Saravanan666/pypi-org huggingface dataset:
Package: analitico
Analitico SDKThis package contains plugins and classes used to access analitico.ai cloud services and machine learning models. The package can be installed in Jupyter notebooks, Colaboratory notebooks or other Python environments. To access assets stored in Analitico you will need an API token.InstallationTo install in Python:pip install analiticoTo install on Jupyter, Colaboratory, etc:!pip install analiticoUsageimportanalitico# authorize calls with developer tokensdk=analitico.authorize_sdk(token="tok_xxx")# retrieve a dataset object from analiticodataset=sdk.get_dataset("ds_xxx")# download a data file from storage into a Pandas dataframedf=dataset.download(df="customers.csv")
Package: analog
Analog is a weblog analysis utility that provides these metrics:Number for requests.Response request method (HTTP verb) distribution.Response status code distribution.Requests per path.Response time statistics (mean, median).Response upstream time statistics (mean, median).Response body size in bytes statistics (mean, median).Per path request method (HTTP verb) distribution.Per path response status code distribution.Per path response time statistics (mean, median).Per path response upstream time statistics (mean, median).Per path response body size in bytes statistics (mean, median).Documentation is onanalog.readthedocs.org,
code and issues are ongithub.com/fabianbuechler/analogand the package can be installed
from PyPI atpypi.python.org/pypi/analog.Changelog1.0.0 - 2015-02-26Provide yaml config file for Travis-CI.Extend tox environments to cover 2.7, 3.2, 3.3, 3.4, pypy and pypy3.Convert repository to git and move to github.Set version only in setup.py, use via pkg_resources.get_distribution.1.0.0b1 - 2014-04-06Going beta with Python 3.4 support and good test coverage.0.3.4 - 2014-04-01Testanalog.analyzerimplementation.Testanalog.utilsimplementation.0.3.3 - 2014-03-10Testanalog.renderersimplementation.Fix bug in default plaintext renderer.0.3.2 - 2014-03-02Testanalog.report.Reportimplementation and fix some bugs.0.3.1 - 2014-02-09Rename--max_ageoption to--max-agefor consistency.0.3.0 - 2014-02-09Ignore __init__.py at PEP257 checks since __all__ is not properly supported.Fix custom log format definitions. Format selection in CLI via subcommands.Add pypy to tox environments.0.2.0 - 2014-01-30Remove dependency on configparser package for Python 2.x.Allow specifying allanalogarguments in a file for convenience.0.1.7 - 2014-01-27Giving up on VERSIONS file. Does not work with different distributions.0.1.6 - 2014-01-27Include CHANGELOG in documentation.Move VERSION file to analog module to make sure it can be installed.0.1.5 - 2014-01-27Replace numpy with backport of statistics for mean and median calculation.0.1.4 - 2014-01-27Move fallback for verbs, status_codes and paths configuration toanalyzer.
Also use the fallbacks inanalog.analyzer.Analyzer.__init__andanalog.analyzer.analyze.0.1.3 - 2014-01-27Fix API-docs building on readthedocs.0.1.1 - 2014-01-26Add numpy torequirements.txtsince installation viasetup.py installdoes not work.Strip VERSION when reading it in setup.py.0.1.0 - 2014-01-26Start documentation: quickstart and CLI usage plus API documentation.Add renderers for CSV and TSV output. Use –output [csv|tsv].
Unified codebase for all tabular renderers.Add renderer for tabular output. Use –output [grid|table].Also analyze HTTP verbs distribution for overall report.Remove timezone aware datetime handling for the moment.Introduce Report.add method to not expose Report externals to Analyzer.Install pytz on Python <= 3.2 for UTC object. Else use datetime.timezone.Add tox environment for py2.7 and py3.3 testing.Initial implementation of log analyzer and report object.Initial package structure, docs, requirements, test scripts.
