Saravanan666/pypi-org huggingface dataset:
Package: aimaxcli
SetupFirst, you need to create a virtual environment and activate it.$ pip install virtualenv
$ virtualenv .venv
$ . .venv/bin/activate
(.venv)$Next, installcliffin the environment.(.venv)$ python setup.py installNow, install the demo application into the virtual environment.(.venv)$ cd demoapp
(.venv)$ python setup.py installUsageWith cliff and the demo setup up, you can now play with it.To see a list of commands available, run:(.venv)$ cliffdemo --helpOne of the available commands is “simple” and running it(.venv)$ cliffdemo simpleproduces the followingsending greeting
hi!To see help for an individual command, include the command name on the
command line:(.venv)$ cliffdemo files --helpCleaning UpFinally, when done, deactivate your virtual environment:(.venv)$ deactivate
$
Package: aimbase
Getting StartedTo get started withaimbasefor your application, visit the docs athttps://aimbase.erob.io/To contribute toaimbase, see thecontributingsection below.ContributingGetting Started LocallyLaunch postgres and pgadmin via docker-composedocker-compose up --build.Keeping your containers running, open a new terminal with the root of this repo as the working directory. Installpoetry:pip install poetry(or usepipxon link hereif you prefer isolated envs, or consider usingconda).Create and enter the virtual environment:poetry shellInstall the dependenciespoetry installStart the app:uvicorn examples.example_app:auto_app --reload.Openlocalhost:8000/v1/docsand start interacting with swagger!You can shut down and your db / minio data will persist via docker volumes.MinIOThe MinIO console is available atlocalhost:9001.  Login with user:miniouserand password:minioadminif you launched the containers withdocker-compose up --build.Hooks and TestsSet up the precommit hook withpre-commit install.Run tests and get coverage withpytest --cov, and get html reports for vs code live server (or any server) withpytest --cov --cov-report=html:coverage_reOpen a pull request against the repo!  Please write tests, your coverage will automatically be added as a comment to any PR via GH actions.Viewing Docs LocallyInstall docs dependencies withpip install -r requirements-docs.txt.Installmkdocswithpip install mkdocs-material.Move into thedocs/endirectory viacd docs/en.Runmkdocs serveto start a local server.
Package: aimbat
AIMBATAIMBAT (Automated and Interactive Measurement of Body wave Arrival Times) is an
open-source software package for efficiently measuring teleseismic body wave arrival
times for large seismic arrays[1]. It is based on a widely used method called
MCCC (Multi-Channel Cross-Correlation)[2]. The package is automated in the sense
of initially aligning seismograms for MCCC, which is achieved by an ICCS (Iterative Cross
Correlation and Stack) algorithm. Meanwhile, a GUI (graphical user interface) is built to
perform seismogram quality control interactively. Therefore, user processing time is
reduced while valuable input from a user's expertise is retained. As a byproduct, SAC[3]plotting and phase picking functionalities are replicated and enhanced.Modules and scripts included in the AIMBAT package were developed usingPythonand its open-source modules on the Mac OS X platform
since 2009. The original MCCC[2]code was transcribed into Python.
The GUI of AIMBAT was inspired and initiated at the2009 EarthScope USArray Data Processing and Analysis Short Course.
AIMBAT runs on Mac OS X, Linux/Unix and Windows thanks to the platform-independent
feature of Python.For more information visit theproject websiteor thepysmo repositories.Authors' ContactsXiaoting LouEmail: xlou at u.northwestern.eduSuzan van der LeeEmail: suzan at northwestern.eduSimon LloydEmail: simon at slloyd.netContributorsLay Kuan LohReferences[1]Xiaoting Lou, Suzan van der Lee, and Simon Lloyd (2013),
AIMBAT: A Python/Matplotlib Tool for Measuring Teleseismic Arrival Times.Seismol. Res. Lett., 84(1), 85-93, doi:10.1785/0220120033.[2]VanDecar, J. C., and R. S. Crosson (1990),
Determination of teleseismic relative phase arrival times using multi-channel
cross-correlation and
least squares.Bulletin of the Seismological Society of America, 80(1), 150–169.[3]Goldstein, P., D. Dodge, M. Firpo, and L. Minner (2003),
SAC2000: Signal processing and analysis tools for seismologists and engineers,International Geophysics, 81, 1613–1614.
Package: aimd
AimAim is an AI deployment and version control system.
It can handle both small and large projects through their whole life cycle with efficiency and speed.
