visualizations through one line of codeReusable code - no more copying code from notebook to notebookAutomated dependency and corpus managementDatascience project templatesIntegrated 3rd party jupyter plugins to make analyzing data more friendlyModel analysis use cases - Confusion Matrix, ROC Curve, all metrics, decision tree plots, etc.Model interpretability - Local through SHAP and LIME, global through Morris SensitivityInteractive checklists and tips to either remind or help you through your analysis.Comparing train and test data distributionExporting trained models as a service (Generates the necessary code, files and folder structure)Experiment tracking with MLFlowPre-trained models - BERT, GPT2, etc.Statistical tests - Anova, T-test, etc.You can view a full list of implemented techniques in the documentation or here:TECHNIQUES.mdPlus more coming soon such as:Testing for model driftRecommendation modelsParralelization through Dask and/or SparkUniform API for deep learning modelsAutomated code and file generation for jupyter notebook development and a python file of your data pipeline.Aethos makes it easy to PoC, experiment and compare different techniques and models from various libraries. From imputations, visualizations, scaling, dimensionality reduction, feature engineering to modelling, model results and model deployment - all done with a single, human readable, line of code!Aethos utilizes other open source libraries to help enhance your analysis from enhanced stastical information, interactive visual plots or statistical tests and models - all your tools in one place, all accessible with one line of code or a click! See below in theAcknowledgmentsfor the open source libraries being used in this project.UsageFor full documentation on all the techniques and models, clickhereorhereExamples can be viewedhereTo start, we need to import Aethos dependencies as well as pandas.Before that, we can create a full data science folder structure by runningaethos createfrom the command line and follow the command prompts.OptionsTo enable extensions, such as QGrid interactive filtering, enable them as you would in pandas:importaethosasatat.options.interactive_df=TrueCurrently the following options are:interactive_df: Interactive grid with QGridinteractive_table: Interactive grid with Itable - comes with built in client side searchingproject_metrics: Setting project metricsProject metrics is a metric or set of metrics to evaluate models.track_experiments: Uses MLFlow to track models and experiments.User options such as changing the directory where images, and projects are saved can be edited in the config file. This is located atUSER_HOME/.aethos/ .This location is also the default location of where any images, and projects are stored.New in 2.0The Data and Model objects no longer exist but instead there a multiple objects you can use with more of a purpose.Analysis - Used to analyze, visualize and run statistical models (t-tests, anovas, etc.)Classification - Used to analyze, visualize, run statistical models and train classification models.Regression - Used to analyze, visualize, run statistical models and train regression models.Unsupervised - Used to analyze, visualize, run statistical models and train unsupervised models.ClassificationModelAnalysis - Used to analyze, interpret and visualize results of a Classification model.RegressionModelAnalysis - Used to analyze, interpret and visualize results of a Regression model.UnsupervisedModelAnalysis - Used to analyze, interpret and visualize results of a Unsupervised model.TextModelAnalysis - Used to analyze, interpret and visualize results of a Text model.Now all modelling and anlysis can be achieved via one object.Analysisimportaethosasatimportpandasaspdx_train=pd.read_csv('https://raw.githubusercontent.com/Ashton-Sidhu/aethos/develop/examples/data/train.csv')# load data into pandas# Initialize Data object with training data# By default, if no test data (x_test) is provided, then the data is split with 20% going to the test set## Specify predictor field as 'Survived'df=at.Classification(x_train,target='Survived')df.x_train# View your training datadf.x_test# View your testing datadf# Glance at your training datadf[df.Age>25]# Filter the datadf.x_train['new_col']=[1,2]# This is the exact same as the either of code abovedf.x_test['new_col']=[1,2]df.data_report(title='Titanic Summary',output_file='titanic_summary.html')# Automate EDA with pandas profiling with an autogenerated reportdf.describe()# Display a high level view of your data using an extended version of pandas describedf.column_info()# Display info about each column in your datadf.describe_column('Fare')# Get indepth statistics about the 'Fare' columndf.mean()# Run pandas functions on the aethos objectsdf.missing_data# View your missing data at anytimedf.correlation_matrix()# Generate a correlation matrix for your training datadf.predictive_power()# Calculates the predictive power of each variabledf.autoviz()# Runs autoviz on the data and runs EDA on your datadf.pairplot()# Generate pairplots for your training data features at any timedf.checklist()# Will provide an iteractive checklist to keep track of your cleaning tasksNOTE:One of the benefits of usingaethosis that any method you apply on your train set, gets applied to your test dataset. For any method that requires fitting (replacing missing data with mean), the method is fit on the training data and then applied to the testing data to avoid data leakage.