Package: analogainas
analogai-nasAnalogAINasis a modular and flexible framework to facilitate implementation of Analog-aware Neural Architecture Search. It offers high-level classes to define: the search space, the accuracy evaluator, and the search strategy. It leveragesthe aihwkit frameworkto apply hardware-aware training with analog non-idealities and noise included.AnalogAINASobtained architectures are more robust during inference on Analog Hardware. We also include two evaluators trained to rank the architectures according to their analog training accuracy.SetupWhile installing the repository, creating a new conda environment is recomended.git clone https://github.com/IBM/analog-nas/
pip install -r requirements.txt 
pip setup.py installUsageTo get started, check outnas_search_demo.pyto make sure that the installation went well.This python script describes how to use the package.
Package: analogcaption
Analog Image Caption PackageThis is a Python package that helps give caption to your images, it is built on the Flickr_8k Dataset and trained with the Xception pre-trained model.How to try this package ?This package is very easy to use and has 2  fuctions which are:extract_features()this function simply take the image and extract the features from it.generate_desc()this function now generates a description for the Image.Demo in a flask app@app.route("/generateCaption", methods=["POST"])
def generateCaption():
    image = request.files['image']
    img = image.read()

    # convert string of image data to uint8
    nparr = np.fromstring(img, np.uint8)
    # decode image
    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    img = cv2.resize(img, (224, 224))
   

    photo = extract_features(img)
    # generate description
    caption = generate_desc(model, tokenizer, photo, max_length)

   
    return render_template("results.html", image=image, caption=caption)Demo in a python script#import image and extract feature
photo = extract_features(img_path)
img = Image.open(img_path)
description = generate_desc(model, tokenizer, photo, max_length)
print("\n\n")
print(description)
plt.imshow(img)Hope this helps you understand the package and use it in a project. We are currently working on more cool features and will do an update when we're done.
Package: analogistics
# analogistics## Analytical Tools for Logistics System Design and Operations Management
This packages is developed for analysts and researchers in the field of Supply Chain Managements. Several methods are developed to support decisions regarding the design and control of three types of Supply Chain systems:
* Distribution Network
* Storage Systems (i.e. warehouses)
* Production SystemsThe subfolderexamplescontains useful implementation of the methods using sample data.## SetupTo install the package, use Python >= 3.6, and run`sh pip install analogistics `Using window operating system, it is possible you are required to have Microsoft Visual C++ 14.0 or greater. Please download it fromhttps://visualstudio.microsoft.com/visual-cpp-build-tools/## License
MIT
Package: analogvnn
AnalogVNNDocumentation:https://analogvnn.readthedocs.io/Installation:InstallPyTorchInstall AnalogVNN usingpip# Current stable release for CPU and GPUpipinstallanalogvnn# For additional optional featurespipinstallanalogvnn[full]Usage:Sample code with AnalogVNN:sample_code.pySample code without
AnalogVNN:sample_code_non_analog.pySample code with AnalogVNN and
Logs:sample_code_with_logs.pyJupyter
Notebook:AnalogVNN_Demo.ipynbAbstractAnalogVNNis a simulation framework built on PyTorch which can simulate the effects of
optoelectronic noise, limited precision, and signal normalization present in photonic
neural network accelerators. We use this framework to train and optimize linear and
convolutional neural networks with up to 9 layers and ~1.7 million parameters, while
gaining insights into how normalization, activation function, reduced precision, and
noise influence accuracy in analog photonic neural networks. By following the same layer
structure design present in PyTorch, the AnalogVNN framework allows users to convert most
digital neural network models to their analog counterparts with just a few lines of code,
taking full advantage of the open-source optimization, deep learning, and GPU acceleration
libraries available through PyTorch.AnalogVNN Paper:https://arxiv.org/abs/2210.10048Citing AnalogVNNWe would appreciate if you cite the following paper in your publications for which you used AnalogVNN:@article{shah2022analogvnn,title={AnalogVNN: A fully modular framework for modeling and optimizing photonic neural networks},author={Shah, Vivswan and Youngblood, Nathan},journal={arXiv preprint arXiv:2210.10048},year={2022}}Or in textual form:Vivswan Shah, and Nathan Youngblood. "AnalogVNN: A fully modular framework for modeling 
and optimizing photonic neural networks." arXiv preprint arXiv:2210.10048 (2022).
