Saravanan666/pypi-org huggingface dataset:
Package: algo-ops
Algo-OpsPipeline Infrastructure for Prototyping and Deploying AlgorithmsYou like algorithms. But prototyping complicated algorithms can be challenging, especially when they have complicated
architectures with many components. How do you prototype algorithms so that the data flow is sensible, the results are
interpretable, and the algorithm is easily debuggable on incorrect predictions?Algo-Ops has the following features:An algorithm is a recipe for executing a computation, generally consisting of several steps. In Algo-Ops, each step is
an Op.Ops are automic units of an algorithm. Various types of Ops are supported such as TextOps (for NLP) and CVOps (for
computer vision). The user can easily add their own Ops.Ops are linked together to form an algorithm. They execute sequentially where the first Op's outputs are passed as the
second Op's inputs. The feed-forward pipeline is run to execution.pipeline=Pipeline.init_from_funcs(funcs=[self.append_a,self.append_something,self.reverse,self.reverse],op_class=TextOp,)Algo-Ops pipelines automatically dashboard in a Jupyter notebook. Simply do pipeline.vis() to visualize the data flow
in a Jupyter notebook, making it easy to visualize data flow and debug an algorithmic edge case on some input.pipeline.vis()Algo-Ops pipelines automatically keep a profile of their own performance over time. Simply do pipeline.vis_profile()
to see a profile of your Op executions in terms of runtime and memory usage.pipeline.vis_profile()Algo-Ops pipeline supports easy debugging. A pipeline can be run on a set of supervised inputs with known outputs. In
this case, if the algorithm got the wrong prediction, the entire pipeline dataflow is auto-pickled to allow the user
to investigate where the algorithm went wrong.An Algo-Ops pipeline is itself an Op, so pipelines themselves can be stacked to create very intricate workflows that
are still easy to track and debug. The entire framework is highly extensible.
Package: algopack
Implementation of various common CS algorithms in CythonUsageInstallation$pipinstallalgopackExamplesSorting:fromalgopackimportsortmy_array=[10,0,5,15,-5]sorted_array=sort.bubble(my_array)print(sorted_array)# [-5, 0, 5, 10, 15]TestingClone the repository:$ git clone https://github.com/divykj/algopack.git
$ cd algopackInstall requirements:$ pip install -r requirements.txt
$ pip install -r requirements_dev.txtRun tests:$ doit testsModulessortbubblebucketcocktail_shakercombcountinggnomeheapinsertionmergepancakequickradixselectionshell
Package: algopy
ALGOPY is a tool for Algorithmic Differentiation (AD) and Taylor polynomial approximations.
ALGOPY makes it possible to perform computations on scalar and polynomial matrices.
It is designed to be as compatible to numpy as possible. I.e. views, broadcasting and most
functions of numpy can be performed on polynomial matrices. Exampels are dot,trace,qr,solve,
inv,eigh.
The reverse mode of AD is also supported by a simple code evaluation tracer.Documentation with examples is available athttp://packages.python.org/algopy/.
Package: algoql
algoqlAlgorand streaming Query LanguageUsagepython main.py <query>Get current block roundSELECTb.rndFROMblockASb;Output:{'rnd': 25242464}
{'rnd': 25242465}Get decoded transaction notes:SELECTBASE64DECODE(txns.txn.note,"utf-8")ASnoteFROMblock.txnsAStxnsWHEREBASE64DECODE(txns.txn.note,"utf-8")ISNOTNULL;Output:{'note': 'Reach 0.1.11'}
Package: algorand-governance-oracle
Algorand Governance OracleIt is an "oracle" for Algorand Governance.Mainnet Application ID:will go hereTestnet Application ID:109903471Application StateWe'll have how to use application state in other application.Development and UpdateHow to create new applicationfromalgorand_governance_oracle.deploymentimportcreate_oracle_applicationfromalgorand_governance_oracle.clientsimporttestnet_algod_clientprivate_key="put your private key in here."application_id=create_oracle_application(algod_client=testnet_algod_client,private_key=private_key)print(application_id)How to update application state with statisticsfromalgorand_governance_oracle.apiimportupdatefromalgorand_governance_oracle.clientsimporttestnet_algod_clientprivate_key="put your private key in here."application_id="put your application id in here."values={"period_identifier":"governance-period-4".encode(),"eligible_committed_stake":3788144125919121,"ineligible_committed_stake":295846447001842,"eligible_governor_count":32677,"ineligible_governor_count":1923,"algo_amount_in_reward_pool":70500000000000,"updated_at":1662985556,}transaction_id=update(algod_client=testnet_algod_client,private_key=private_key,application_id=application_id,values=values,)print(transaction_id)