# Replace missing values in the 'Fare' and 'Embarked' column with the most common values in each of the respective columns.df.replace_missing_mostcommon('Fare','Embarked')# To create a "checkpoint" of your data (i.e. if you just want to test this analytical method), assign it to a variabledf.replace_missing_mostcommon('Fare','Embarked')# Replace missing values in the 'Age' column with a random value that follows the probability distribution of the 'Age' column in the training set.df.replace_missing_random_discrete('Age')df.drop('Cabin')# Drop the cabin columnAs you've started to notice, alot of tasks to df the data and to explore the data have been reduced down to one command, and are also customizable by providing the respective keyword arguments (see documentation).# Create a barplot of the mean surivial rate grouped by age.df.barplot(x='Age',y='Survived',method='mean')# Plots a scatter plot of Age vs. Fare and colours the dots based off the Survived column.df.scatterplot(x='Age',y='Fare',color='Survived')# One hot encode the `Person` and `Embarked` columns and then drop the original columnsdf.onehot_encode('Person','Embarked',drop_col=True)ModellingRunning a Single ModelModels can be trained one at a time or multiple at a time. They can also be trained by passing in the params for the sklearn, xgboost, etc constructor, by passing in a gridsearch dictionary & params, cross validating with gridsearch & params.After a model has been ran, it comes with use cases such as plotting RoC curves, calculating performance metrics, confusion matrices, SHAP plots, decision tree plots and other local and global model interpretability use cases.lr_model=df.LogisticRegression(random_state=42)# Train a logistic regression model# Train a logistic regression model with gridsearchlr_model=df.LogisticRegression(gridsearch={'penalty':['l1','l2']},random_state=42)# Crossvalidate a a logistic regression model, displays the scores and the learning curve and builds the modellr_model=df.LogisticRegression()lr_model.cross_validate(n_splits=10)# default is strat-kfold for classification problems# Build a Logistic Regression model with Gridsearch and then cross validates the best model using stratified K-Fold cross validation.lr_model=model.LogisticRegression(gridsearch={'penalty':['l1','l2']},cv_type="strat-kfold")lr_model.help_debug()# Interface with items to check for to help debug your model.lr_model.metrics()# Views all metrics for the modellr_model.confusion_matrix()lr_model.decision_boundary()lr_model.roc_curve()Running multiple models in parallel# Add a Logistic Regression, Random Forest Classification and a XGBoost Classification model to the queue.lr=df.LogisticRegression(random_state=42,model_name='log_reg',run=False)rf=df.RandomForestClassification(run=False)xgbc=df.XGBoostClassification(run=False)df.run_models()# This will run all queued models in paralleldf.run_models(method='series')# Run each model one after the otherdf.compare_models()# This will display each model evaluated against every metric# Every model is accessed by a unique name that is assiged when you run the model.# Default model names can be seen in the function header of each model.df.log_reg.confusion_matrix()# Displays a confusion matrix for the logistic regression modeldf.rf_cls.confusion_matrix()# Displays a confusion matrix for the random forest modelUsing Pretrained ModelsCurrently you can use pretrained models such as BERT, XLNet, AlBERT, etc. to calculate sentiment and answer questions.df.pretrained_sentiment_analysis(`text_column`)# To answer questions, context for the question has to be supplieddf.pretrained_question_answer(`context_column`,`question_column`)Model InterpretabilityAs mentioned in the Model section, whenever a model is trained you have access to use cases for model interpretability as well. There are prebuild SHAP usecases and an interactive dashboard that is equipped with LIME and SHAP for local model interpretability and Morris Sensitivity for global model interpretability.lr_model=model.LogisticRegression(random_state=42)lr_model.summary_plot()# SHAP summary plotlr_model.force_plot()# SHAP force plotlr_model.decision_plot()# SHAP decision plotlr_model.dependence_plot()# SHAP depencence plot# Creates an interactive dashboard to interpret predictions of the modellr_model.interpret_model()Code GenerationCurrently you are only able to export your model to be ran a service, and will be able to automatically generate the required files. The automatic creation of a data pipeline is still in progress.lr_model.to_service('titanic')Now navigate to 'your_home_folder'('~' on linux and Users/'your_user_name' on windows)/.aethos/projects/titanic/ and you will see the files needed to run the model as a service using FastAPI and uvicorn.InstallationPython Requirements: 3.6, 3.7pip install aethosTo install the dependencies to use pretrained models such as BERT, XLNet, AlBERT, etc:pip install aethos[ptmodels]To install associating corpora for nltk analysis:aethos install-corporaTo install and use the extensions such asqgridfor interactive filtering and analysis with DataFrames:aethos enable-extensionsCurrently working on condas implementation.To create a Data Science project run:aethos createThis will create a full folder strucuture for you to manage data, unit tests, experiments and source code.If experiment tracking is enabled or if you want to start the MLFlow UI:aethos mlflow-uiThis will start the MLFlow UI in the directory where your Aethos experiemnts are run.