It is built to seamlessly blend in with existing ML stack and become an integral part of the development lifecycle.Aim CLIAim CLI is a command line tool for building end-to-end AI.
Aim is built to be:
compatible with the existing ecosystem of tools
be familiar
just work
make building AI productiveAim has three main features: tracking of training, export and deploy.Tracking - ML TrainingCommand:aim trainAim train runs training for the given aim repository. Aim train tracks the gradients and updates in the model with given interval and saves them for visualization and analysis.
Aim Train is paired with UI that visualizes the artifacts tracked.
Aim Tracking is used to debug and have a detailed understanding of the process of training.Export - ML ModelCommand:aim exportAim export creates the saved model checkpoint file and exports .aim model which could be committed and pushed to the Aimhub and/or deployed to different platforms.
Exported .aim model could also be converted to .onnx, .tf and other checkpoints for other frameworks.
Aim CLI Export is based on aim Intermediate Representation that allows for automatic deployment of the model.
Aim Export can also export pre-processing steps similarly to the model and could be included in the model deployment process.Deploy - Aim ModelCommand:aim deployAim Deploy produces a deployable artifact from .aim (model and preprocessing) files. The produced artifacts can run in cloud, on different hardware and as a hybrid.
Deployments are also reflected on Aimhub to track and version the deployed artifacts.Other Commandsaimfork
aimbranchoff
aimpause,continueaimconvert
Package: aimdfragmentation
Automated Fragmentation AIMD CalculationA automated fragmentation method for Ab Initio Molecular Dynamics (AIMD).Author: Jinzhe ZengEmail:jzzeng@stu.ecnu.edu.cnRequirementsOpenBabelnumpyASEGaussianRunnerInstallationUsing pip$pipinstallaimdfragmentationBuild from sourceYou should installGaussian 16andOpenBabelfirst. Then:gitclonehttps://github.com/njzjz/aimdfragmentationcdaimdfragmentation/
pipinstall.ExampleRun a Python programYou can seeexamples/example.pyas an example, and run with:pythonexample.pyRun MD with LAMMPSSeenjzjz/Pyforcerepository and install Pyforce module. Then renameexamples/example.pyasforce.pyand put it where you run LAMMPS. Add a line in the LAMMPS input file:fix 1 all pyforce C H O
Package: aimdvcli
This is forSNS AIMDV (AI based Mood Detection thru Voice)clients, who want to install and test mood AIMDV engine.Table of ContentsInstallationSystem RequirementsInstallataionUpgradeBasic UsageAudio Format ConverterChange LogsInstallationSystem RequirementsOS: Windows, Linux (and maybe Mac)Python 3.4+Installataionpipinstallaimdvcli# orpip3installaimdvcliUpgrade# for updatingpipinstall-UaimdvcliBasic UsageType like this at your console,aimdvcli<command>[options]Audio Format ConverterThis is a audio format converter to standardize the wave format to fit into AIMIDV embedded version in embedded environment, esp., in Android. It can convert all files of the directory you specify, and save into target directory.Target format:sampling rate: 22,050quantization bit: 16 bitchannels: 1 (mono)byte order: little endianusage:aimdvcliaudio[options]<sourcedirectory>[outputdirectory]options:--help:displayhelp-r:convertallaudiofilesandtheirsubdirectoriesrecursivelyarguments:sourcedirectory:directorycontainsaudiofilesoutputdirectory:defaultis<sourcedirectory>/AIMDVexample:# convert all audios in testset directory recursivelyaimdvcliconvert-r./testset./outputChange Logs0.1 (June 28, 2018): project initialized
Package: aimes.skeleton
<a href=”http://dx.doi.org/10.5281/zenodo.13750”><img src=”https://zenodo.org/badge/doi/10.5281/zenodo.13750.svg” alt=”10.5281/zenodo.13750”></a>SkeletonApplication Skeleton is a tool to generate skeleton applications — easy-to-program, easy-to-run applications — that mimic a real applications’ parallel or distributed performance at a task (but not process) level.Application classes that can be represented include: bag of tasks, map reduce, multi-stage workflow, and variations of these with a fixed number of iterations.Applications are described as one or more stages.Stages are described as one more more tasks.  Stages can also be iterative.Tasks can be serial or parallel, and have compute and I/O (read and write) elements.Documentation about Skeletons can be found in the report directoryContributors are welcome!A paper about the first version of Application Skeletons is:
Z. Zhang and D. S. Katz, “Application Skeletons: Encapsulating MTC Application Task Computation and I/O,” Proceedings of 6th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS), (in conjunction with SC13), 2013.http://dx.doi.org/10.1145/2503210.2503222A paper about the current version is:
Z. Zhang and D. S. Katz, “Using Application Skeletons to Improve eScience Infrastructure,” Proceedings of 10th IEEE International Conference on eScience, 2014.http://dx.doi.org/10.1109/eScience.2014.9(paper).http://www.slideshare.net/danielskatz/using-application-skeletons-to-improve-escience-infrastructure(slides).