Package: analyseGoogleMyActivity
analyseGoogleMyActivityGenerates Reports of Sleep Time, Sleep Routine and App Usage using Data from Google MyActivity : myactivity.google.comSleep Data and App Usage Data are generated on the basis of Following Assumptions :Sleep Data :Bed Time : Time at which User stops using phone and goes to bed.
   Wake Up Time : After bed time, the First Time at which User starts using phone.Time Spent on a App : Difference of Time between the opening time of the app and the next app.First Download Google Activity Data File by following these steps :1. Login to Google account. Go to : https://takeout.google.com/

2. Under  "Select data to include", Click on "Deselect all" .

3. Scroll Down and Select "My Activity" . Click on "Multiple formats".  In  "Activity records, Choose 'JSON'  & then 'ok'.

4. Scroll Down. Click on "Next Step"  and then on "Create Export".

5. Wait for the Google Data Download mail to arrive in your Gmail. Download the Zip file.Installation :pip install analyseGoogleMyActivityRequirements : "numpy", "pandas", "matplotlib"Usage :By Default, Looks for the Latest Takeout Zip in the current working directoryfrom analyseGoogleMyActivity import androidReport
reports = androidReport()Directly Pass the Takeout Zip to the parameter file ( Pass its Path also if the zip file is not in the current working directory)reports = androidReport(file='takeout-2020XXXXTXXXXXXZ-001.zip')Parameters :file : str, optional
    Pass MyActivity JSON file or Takeout zip file with path. The default is 'MyActivity.json'.
apps : int or list , optional
    No. of Top Apps or List of Apps to find usage for. The default is 12.
timezone : str
    Pass the timezone region code. The default is 'in' for Indian Standard Time (IST).
excludeapps : List
    List of app names to Exclude from App Usage calculation. The default is ['com.miui.home' ].
idealsleeptime : int
    Ideal Sleep Time. The default is 8.
inlineimg : 0 or 1, 
    To include image in the Report itself or not. The default is 1.
showmarkerday: 0 or 1,
    To show day on each marker in sleep routine graphs. The default is 0.
output : 0 or 1, optional
    If 1 , Returns Dictionary with Results in Pandas DataFrames, otherwise returns Reports names. The default is 1.
yeartabs : 0 or 1, optional
    To Show Year & its Data in Tabs, The default is 1.
verbose : 0 or 1, optional
    Shows Additional Progess during Report Generation. The default is 0.

Returns
Dictionary if Parameter output = 1   OR
Tuple having Generated Report names if output = 0
-------
Dictionary with Following Keys having Values 
'AppUsage': Time at which an App is opened, Pandas DataFrame
'AppDailyUsage': Day wise data of App opened, Pandas DataFrame
'SleepData': Bed Time & WakeUp Time with Sleep Duration , Pandas DataFrame
'SleepYearlyTable': Yearly Stats of Sleep Time & Sleep Routine
'SleepMonthlyTable': Monthly Stats of Sleep Time & Sleep Routine
'AppYearlyTable': Yearly App Usage Stats 
'AppMonthlyTable': Monthly App Usage Stats
 __________________________________________________________
Package: analyser
Analyser v1.0The analyser Python package is designed to fetch and analyze signal data. It interfaces with a web API to retrieve signals and their values, offering functionalities to calculate statistical measures like mean and standard deviation. It supports filtering by groups, ensuring tailored analyses. The package also includes efficient data handling with batch retrieval, making it suitable for large datasets.Environment setupCreate virtual environment with Python version > 3.8.5python3 -m venv .venvActivate virtual environmentsource .venv/bin/activateInstall requirementspip install -r requirements.txtRun all Testspytest -v -sUseage(Refer to unseage.md)Installation(Refer to installation.md)