Package: algorand-vanity
Algorand VanityPython utility for generating vanity algorand wallet addresses.Installing from sourcepoetryinstallInstalling with pippipinstallalgorand-vanityUsagealgorand_vanityADDRESS1ADDRESS2OptionsOptionDescriptionDefault--threads, -t {start, end}Number of threads to use for address generation# of CPU cores--filename, -f {start, end}Filename to output addresses tovanity_addresses--location, -l {start, end}location in address where the vanity string should be foundif not specified then string can be anywhere in the addressvanitylist of vanity addresses to search for(Must only contain the following characters: A-Z, 2-7)required
Package: algora-sdk
Algora LabsAlgora Labsis a strategy development platform for trading. Build, test and launch
complex trading strategies in minutes instead of days or weeks. We manage your environment, so you can focus more time
on building profitable strategies.FeaturesCreate complex, multi-asset class strategies with instruments of any typeTrack position- and portfolio- level risksManage cash payments and slippageDesign and develop custom instrumentsIntegrate external data and pricers without exposing any proprietary logicSDKThealgora-sdkallows users to programmatically access our APIs and data.NoteOur quantitative finance framework (strategy development and backtesting) is only available through our platformInstallationSet the following environment variables for your username and password forAlgora Labs.ALGORA_USER=<username>
ALGORA_PWD=<password>HelpIf you need any help or have feedback, please email us atsupport@algoralabs.com.
Package: algorename
AlgoRenameTable of ContentsIntroductionPrerequisitesInstallationUsageCommand Line ArgumentsAlgorithm LoadingAlgorithm ListingMultiprocessingLoggingLicenseIntroductionThis utility is a Python script designed to rename files in a folder or an individual file based on a dynamically loaded algorithm. It utilizes multiprocessing for efficiency and can operate recursively on directories. The example algorithm shifts each alphabetic character in the filenames to the left by 1.PrerequisitesPython 3.6+InstallationClone the GitHub repository.git clone https://github.com/darkarp/algorename.gitNavigate to the project directory.cd algorenameInstall the required Python packages.pip install -r requirements.txtUsageCommand Line ArgumentsTo rename a single file:python algorename.py -f [path/to/your/file]To rename all files in a directory:python algorename.py -d [path/to/your/directory]To rename files recursively in a directory:python algorename.py -d [path/to/your/directory] -rFor silent mode (suppress errors):python algorename.py -s (...)To not create a log file:python algorename.py -nl (...)To use a specific algorithm:python algorename.py -a [algorithm_name]To list available algorithms:python algorename.py -lFor help:python algorename.py -hAlgorithm LoadingThe script supports loading custom algorithms. Place your algorithm module under thealgorithms/folder, and the script will be able to import it dynamically. Make sure to define a function calledapply_algorithmwithin the module.The environment variableFCLOG_PATHspecifies the directories where the script will look for custom algorithm modules. You can modify this by changing your environment variables or by creating a.envfile.Example:Windows:FCLOG_PATH=C:\path\to\algorithms;D:\another\path\to\algorithmsLinux/Mac:FCLOG_PATH=/path/to/algorithms:/another/path/to/algorithmsAlgorithm ListingYou can list all available algorithms along with their metadata using the-lflag. This is useful for understanding what each algorithm does before using it.MultiprocessingThis utility uses multithreading to efficiently rename multiple files in a directory. It usesconcurrent.futures.ThreadPoolExecutorto speed up the renaming tasks.LoggingLogging is implemented with a rotating log file mechanism to ensure that it doesn't consume too much disk space over time. The log file will be generated in the same directory as the script, namedalgorename.log.You can set the logging verbosity level via an environment variableFCLOG_LEVEL, which accepts values likeDEBUG,INFO,WARNING,ERROR, andCRITICAL.You can set the log file name via an environment variableFCLOG_NAME. This name will be converted to lower-case.You can disable logging by specifying-nlargument.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details.
