Saravanan666/pypi-org huggingface dataset:
Package: ablator-ken-test
ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone git@github.com:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev]
Package: ablator-ken-test2
ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone git@github.com:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev]
Package: ablator-ken-test3
ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone git@github.com:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev]
Package: ablerConfig
ablerConfig简介这是一个用于处理配置信息的Python包。它可以帮助应用程序读取、写入和验证配置文件。该包暂时只供彭彭项目组自用。特点支持JSON5格式的配置文件。可以验证配置文件的格式和内容。可以使用默认值填充缺失的配置项。可以将配置文件转换为Python对象，以便更方便地访问和修改。支持内部配置文件和本地外部配置文件。依赖项pyjson5json5安装您可以使用pip安装abler_config：pipinstallablerConfig使用方法加载配置文件您可以使用ablerConfig.Config()函数加载配置文件。例如：importablerConfigasconfigconfig.Config(default='my-config',local_dir='ex-conf',show_message=my_show_message)引用配置项您可以使用ablerConfig.Config.object_of()方法引用配置文件中的配置对象，也可以使用ablerConfig.Config.value_of()方法将引用配置文件中的简单配置项。例如：fromablerConfigimportConfig,ConfigNodeconf=Config.object_of('alarm_control.state_send')addr=tuple(Config.value_of('net_gate.tt_addr'))
Package: able-recipe
Pythoninterface to Android Bluetooth Low Energy API.Code repository:https://github.com/b3b/ableDocumentation:https://herethere.me/ableChangelog:https://github.com/b3b/able/blob/master/CHANGELOG.rstQuick start development environmentableis included inPythonHereapp, together with theJupyter Notebookit could be used as a development environment.Usage example:https://herethere.me/pythonhere/examples/ble.htmlBuildThe following instructions are for building app withbuildozertool.able_reciperecipe should be added to buildozer.spec requirements:requirements = python3,kivy,android,able_recipeBluetooth permissions should be requested in buildozer.spec:android.permissions = BLUETOOTH, BLUETOOTH_ADMIN, BLUETOOTH_SCAN, BLUETOOTH_CONNECT, BLUETOOTH_ADVERTISE, ACCESS_FINE_LOCATIONApp configuration example:buildozer.specBuild with a local versionTo build app with a local (modified) version ofable,path toablerecipes directory should be set in buildozer.spec:p4a.local_recipes = /path/to/cloned/repo/recipesContributorsThanks,andfmartandreamerellodatmaniac95dgatfdwmoffattEnkumicahelhailesirHelaFayejacklinquanjuasiepoPapoKarloRoberWareRowatarorobgar2001sodefsooko_io
Package: abletonparsing
AbletonParsingParse an Ableton ASD clip file and its warp markers in Python. This module has been tested with.asdfiles saved with Ableton 9 and Ableton 10.Installpip install abletonparsingAPIClip class:.loop_on - ( bool , READ/WRITE ) - Loop toggle is on.start_marker - ( float , READ/WRITE ) - Start marker in beats relative to 1.1.1.end_marker - ( float , READ/WRITE ) - End marker in beats relative to 1.1.1.loop_start - ( float , READ/WRITE ) - Loop start in beats relative to 1.1.1.loop_end - ( float , READ/WRITE ) - Loop end in beats relative to 1.1.1.hidden_loop_start - ( float , READ/WRITE ) - Hidden loop start in beats relative to 1.1.1.hidden_loop_end - ( float , READ/WRITE ) - Hidden loop end in beats relative to 1.1.1.warp_markers - ( list[WarpMarker] , READ/WRITE ) - List of warp markers.warp_on - ( bool , READ/WRITE ) - Warping is on.sr - ( float , READ/WRITE ) - Sample rate of audio dataWarpMarker class:.seconds - ( float , READ/WRITE ) - Position in seconds in the audio data..beats - ( float , READ/WRITE ) - Position in "beats" (typically quarter note) relative to 1.1.1Ifloop_onis false, thenloop_startwill equal thestart_marker, andloop_endwill equal theend_marker.Exampleimportabletonparsingimportlibrosaimportsoundfileassfimportpyrubberbandaspyrbbpm=130.audio_path='drums.wav'clip_path=audio_path+'.asd'audio_data,sr=librosa.load(audio_path,sr=None,mono=False)num_samples=audio_data.shape[1]clip=abletonparsing.Clip(clip_path,sr,num_samples)time_map=clip.get_time_map(bpm)# Time-stretch the audio to the requested bpm.output_audio=pyrb.timemap_stretch(audio_data.transpose(),sr,time_map)withsf.SoundFile('output.wav','w',sr,2,'PCM_24')asf:f.write(output_audio)
Package: ablilcalculator
My python packageGet startedPre-requisitesInstall necessary dependencies for developementpipinstalltwinebuildsetuptoolsDevelopClone project and reset git historygitclonehttps://github.com/ablil/python-starterpypkg&&cdpypkg&&rm-rf.git&&gitinit&&gitadd.&&gitcommit-m'initial commit'Run your testsBuild and publishBuildrm-rfdist&&python-mbuildPublish to TestPyPipython-mtwineupload--repositorytestpypidist/*Publish to PyPipython-mtwineupload--repositorytestpypidist/*
Package: ablk
Matthew Oertle <moertle@gmail.com>ablk is a image viewer that uses the UTF-8 block character and 24-bit ANSI escape sequences to render an image in the terminal window. Useful over SSH into a remote machine to get an idea of what an image file looks like.usage: ablk [-h] [–delay DELAY] [–no-name] images [images …]positional arguments:images                Image filesoptional arguments:-h,--helpshow this help message and exit--delayDELAY,-dDELAYDelay between each image--no-name,-nShow filename of image
Package: ablog
ABlog is a Sphinx extension that converts any documentation or personal website project into a full-fledged blogPlease check our documentation for information on how to get started.NoteThis is the new home ofAhmet Bakan’s Ablog Sphinx extension.