Package: analyse-stock
analyse - a powerful staock data analysis and manipulation library for Pythonanalyseis a Python package providing fast, flexible, and expressive data
structures designed to analyse stock both in hongkong,us,china market,it has
the broader goal of becomingthe most powerful and flexible open source data
analysis / manipulation tool in analyzing stock. It is already well on
its way toward this goal.Main FeaturesHere are just a few of the things that analyse does well:-get raw data from tdx
-calculate pe,pe_ttm
-calculate growth
-calculate current vix
Package: analysis_project_root
analysis-project-rootReset project root for analysis projects.For analysis projects sometimes it is useful to have a  scripts that can import from lots of relative locations in a project;root/
  sub1/
    a.py
    sub2/
      b.py
  sub3/
    c.pyThis is a very simple package that solves the problem for me and hides away unpleasant code to add tosys.pathand change the working directory.To install the package;pip install analysis-project-root
Package: analysis-tools
Analysis tools for machine learning projects1. Usage$pipinstallanalysis-tools2. Tutorialexamples/titanic/eda.ipynb를 참고fromanalysis_toolsimporteda,metricsdata=pd.DataFrame(..)target='survived'num_features=['age','sibsp','parch','fare']cat_features=data.columns.drop(num_features)data[num_features]=data[num_features].astype('float32')data[cat_features]=data[cat_features].astype('string')eda.plot_missing_value(data)eda.plot_features(data)eda.plot_features_target(data,target)eda.plot_corr(data.corr())metrics.get_feature_importance(data,target)
Package: analysts-task-pkg-macca2707
data-analyst-taskSimple data analyst program developed for Halfbrick studios as a mini assignment
Instructions are contained within CLI program but i will add them here aswellCommands:
json -- Creates a json file when provided an appropriate CSV file
data -- Makes a data summary of a provided CSV file
sql -- Creates an SQL file when provided an appropriate CSV file, also requires a table name
*quit -- exits the program*Note all commands except this one require a file name, file is contained within this package
Package: analytical
Analytical is a Python library for sending pageviews and events to analytics platforms
like Google Analytics except from Python rather than JavaScript so it can be done server side.
This has a number of advantages such as working regardless of whether clients block analytics scripts,
privacy sensitive information can be anonymized or removed before sending,
and it allows sending data only known by the server.Feature supportConvenient utilities for anonymizing sensitive information like IP addressesPluggable provider backends for different analytics platforms (currently just Google)Supports Python 2.7, Python 3.5+, and PyPy.Exampleimportanalyticalprovider=analytical.Provider('googleanalytics','UA-XXXXXXX-1')provider.pageview({'dl':'https://example.com','dt':'My Page Title','ua':'user-agent',# User agent'uip: '12.34.56.78',            # User IP address})ResourcesGitHub:https://github.com/rtfd/analyticalDocumentation:https://analytical.readthedocs.ioIRC: #readthedocs on freenode