Package: algorithm-analyse
# 验证算法公平性与完整性
# 开始# 传入pandas dateframe作为数据集
dataset = Analyse(pandas_dataframe)# 验证完整性
result = Analyse.completeness(col_name)
# col_name 数据列名，返回有多少空值# 验证公平性
dataset.new_test
# 将pandas_dataframe中的敏感属性所在列随机打乱，重新赋值，然后再进行实验# 查看数据分布
dataset.dis_analyse(‘x’, ‘y’)
# 输出y相对于x的分布图像# 比较两次实验结果差异
dataset.compare(x, y)
# 输入两次结果x, y(二者均为torch向量)，比较KL散度，越大说明相差越大
Package: algorithm-prep
#Algorithm Training for PythonThis project is for programmers and students to practice the programming of algorithms. The test cases and example implementation are included and ready to use.Installation$ pip install algorithm-trainingUsageTest your own algorithmLet's take sorting algorithm as an example. You can create your own algorithm in a function, then add a main block to test your function.from algorithm_training import test
from algorithm_training.classic.sort import test_cases

def my_sort_function(arr):
    # my implementation
    return arr

if __name__ == '__main__':
    test(my_sort_function, test_cases)Benchmark your algorithm against reference implementationsfrom algorithm_training import benchmark
from algorithm_training.classic.sort import algorithms, test_cases

def my_sort_function(arr):
    # my implementation
    return arr

if __name__ == '__main__':
    benchmark(my_sort_function, algorithms, test_cases)Implemented AlgorithmsSortFollowing import statement should be included in your code.from algorithm_prep import test, benchmark
from algorithm_prep.classic.sort import test_cases, algorithmsDefine your sorting functiondef your_sort_algorithm(arr):
	"""
    :type arr: List[int]
    :rtype: List[int]
    """
	# your codes go hereTo test the functiontest(your_sort_algorithm, test_cases)To benchmark your algorithmbenchmark(your_sort_algorithm, algorithms, test_cases)Following reference algorithms are implemented for benchmarkBubble SortInsertion SortMerge SortQuick SortHeap SortRadix SortString Search
Package: algorithmx
AlgorithmX PythonAlgorithmX Pythonis a library for network visualization and algorithm simulation, built onAlgorithmX. It can be used through a HTTP/WebSocket
server, or as a widget in Jupyter Notebooks and JupyterLab.ResourcesWebsiteDocumentationInstallationPython 3.7.0 or higher is required. Using pip:python -m pip install algorithmxJupyter WidgetIn classic Jupyter notebooks, the widget will typically be enabled by default. To enable it
manually, runjupyter nbextension enable --sys-prefix --py algorithmxwith theappropriate flag. To enable in JupyterLab, runpython -m jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
python -m jupyter lab buildExample UsageIf you wish to use the library through a HTTP/WebSocket server, follow the template below:importalgorithmxserver=algorithmx.http_server(port=5050)canvas=server.canvas()defstart():canvas.nodes([1,2]).add()canvas.edge((1,2)).add()canvas.onmessage('start',start)server.start()The graphics can be found athttp://localhost:5050/.If you are using Jupyter, try adding the following code to a cell:fromalgorithmximportjupyter_canvascanvas=jupyter_canvas()canvas.nodes([1,2]).add()canvas.edge((1,2)).add()canvasDevelopmentManual installMake sure you have Python 3.7 of higher, then run# build js
cd js
npm run build
cd ..

# install dependencies
python -m pip install -r dev-requirements.txt
python -m pip install --no-deps --editable ".[jupyter,networkx]"HTTP ServerDocker:Docker-compose up http-serverManually:python examples/basic_http_server.pyThen openlocalhost:5050in a browser.Jupyter notebookDocker:docker-compose up notebookManually:python -m jupyter nbextenion list
python -m jupyter notebookThen try openingexamples/basic_notebook.ipynb.Jupyter labDocker:docker-compose up jupyter-labManually:python -m jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
python -m jupyter lab build
python -m jupyter labextension list
python -m jupyter labThen try openingexamples/basic_notebook.ipynb.Build and test packageClean any previous builds withrm -rf build dist.Docker:docker-compose up --build buildManually:python -m mypy .