NOTE: This only works for local use of MLFLOW, if you are running MLFlow on a remote server, just start it on the remote server and enter the address in the%HOME%/.aethos/config.ymlfile.Development PhasesPhase 1Data Processing techniquesData Cleaning V1Feature Engineering V1Reporting V1Phase 2Data visualizationsModels and EvaluationReporting V2Phase 3Quality of life/addonsCode Generation V1Experiment TrackingPre trained modelsPhase 4Deep learning integrationStatistical TestsRecommendation ModelsCode Generation V2Phase 5Add time series models (i.e ARIMA) and feature engineeringParallelization (Spark, Dask, etc.)Cloud computingGraph based learning and representationPhase 6Web AppThese are subject to change.FeedbackI appreciate any feedback so if you have any feature requests or issues make an issue with the appropriate tag or futhermore, send me an email [email protected] project follows theall-contributorsspecification and is brought to you by theseawesome contributors.SponsorsN/AAcknowledgmentsCredits go to the backbone of open source DataScience and ML: Pandas, Numpy, Scipy, Scikit Learn, Matplotlib, Plotly, Gensim and Jupyter.Community credits go to:@mouradmourafiqfor hispandas-summarylibrary.@PatrikHlobilfor hisPandas-Bokehlibrary.@pandas-profilingfor their automatedEDA report generationlibrary.@slundbergfor hisshapmodel explanation library.@microsoftfor theirinterpretmodel explanation library.@DistrictDataLabsfor theiryellowbrickvisual analysis and model diagnostic tool.@Quantopianfor their interactive DataFrame libraryqgrid.@mwoutsfor their interactive Dataframe libraryitable.@jmcarpenter2for his parallelization librarySwifter.@mlflowfor their model tracking librarymlflow.@huggingfacefor their automated pretrained model librarytransformers.@AutoViMLfor their auto visualization libraryautoviz.@8080labsfor their predictive power score libraryppscore.For DevelopersTo install packagespip3 install -r requirements-dev.txtTo run testspython3 -m unittest discover aethos/ |
aetos_serialiser | UNKNOWN |
ae-transfer-service | transfer_service 0.3.10ae namespace module portion transfer_service: transfer client and server services.installationexecute the following command to install the
ae.transfer_service module
in the currently active virtual environment:pipinstallae-transfer-serviceif you want to contribute to this portion then first forkthe ae_transfer_service repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_transfer_service):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aetros | No description available on PyPI. |
aetst | aetstnamespace-root projectaetst namespace-root: aetst namespace-root grouping testing helper and template portions.aetst namespace root package use-casesthis project is maintaining all the portions (modules and sub-packages) of the aetst namespace to:update and deploy common outsourced files, optionally generated from templatesmerge docstrings of all portions into a single combined and cross-linked documentationpublish documentation via Sphinx ontoReadTheDocsrefactor multiple or all portions of this namespace simultaneously using the grm portions actionsthis namespace-root package is only needed for development tasks, so never add it to the installation requirements
file (requirements.txt) of a project.to ensure the update and deployment of outsourced files generated from the templates provided by this root package via
thegit repository manager tool, add this root package to the
development requirements file (dev_requirements.txt) of a portion project of this namespace.the following portions are currently included in this namespace: |
ae-updater | updater 0.3.13ae namespace module portion updater: application environment updater.installationexecute the following command to install the
ae.updater module
in the currently active virtual environment:pipinstallae-updaterif you want to contribute to this portion then first forkthe ae_updater repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_updater):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-uptime-ce | # Appenlight uptime plugin |
aeurtiaeu | No description available on PyPI. |
aeutils | Failed to fetch description. HTTP Status Code: 404 |
aeval | No description available on PyPI. |
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