Package: analyticlab
analyticlab（分析实验室）analyticlab是一个实验数据计算、分析和计算过程展示的Python库，可应用于大学物理实验、分析化学等实验类学科以及大创、工艺流程等科技类竞赛的数据处理。该库包含以下5个部分：数值运算（num、numitem模块）：按照有效数字运算规则的数值运算。数理统计（numitem、twoitems模块）：包括偏差、误差、置信区间、协方差、相关系数、单个样本的显著性检验、两个样本的显著性检验。离群值处理（outlier模块）：包括Nair检验、Grubbs检验、Dixon检验和偏度-峰度检验。符号表达式合成（lsym、lsymitem模块）：根据符号和关系式，得到LaTeX格式的计算式。测量及不确定度计算（measure包）：根据实验数据或其他不确定度报告中的数据、测量仪器/方法和测量公式，得出测量结果，包含测量值和不确定度。模块定义库中定义了9个类：Num：分析数值运算类，位于num模块。NumItem：分析数组类，位于numitem模块。LSym：LaTeX符号生成类，位于lsym模块。LSymItem：LaTeX符号组类，位于lsymitem模块。Const：常数类，位于const模块。LaTeX：公式集类，位于latexoutput模块。measure.Ins：测量仪器类，位于measure.ins模块。measure.BaseMeasure：基本测量类，位于measure.BaseMeasure模块。measure.Measure：测量类，位于measure.Measure模块。7个函数模块：amath：对数值、符号、测量的求根、对数、三角函数运算。twoitems：两组数据的数理统计。outlier：离群值处理。latexoutput：数学公式、表格、LaTeX符号和不确定度等的输出。measure.std：计算标准偏差。measure.ACategory：计算A类不确定度。measure.BCategory：计算B类不确定度。类的主要功能和绝大多数函数支持process（显示计算过程），通过在调用类方法或函数时，附加参数process=True实现。具体哪些类方法和函数支持process，可以查阅使用教程，或者通过help函数查询其说明文档。注意计算过程是以LaTeX格式输出的，因此计算过程展示功能只有在Jupyter Notebook环境下才能使用。如果只需要得到计算结果而不需要展示其过程，那么一般的Python3开发环境即可。如何安装或更新1.通过pip安装：pip install analyticlab2.通过pip更新版本：pip install analyticlab --upgrade如果更新失败，可以尝试先卸载旧版本，再安装新版本：pip uninstall analyticlabpip install analyticlab3.在pypi上下载analyticlab源代码并安装：打开网址https://pypi.python.org/pypi/analyticlab通过download下载tar.gz文件，解压到本地，通过cd指令切换到解压的文件夹内通过python setup.py install实现安装运行环境analyticlab只能在Python 3.x环境下运行，不支持Python 2.x环境。要求系统已安装numpy、scipy、sympy、quantities库。可以在绝大多数Python平台下运行，但计算过程只有在Jupyter Notebook环境下才能显示出来。参照标准文件GBT 8170-2008 数值修约规则与极限数值的表示和判定GBT 4883-2008 数据的统计处理和解释正态样本离群值的判断和处理JJF1059.1-2012 测量不确定度评定与表示CNAS-GL06 化学领域不确定度指南
Package: analyticord
# analyticord[![Supported Python versions](https://img.shields.io/pypi/pyversions/analyticord.svg)](https://pypi.org/project/analyticord)[![License](https://img.shields.io/pypi/l/analyticord.svg)](https://choosealicense.com/licenses)[![Documentation Status](https://readthedocs.org/projects/discordanalytics-python/badge/?version=latest)](http://discordanalytics-python.readthedocs.io/en/latest/?badge=latest)[![Build Status](https://travis-ci.org/nitros12/module-python.svg?branch=master)](https://travis-ci.org/nitros12/module-python)-----**Table of Contents*** [Installation](#installation)* [Getting Started](#getting-started)* [License](#license)## Installationanalyticord is distributed on [PyPI](https://pypi.org) as a universalwheel and is available on Linux/macOS and Windows and supportsPython 3.5+ and PyPy.```bash$ pip install analyticord```## Getting Started```pythonimport asynciofrom analyticord import AnalytiCordloop = asyncio.get_event_loop()# The most basic usage, with a single bot tokenanalytics = AnalytiCord("token")# start up the analytics# this could be done inside the on_ready event, etc of a d.py botloop.run_until_complete(analytics.start())# hook the on_message event of a d.py bot# this will send message count events to analyticord for youanalytics.messages.hook_bot(bot)```## Licenseanalyticord is distributed under the terms of the[MIT License](https://choosealicense.com/licenses/mit).
Package: analyticsdf
Analytic generation of datasets with specified statistical characteristics.Introductionanalytics-dataset provides a set of functionality to enable the specification and generation of a wide range of datasets with specified statistical characteristics. Specification to include the predictor matrix and the response vector. Check theanalyticsdf documentationfor more details.