python -m pytest tests -vv
python setup.py build sdist bdist_wheelThe bundle can be found indist/.DistributeSet up pre-commit hooks:pre-commit installPublish to PyPI:./scripts/deploy.sh
Package: algorithm-x
Algorithm XAn efficient Python implementation ofAlgorithm
X, which finds solutions to instances of theExact Cover problem.Installation$pipinstallalgorithm-xUsagefromalgorithm_ximportAlgorithmXsolver=AlgorithmX(7)solver.appendRow([2,4,5],'row 1')solver.appendRow([0,3,6],'row 2')solver.appendRow([1,2,5],'row 3')solver.appendRow([0,3],'row 4')solver.appendRow([1,6],'row 5')solver.appendRow([3,4,6],'row 6')forsolutioninsolver.solve():print(solution)
Package: algoritmia
BibliotecaalgoritmiaImplementada en Python 3.Utilizada en dos asignaturas de la Universitat Jaume I de Castelló:EI1022: Grado en Ingeniería Informática.MT1022: Grado en Matemática Computacional.ContenidoEstructuras de datos:Colas: Fifo, LifoListas enlazadas: LinkedListGrafos: Digraph, UndirectedGraphMontículos: MinHeap, MaxHeapDiccionarios de prioridad: MinHeapMap, MaxHeapMapConjuntos disjuntos: MFSetAlgoritmos sobre grafos:Recorridos de vértices y aristasComponentes conexosÁrbol de recubrimiento mínimoCamino más cortoAlgoritmo de DijkstraEsquemas algorítmicos:Búsqueda con retroceso (backtracking)Ramificación y acotación (brand and bound)Divide y vencerás (divide and conquer)Reduce y vencerás (reduce and conquer)Problemas:Mochila (Knapsack)Cambio de moneda (Coin change)Empaquetado (Bin packing)Viajante (Travelling salesman)N-reinas (N-queens)Coloreado de grafos (Graph coloring)Voraces exactos: MST (Kruskal y Prim), mochila fraccionaria
Package: algoritmika-quiz-package
Math quiz packageMy unique package with unique functionality, which wasn't onhttps://pypi.org/In variableOPERATIONS_DICTstore user and math operations.random_calculatorfunction generating random numbers for user, and choice random math operation fromOPERATIONS_FOR_USERlist, where storedOPERATIONS_DICTkeys with user operations -+  - * /.After generating numbers and choosing math operation calculated result, and ask user for his
answer in functionuser_poll.Usingquizfunc to poll user required number of times and for count his correct answersUse your mind
Package: algorsort
Sort lists with Algorsort
supports number lists and word lists
batteries not includedcreated by:
Jased#0001
and Github CopilotChangelogtip: dates are in MM/DD/YYYY format.2 (11/20/2022)
You know it’s not the same as it was
——————-
- Started adding support for words
- Seperated words and numbers into classes
- Changed readme
- Added my new email and website1 (8/5/2022)
Sodium Cloride
——————-
- released :pog:
Package: algorthm
algorthmA python package for visualizing and learning algorithms.AuthorsDinak Lal V -[www.dinaklal.in]dinaklal@gmail.comUsageInstall package -pip install algorthmImport into your code -import algorthmUse methods. Default case the algorithms in this package give you step wise result so that one
can understand the back ground operations-algorthm.sort.bubbleSort(list)For using  algorithms without stepwise result, pass "False" as last argument
eg -:algorthm.sort.bubbleSort(list,False)
Package: algorun
Algorun CLITHIS IS IN BETAIs this for me?This tool simplifies setting up and starting an Algorand mainnet node. You should know your way around a CLI if you're planning to use itInstallPrerequisitesThe key required dependency is Python 3.10+, but some of the installation options below will install that for you.Algorun also has some runtime dependencies that also need to be available for particular commands.Docker - Docker Compose (and by association, Docker) is used to run the Algorand mainnet container, we require Docker Compose 2.5.0+Pipx - a better package manager than pip that you'll use to install the cliInstall Algorun with pipx on any Mac, Linux and Windows subsystem for LinuxEnsure desired prerequisites are installedPython 3.10+pipxDockerInstall using pipxpipx install algorunRestart the terminal to ensure Algorun is available on the pathVerify installationMaintenanceTo update Algorun:pipx upgrade algorunTo remove Algorun:pipx uninstall algorunVerify installationVerify Algorun is installed correctly by runningalgorun --versionand you should see output similar to:algorun, version 0.1UsageCreate a directory where you're comfortable keeping the node config and files, we suggest naming italgorand, open that directory in a terminalalgorun startwill start your node by creatingdocker-compose.yml,config.jsonfiles and adatadirectory where your node will persist.algorun stopwill shut down your nodealgorun goalis a wrapper for theGoal CLItypingalgorun goal node statuswill return your nodes status, typingalgorun goal node status -w 1000instead will keep giving you node status updates every 1 secondNoteIf you get receive one of the following errors:command not found: algorun(bash/zsh)The term 'algorun' is not recognized as the name of a cmdlet, function, script file, or operable program.(PowerShell)Then ensure thatalgorunis available on the PATH by runningpipx ensurepathand restarting the terminal.If you're experiencing issues with algorunraise an issue.