Package: aimet-ml
aimet-mlPython package of frequently used modules for ML developments in AIMET.Documentation:https://aimet-tech.github.io/aimet-mlGitHub:https://github.com/aimet-tech/aimet-mlPyPI:https://pypi.org/project/aimet-ml/Free software: MITFeaturesTODOCreditsThis package was created withCookiecutterand thewaynerv/cookiecutter-pypackageproject template.
Package: aimet-torch
#==============================================================================#  @@-COPYRIGHT-START-@@##  Copyright (c) 2021-2023, Qualcomm Innovation Center, Inc. All rights reserved.##  SPDX-License-Identifier: BSD-3-Clause##  @@-COPYRIGHT-END-@@#======================================================================================Overview========AI Model Efficiency Toolkit (AIMET) is a library that provides advanced modelquantization and model compression techniques for trained neural networkmodels. It provides features that have been proven to improve run-timeperformance of deep learning neural network models with lower compute andmemory requirements and minimal impact to task accuracy.Features========AIMET supports the following features- Model Quantization- Quantization simulation: Simulates on-target quantized inference.Specifically simulates Qualcomm SnapDragon DSP accelerators.- Quantization-aware training: Fine-tune models to improve on-targetquantized accuracy- Data Free quantization: Post-training technique to improve quantizedaccuracy by equalizing model weights (Cross-Layer Equalization) andcorrecting shifts in layer outputs due to quantization (Bias Correction)- Model Compression- Spatial SVD: Tensor decomposition technique to split a large layerinto two smaller ones- Channel Pruning: Removes redundant input channels of convolutionallayers and modifies the model graph accordingly- Compression-ratio Selection: Automatically selects per-layer compressionratios============Dependencies============See the https://quic.github.io/aimet-pages/releases/latest/install/index.html for details.=============Documentation=============Please refer to the Documentation at https://quic.github.io/aimet-pages/index.htmlfor the user guide and API documentation.=================Using the Package=================Please see https://github.com/quic/aimet#getting-started for package requirementsand usage.
Package: aime-xai
**AIME:**Approximate Inverse Model ExplanationsThe AIME methodology is detailed in the paper available at The AIME methodology is detailed in the paper available athttps://ieeexplore.ieee.org/document/10247033. AIME is proposed to address the challenges faced by existing methods in providing intuitive explanations for black-box models. AIME offers unified global and local feature importance by deriving approximate inverse operators for black-box models. It introduces a representative instance similarity distribution plot, aiding comprehension of the predictive behavior of the model and target dataset. This software only supports the global feature importance of AIME.FeaturesUnified Global and Local Feature Importance: AIME derives approximate inverse operators for black-box models, offering insights into both global and local feature importance.Representative Instance Similarity Distribution Plot: This feature aids in understanding the predictive behavior of the model and the target dataset, illustrating the relationship between different predictions.Effective Across Diverse Data Types: AIME has been tested and proven effective across various data types, including tabular data, handwritten digit images, and text data.LicenseAIME is dual-licensed under the The 2-Clause BSD License and the Commercial License. Apply the The 2-Clause BSD License only for academic or research purposes, and apply Commercial License for commercial and other purposes. You can choose which one to use.Commercial LicenseFor those interested in Commercial License, a licensing fee may be required. Please contact us for more details at:Email:takafumi@eigenbeats.comInstallationpip install aime-xaiCitationIf you use this software for research or other purposes, please cite the following paper@ARTICLE{10247033,
author={Nakanishi, Takafumi},
journal={IEEE Access}, 
  title={Approximate Inverse Model Explanations (AIME): Unveiling Local and Global Insights in Machine Learning Models}, 
  year={2023},
  volume={11},
  number={},
  pages={101020-101044},
 doi={10.1109/ACCESS.2023.3314336}}
Package: aimfast
aimfastAn Astronomical Image Fidelity Assessment ToolMain website:aimfast.rtfd.ioIntroductionImage fidelity is a measure of the accuracy of the reconstructed sky brightness distribution. A related metric, dynamic range, is a measure of the degree to which imaging artifacts around strong sources are suppressed, which in turn implies a higher fidelity of the on-source reconstruction. Moreover, the choice of image reconstruction algorithm also affects the correctness of the on-source brightness distribution.InstallationInstallation fromsource,
working directory where source is checked out$pipinstall.This package is available onPYPI, allowing$pipinstallaimfastLicenseThis project is licensed under the GNU General Public License v3.0 - seelicensefor details.ContributeContributions are always welcome! Please ensure that you adhere to our coding
standardspep8.