The original project is no longer maintained and theSunPy Projecthas taken over maintenance.WarningThis version is maintained with the aim to keep it working for SunPy Project website and thus new features or bugfixes are highly unlikely unless they directly impact.We strongly encourage users and interested in parties in submitting patches to ``ablog``.
Package: ablpywrapper
Astro Bot List Python WrapperThis is a wrapper for astro bot list made for pythonGet Bot StatsfrommainimportBotlistsabl=Botlists('api key')x=abl.get()print(x)Post Server CountfrommainimportBotlistsabl=Botlists('api key')x=abl.count(12)print(x)
Package: abl-quantconnect-stubs
QuantConnect StubsThis package contains type stubs for QuantConnect'sLeanalgorithmic trading engine and for parts of the .NET library that are used by Lean.These stubs can be used by editors to provide type-aware features like autocomplete and auto-imports in QuantConnect strategies written in Python.After installing the stubs, you can copy the following line to the top of every Python file to have the same imports as the ones that are added by default in the cloud:fromAlgorithmImportsimport*This line importsall common QuantConnect membersand provides autocomplete for them.
Package: ably
A Python client library for Ably Realtime messaging.SetupYou can install this package by using the pip tool and installing:pip install ablyUsing Ably for PythonSign up for Ably athttps://ably.com/sign-upGet usage examples athttps://github.com/ably/ably-pythonVisithttps://ably.com/docsfor a complete API reference and more examples
Package: abmap
AbMAP: Antibody Mutagenesis-Augmented ProcessingThis repository is a work in progress.This repository contains code and pre-trained model checkpoints for AbMAP, a Protein Language Model (PLM) customized for antibodies as featured inLearning the Language of Antibody Hypervariability(Singh, Im et al. 2023). AbMAP leverages information from foundational PLMs as well as antibody structure and function, offering a multi-functional tool useful for predicting structure, functional properties, and analyzing B-cell repertoires.InstallationAbMAP relies on ANARCI to assign IMGT labels to antibody sequences. Please see theANARCIrepo or run the following in a new conda environment:condainstall-cbiocorehmmer# Can also install using `brew/port/apt/yum install hmmer`gitclonehttps://github.com/oxpig/ANARCI.gitcdANARCI
pythonsetup.pyinstallThen install abmap using:pipinstallabmap# (recommended) latest release from PyPIpipinstallgit+https://github.com/rs239/ablm.git# the live main branchUsage:After installation, AbMAP can be easily imported into your python projects or run from the command line. Please seeexamples/demo.ipynbfor common use cases. Instructions for running via CLI are below.Command Line UsageInstructions In ProgressAugmentGiven a sequence, generate a foundational PLM embedding augmented with in-silico mutagenesis and CDR isolation.TrainGiven a dataset of labeled pairs of sequences and their augmented embeddings, train the AbMAP model on downstream prediction tasks.EmbedGiven fasta sequences and a pre-trained AbMAP model, generate their AbMAP embeddings (fixed or variable).Please provide feedback on the issues page or by opening a pull request. If AbMAP is useful in your work, please consider citing ourbioRxiv preprint.