Package: algorythm
AlgoRythmAlgoRythm is a real-time audio visualizer with customizable features and Windows media integration to graphically display information about audio levels and media.Made using Python while utilizing performant programming techniques.CIS4930 - Performant Programming with Python - UF Summer 2021Project ResourcesOutline:https://docs.google.com/document/d/1MjfzGPgUQ3EDTRZ4OSIiPHjVnTdgo9IleFzqqXE-JPoTestPyPI link:https://test.pypi.org/project/algorythm/Project Repository link:https://github.com/cburrows1/AlgoRythmInstallation instructionsInstall Python 3.8 or LaterManually install pyaudio (see section below)On Windows enable Stereo mix (or skip this step and Algorythm will visualize your default input device):Control panel -> Hardware and Sound -> Sound -> Recording -> Stereo Mix -> *Right Click* EnableIf Stereo Mix does not appear under recording devices, follow the Driver Installation Instructions belowRunpip install -i https://test.pypi.org/simple/ algorythmORDownload the latest wheel package from ourrelease listNavigate to the downloaded package and install withpip install <filename>.whlPyAudio Installation InstructionsDownload PyAudio whl fromhttps://www.lfd.uci.edu/~gohlke/pythonlibs/#pyaudiocorresponding to your python versionNavigate to download directoryInstall it withpip install <filename>.whlRealtek Stereo Mix Driver Installation InstructionsCheck that it isn't disabled by right clicking, then select "Show Disabled Devices"If it shows up, then enable it. Otherwise it is necessary to download the driver from Realtek:Download the driver executable (64 bit) fromRealtekor aMuch Faster MirrorRun the executable and follow the installation instructionsRestart Computer and continue with the Algorythm installation instructions aboveExecution of programRun withpython -m algorythmChanges to settings are saved to a file,algorythm_settings, in the current working directory.If no bars appear, even with Stereo Mix enabled, a USB DAC or audio device is likely interfering and bypassing RealTek. Unplug that device and connect to AUX instead. Restart Algorythm.
Package: algosec
docstestspackageA Python SDK providing simple access to AlgoSec APIs, including handy methods
to implement common network security policy management tasks, such as:Check whether specific traffic is allowed by the firewalls and security devices in the network.Open a network security change request.Check status of existing change requests.Update business application connectivity requirements (and automatically trigger change requests as needed)Useful for automation and orchestration (e.g. DevOps), building custom portals, or exposing specific functionality to Application Owners, IT, Helpdesk, Information Security, Security Operations, etc.Included in this package are clients for AlgoSec Firewall Analyzer, FireFlow and BusinessFlow.InstallationInstall the latest version from PyPi by running:pip install algosec --upgradeor clone this repo and run:python setup.py installContributionContributions are welcome! Please follow the standard pull request process.DocumentationDocumentation available online at:https://algosec-python.readthedocs.io/en/latest/How to build doc’s locally?Using Spinx:$ cd docs
$ make htmlThen see thedocs/_buildfolder created for the html files.DevelopingTo install the package for local development just run:pipenv installThis will install all the dependencies and set the project up for local usage and development .TestingTo run the unittests for all supported python versions, simply run:tox
Package: algoseek-connector
Algoseek ConnectorA wrapper library for ORM-like SQL builder and executor.
The library provides a simple pythonic interface to algoseek datasets with custom data filtering/selection.Supported FeaturesThe following query operations on datasets are supported:Selecting columns and arbitrary expressions based on columnsFiltering by column value/column expressionGrouping by column(s)Sorting by column(s)All common arithmetic, logical operations on dataset columns and function applicationFetching query results as a pandas DataFrameInstallationalgoseek-connectoris available on the Python Package Index. Install it using
thepipcommand:pip install algoseek-connectorDocumentationDocumentation is availablehere.Dev installationalgoseek-connectoris installed usingPoetry.A Makefile recipe is available to install the package in developer mode along
with developer dependencies:makedev-installIfmakeis not available, run:poetry install --with dev,docs
pre-commit installTestingRefer to the README inside the tests directory.Building the docsThe documentation is generated using the sphinx library. First, install
the necessary dependencies with the following command:poetryinstall--withdocsBuild the documentation using the Makefile located in thedocsdirectory:makehtmlPublishing to pypiIn order to pubish a new package version to pypi:update library version inpyproject.toml(note, we use semantic versioning with where version numbers correspond to major, minor and patch)runpoetry buildto create a packageconfigure your pypi credentials for poetry withpoetry config http-basic.pypi <username> <password>runpoetry publishto publish the library to PyPI