Package: aim-git-util
AI Maintainer Git UtilDocumentation:https://docs.ai-maintainer.comA git util for working with the AI Maintainer go git repo in google cloud.
Used in several of our other libraries as well as by us locally.Free software: MIT licenseFeaturesAllows for easy forking, cloning and pushing to our remote repositories, via wrapped sub process commands.History0.1.0 (2023-08-31)First release on PyPI.0.1.1 (2023-09-11)OSS Prep0.1.2 (2023-09-15)Add branch arg to clone and push
Package: aimhii
This package provides software for identifying the genome insertion points random mutants generated by insertional mutagenesis, which have been sequenced as a pool.The package will install two executables: aimhii and extract_chimeras.aimhii will run a full analysis starting from sequence data (a FASTQ file), genome sequence, and insert sequence.  extract_chimeras just runs the last step of this analysis, assuming you already have a BAM file and a concatenated genome-insert sequence.For details see the project websitehttps://github.com/granek/aimhii/Changes in v0.5.5: Added filter_bam.filter_chimeric_reads to pipeline extract only putative junction reads from BWA output. Shifted to hosting on github.Changes in v0.5.4: Added –plot option to aimhii and shifted a few remaining “prints” to loggingChanges in v0.5.3: Shifted debugging prints to logging and added scripts for generating synthetic sequencesChanges in v0.5.2: Lowered pysam version requirement to allow installation using apt-get in Debian v8.Changes in v0.5.1: Fixed problem with missing DESCRIPTION.rst.
Package: aimhub
AimAim is an AI deployment and version control system.
It can handle both small and large projects through their whole life cycle with efficiency and speed.
It is built to seamlessly blend in with existing ML stack and become an integral part of the development lifecycle.Aim CLIAim CLI is a command line tool for building end-to-end AI.
Aim is built to be:
compatible with the existing ecosystem of tools
be familiar
just work
make building AI productiveAim has three main features: tracking of training, export and deploy.Tracking - ML TrainingCommand:aim trainAim train runs training for the given aim repository. Aim train tracks the gradients and updates in the model with given interval and saves them for visualization and analysis.
Aim Train is paired with UI that visualizes the artifacts tracked.
Aim Tracking is used to debug and have a detailed understanding of the process of training.Export - ML ModelCommand:aim exportAim export creates the saved model checkpoint file and exports .aim model which could be committed and pushed to the Aimhub and/or deployed to different platforms.
Exported .aim model could also be converted to .onnx, .tf and other checkpoints for other frameworks.
Aim CLI Export is based on aim Intermediate Representation that allows for automatic deployment of the model.
Aim Export can also export pre-processing steps similarly to the model and could be included in the model deployment process.Deploy - Aim ModelCommand:aim deployAim Deploy produces a deployable artifact from .aim (model and preprocessing) files. The produced artifacts can run in cloud, on different hardware and as a hybrid.
Deployments are also reflected on Aimhub to track and version the deployed artifacts.Other Commandsaimfork
aimbranchoff
aimpause,continueaimconvert
Package: aiml
aiml implements an interpreter for AIML, the Artificial Intelligence
Markup Language developed by Dr. Richard Wallace of the A.L.I.C.E. Foundation.
It can be used to implement a conversational AI program.Forked from:0.9.1https://github.com/paulovn/python-aiml0.8.6https://github.com/cdwfs/pyaimlPyAIML (c) Cort Stratton
Package: aimlbotkernel
This is a Jupyter kernel that deploys a chatbot, implemented using thepython-aimlpackage. The idea was taken from theCalysto chatbotkernel.It has been tested with Jupyter 4.x. The code works with either Python 2.7
or Python 3 (tested with Python 3.4)InstallationThe installation process requires two steps:Install the Python package:pip install aimlbotkernelInstall the kernel into Jupyter:jupyter aimlbotkernel install [--user] [--logdir <dir>]The--useroption will install the kernel in the current user’s personal
config, while the generic command will install it as a global kernel (but
needs write permissions in the system directories).The--logdirspecifies the default place into which the logfile will be
written (unless overriden at runtime by theLOGDIRenvironment variable).