Examples include:High correlation and multi-collinearity among predictor variablesInteraction effects between variablesSkewed distributions of predictor and response variablesNonlinear relationships between predictor and response variablesResearch existing automate dataset functionalitySklearnMake DatasetsfunctionalityMIT Synthetic Data Vault projectMIT Data to AI Labdatacebo2016 IEEE conference paper, The Synthetic Data Vault.Public PackageThis repo has published beta packages on bothPypiandAnaconda
Package: analytics-mesh
analytics-meshInterfaces and facades that facilitate a common approach to analytics tasksGetting StartedPlease install the requirements:pipinstall-rrequirementsIf you are going to be making modifications to theipynbnotebooks, then be sure to install the pre-commit hook (see below):pre-commitinstallTestsTests are currently split intounitandintegrationtests. As this package integrates with storage systems, the integration tests are typically running against things like Google Cloud Platform.Tests may be run in the root folder of the repo with:coverage run -m unittest discover tests && coverage reportIf you want to run just the unit tests (and ignore coverage) then,python-munittestdiscovertests/unitSimilarly, for the integration tests.Pre-Commit Hooks and Notebook WorkflowIn this repo we are using the pythonprecommitpackage (included in requirements.txt file). In order to leverage it in your development workflow, you need to run the following commands (assuming you have already installed your requirements).pre-commitinstallWe follow the convention that a version controlledipynbfile is converted to a markdown (md) file to form anipynb-mdpair that are both version controlled. This allows us to code review the markdown files whilst keeping the original ipynb file output intact for easy perusal on the repository manager (Gitlab in our case).Packaging the CodeThe contents of themeshpackage are packaged in the build pipeline and submitted to pypi when merged to the master branch. The contents of thedemosfolder andtestsis omitted from the package. See the.gitlab-ci.ymlfile for the pypi instructions we use, as well as the tests and linting that are performed.The consequences of this is that you will convertipynbnotebooks tomd.ContributionsContributions are most welcome. Please submit patches or new features for code review by any of the main contributors:Jacques du ToitCarl du PlessisJaco GerickePlease be sure to run the tests prior to submission.
Package: analytics-python
This library was renamed due to a naming conflict with an official python library.
Please use the new listing athttps://pypi.org/project/segment-analytics-python/Segment is the simplest way to integrate analytics into your application.
One API allows you to turn on any other analytics service. No more learning
new APIs, repeated code, and wasted development time.This is the official python client that wraps the Segment REST API (https://segment.com).Documentation and more details athttps://github.com/segmentio/analytics-python
Package: analytics-zoo
Analytics Zoois an open sourceBig Data AIplatform, and includes the following
features for scaling end-to-end AI to distributed Big Data:Orca:
seamlessly scale out TensorFlow and PyTorch for Big Data (using Spark & Ray)RayOnSpark:
run Ray programs directly on Big Data clustersBigDL
Extensions:
high-level Spark ML pipeline and Keras-like APIs for BigDLChronos:
scalable time series analysis using AutoMLPPML:
privacy preserving big data analysis and machine learning (experimental)For more information, you mayread the docs.
Package: analytics-zoo-serving
Analytics Zoo: A unified Data Analytics and AI platform for distributed TensorFlow,
Keras, PyTorch, Apache Spark/Flink and Ray.You may want to develop your AI solutions using Analytics Zoo if:You want to easily prototype the entire end-to-end pipeline that applies AI models
(e.g., TensorFlow, Keras, PyTorch, BigDL, OpenVINO, etc.) to production big data.You want to transparently scale your AI applications from a laptop to large clusters with "zero"
code changes.You want to deploy your AI pipelines to existing YARN or K8S clustersWITHOUTany modifications
to the clusters.You want to automate the process of applying machine learning (such as feature engineering,
hyperparameter tuning, model selection and distributed inference).Find instructions to install analytics-zoo via pip, please visit our documentation page:https://analytics-zoo.github.io/master/#PythonUserGuide/install/For source code and more information, please visit our GitHub page:https://github.com/intel-analytics/analytics-zoo