Package: abmarl
AbmarlAbmarl is a package for developing Agent-Based Simulations and training them with
MultiAgent Reinforcement Learning (MARL). We provide an intuitive command line
interface for engaging with the full workflow of MARL experimentation: training,
visualizing, and analyzing agent behavior. We define an Agent-Based Simulation
Interface and Simulation Manager, which control which agents interact with the
simulation at each step. We support integration with popular reinforcement learning
simulation interfaces, including gym.Env, MultiAgentEnv, and OpenSpiel. We define
our own GridWorld Simulation Framework for creating custom grid-based Agent Based
Simulations.Abmarl leverages RLlib’s framework for reinforcement learning and extends it to
more easily support custom simulations, algorithms, and policies. We enable researchers to rapidly
prototype MARL experiments and simulation design and lower the barrier for pre-existing
projects to prototype RL as a potential solution.QuickstartTo use Abmarl, install via pip:pip install abmarlTo develop Abmarl, clone the repository and install via pip's development mode.git clone git@github.com:LLNL/Abmarl.git
cd abmarl
pip install -r requirements/requirements_all.txt
pip install -e . --no-depsTrain agents in a multicorridor simulation:abmarl train examples/multi_corridor_example.pyVisualize trained behavior:abmarl visualize ~/abmarl_results/MultiCorridor-2020-08-25_09-30/ -n 5 --recordNote: If you install withconda,then you must also includeffmpegin your
virtual environment.DocumentationYou can find the latest Abmarl documentation onour ReadTheDocs page.CommunityCitationAbmarl has been published to the Journal of Open Source Software (JOSS). It can
be cited using the following bibtex entry:@article{Rusu2021,
  doi = {10.21105/joss.03424},
  url = {https://doi.org/10.21105/joss.03424},
  year = {2021},
  publisher = {The Open Journal},
  volume = {6},
  number = {64},
  pages = {3424},
  author = {Edward Rusu and Ruben Glatt},
  title = {Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning},
  journal = {Journal of Open Source Software}
}Reporting IssuesPlease use our issue tracker to report any bugs or submit feature requests. Great
bug reports tend to have:A quick summary and/or backgroundSteps to reproduce, sample code is best.What you expected would happenWhat actually happensContributingPlease submit contributions via pull requests from a forked repository. Find out
more about this processhere.
All contributions are under the BSD 3 License that covers the project.ReleaseLLNL-CODE-815883
Package: abm-colony-collection
Collection of tasks for analyzing colony dynamics.
Designed to be used both inPrefectworkflows and as modular, useful pieces of code.InstallationThe collection can be installed using:pipinstallabm-colony-collectionWe recommend usingPoetryto manage and install dependencies.
To install into your Poetry project, use:poetryaddabm-colony-collectionUsagePrefect workflowsAll tasks in this collection are wrapped in a Prefect@taskdecorator, and can be used directly in a Prefect@flow.
Running tasks within aPrefectflow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.fromprefectimportflowfromabm_colony_collectionimport<task_name>@flowdefrun_flow():<task_name>()if__name__=="__main__":run_flow()Seecell-abm-pipelinefor examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.Individual tasksNot all use cases require a full workflow.
Tasks in this collection can be used without the Prefect@taskdecorator by simply importing directly from the module:fromabm_colony_collection.<task_name>import<task_name>defmain():<task_name>()if__name__=="__main__":main()or using the.fn()method:fromabm_colony_collectionimport<task_name>defmain():<task_name>.fn()if__name__=="__main__":main()
Package: abm-initialization-collection
Collection of tasks for initializing ABM simulations.
Designed to be used both inPrefectworkflows and as modular, useful pieces of code.InstallationThe collection can be installed using:pipinstallabm-initialization-collectionWe recommend usingPoetryto manage and install dependencies.
To install into your Poetry project, use:poetryaddabm-initialization-collectionUsagePrefect workflowsAll tasks in this collection are wrapped in a Prefect@taskdecorator, and can be used directly in a Prefect@flow.
Running tasks within aPrefectflow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.fromprefectimportflowfromabm_initialization_collection.<module_name>import<task_name>@flowdefrun_flow():<task_name>()if__name__=="__main__":run_flow()Seecell-abm-pipelinefor examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.Individual tasksNot all use cases require a full workflow.
Tasks in this collection can be used without the Prefect@taskdecorator by simply importing directly from the module:fromabm_initialization_collection.<module_name>.<task_name>import<task_name>defmain():<task_name>()if__name__=="__main__":main()or using the.fn()method:fromabm_initialization_collection.<module_name>import<task_name>defmain():<task_name>.fn()if__name__=="__main__":main()
Package: abm-shape-collection
Collection of tasks for analyzing cell shapes.
Designed to be used both inPrefectworkflows and as modular, useful pieces of code.InstallationThe collection can be installed using:pipinstallabm-shape-collectionWe recommend usingPoetryto manage and install dependencies.
To install into your Poetry project, use:poetryaddabm-shape-collectionUsagePrefect workflowsAll tasks in this collection are wrapped in a Prefect@taskdecorator, and can be used directly in a Prefect@flow.
Running tasks within aPrefectflow enables you to take advantage of features such as automatically retrying failed tasks, monitoring workflow states, running tasks concurrently, deploying and scheduling flows, and more.fromprefectimportflowfromabm_shape_collectionimport<task_name>@flowdefrun_flow():<task_name>()if__name__=="__main__":run_flow()Seecell-abm-pipelinefor examples of using tasks from different collections to build a pipeline for simulating and analyzing agent-based model data.Individual tasksNot all use cases require a full workflow.
Tasks in this collection can be used without the Prefect@taskdecorator by simply importing directly from the module:fromabm_shape_collection.<task_name>import<task_name>defmain():<task_name>()if__name__=="__main__":main()or using the.fn()method:fromabm_shape_collectionimport<task_name>defmain():<task_name>.fn()if__name__=="__main__":main()