If no directory is specified, the (platform-specific) default temporal
directory will be used.Note that the Jupyter kernel installation also installs some custom CSS; its
purpose is to improve the layout of the kernel results as they are presented
in the notebook (but it also means that the rendered notebook will look
slightly different in a Jupyter deployment in which the kernel has not been
installed, or within an online viewer).To uninstall, perform the inverse operations (in reverse order), to uninstall
the kernel from Jupyter and to remove the Python package:jupyter aimlbotkernel remove
pip uninstall aimlbotkernelOperationOnce installed, anAIML Chatbotkernel will be available in the NotebookNewmenu. Starting one such kernel will create a chatbot. The chatbot is
initially empty but can be loaded with a couple of predefined DBs (use the%helpmagic for initial instructions).Notebook input is of two kinds:Regular text cells are considered human input and are sent to the chatbot,
which produces its corresponding outputCells starting with%contain “magic” commands that affect the
operation of the kernel (load AIML databases, inspecting/modifying bot
state, saving/loading state to/from disk, etc). Use the%helpmagic for
some instructions, and%lsmagicsto show the current list of defined
magics (magics have autocompletion and contextual help).Theexamplesdirectory contains a few notebooks showing some of the
provided functionality. They can also be seen withonline Notebook viewer(note that, as said above, they will look slightly different than in a running
kernel).AIMLAIMLis an XML-based specification to design conversational agents. Its
most famous application is ALICE, a chatbot (the DB for the free version of
ALICE is included in this kernel, as it is included in python-aiml)The chatbot can load an AIML database (which is basically a bunch of XML
files). It can also define AIML rules on the fly, by using the%aimlmagic
in a cell.
Package: aiml-py-common-utils
AIML Python Common UtilitiesVisit Documentation -Click hereThis repository provides a collection of utilities that are frequently used in various AIML applications.Current Version FeaturesThe current version includes the following utilities:1. YAML File ReaderThis utility reads a YAML file and returns a ConfigBox type object. For instance, given a YAML file with the following content:# Scalarsstring:"Hello,World"integer:25floating_point:3.14boolean:truenull_value:null# Sequencessequence:-item1-item2-item3You can access the content of the YAML file as follows:fromaiml_py_common_utilsimportread_yamlcontent=read_yaml(path_to_yaml)print(content.string)# Outputs: "Hello, World"print(content.integer)# Outputs: 252. Directory CreatorThis utility allows you to create multiple directories. For example, to create directories nameddir_one,dir_two, anddir_three, you can use the function as follows:frompathlibimportPathfromaiml_py_common_utilsimportcreate_directorieslist_of_directories_paths=[Path("./dir_one"),Path("./dir_two"),Path("./dir_three")]create_directories(path_to_directories=list_of_directories_paths)3. JSON File WriterThis utility saves a dictionary as a JSON file:frompathlibimportPathfromaiml_py_common_utilsimportsave_dict2jsonexample_dict={"string":"Hello, World","integer":25,"floating_point":3.14,"boolean":True,"null_value":None,}path_to_json=Path("path/to/example.json")save_dict2json(path=path_to_json)4. JSON File ReaderThis utility loads a JSON file. For example, given a JSON file at a certain path containing:{"string":"Hello, World","integer":25,"floating_point":3.14,"boolean":true,"null_value":null,}You can load the content of the JSON file as follows:frompathlibimportPathfromaiml_py_common_utilsimportload_jsonpath=Path("path/to/example.json")content=load_json(path=path_to_json)print(content.string)# Outputs: "Hello, World"print(content.integer)# Outputs: 255. Binary File WriterThis utility saves a snapshot of data as a binary file:frompathlibimportPathfromaiml_py_common_utilsimportsave_binexample_dict={"string":"Hello, World","integer":25,"floating_point":3.14,"boolean":True,"null_value":None,}path_to_bin=Path("path/to/example.bin")save_bin(data=example_dict,path=path_to_bin)6. Binary File ReaderThis utility loads a snapshot of data from a binary file:frompathlibimportPathfromaiml_py_common_utilsimportload_binpath_to_bin=Path("path/to/example.bin")loaded_bin_content=load_bin(path=path_to_bin)7. File Size CalculatorThis utility calculates the size of a file in kilobytes:frompathlibimportPathfromaiml_py_common_utilsimportget_sizefilepath=Path("path/to/example.file")size_in_kb=get_size(path=filepath)