Saravanan666/pypi-org huggingface dataset:
Package: corunner
Description:This is a framework to execute programs in multiple nodes concurrently.It consists of two modules: controller and executor. The controller controls the whole process, it first dispatches the executorand user-defined program files to all the nodes specified by the user. Then it starts the executor remotely and waits for all the executorsto end. The executor run the program specified by the user and reports to the controller peroidly. When it finishes, it notifys thecontroller and then exits. After all the executors have reported their status or a speicified timeout is reached, the controller exits.The user can then send another request to pull the results.To monitor every executor's status, the executor reports to the controller peroidly. When one executor fails to report in apredefined timeout, the controller will mark it as dead. And if the controller receives the report later, it changes the status to be aliveagain. The controller never waits for a dead node.Features:Heart beat monitorComplete parallel executionEasy tool for cloning filesAgile and simple:unlike program in other languages, this is simple and the executor is only one single file.Minimal resource exhaustion:the execution is distributed in all nodes, and the controller exausted little resource(mainly for monitor heartbeat)Environment:This is just tested on python2.7 and python2.6 in linux environment.And it based on ssh and scp for net communication, before your exection, you should authenticated your controller with all executors.Commands:corunner-run:	to run program in multiple nodes concurrently.corunner-cp:	to dispatch files from or to multiple nodes concurrently.Example:1. Execute the myscript in all machines from 192.168.101 to 192.168.200 conrrently with heartbeat switched off:Run in controller: corunner-run -n 192.168.100.101..200 -f myscript -r "/tmp/corunner" -i 1 python /tmp/corunner/myscript2. Collect all output files from above to the controller and put in seperated directory:Run in controller: corunner-cp -n 192.168.100.101..200 -i -s /tmp/corunner/ouput -d /temp/corunner/all --divideOther:If you have any problem or suggestion, welcome to contact me, my email is zwsun<sun33170161@gmail.com>. I'm pleasant if it can helpyou and improve your efficiency when working with many machines.
Package: corut
corutThis package is intended as a subsystem for applications developed/to be developed and facilitated
many operations. It has been tried to comply with the Design Pattern/Object Orient principles and
PEP standards. It will be constantly updated with additions and corrections.
I will share all the source code and how to use it on my blog website which I will publish soon.Details will be shared onhttp://www.ibrahimcörüt.com/projects/pypi-corut_fastapi_toolsThe general structure of the system to be added was designed.The works are not yet completed.The code to be transferred is not yet complete.Test modules will be added after all is completed.Source code and documentation will be shared on my personal website, which is being prepared.MIT LicenseCopyright (c) 2021 ibrahim CÖRÜTPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: corut-fastapi-tools
corut_fastapi_toolsIt is aimed to get rid of repetitive lines of code by integrating tools
that use multiple APIs for FastApi and facilitate repetitive operations.Details will be shared athttps://www.ibrahimcorut.com/tr/projects/pypi-corut_fastapi_tools.MIT LicenseCopyright (c) 2021 ibrahim CÖRÜTPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: corut-installer
corut_installerIt is aimed to facilitate the necessary installations for python with the tkinter interface on
Linux and Windows systems.For Linux, tkinter should be installed with "sudo apt-get install python3-tk".Details will be shared onhttps://www.ibrahimcorut.com/en/projects/pypi-corut-installer/MIT LicenseCopyright (c) 2021 ibrahim CÖRÜTPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: corut-qt
corut_qtAdvanced facilitating functions and Classes for QT.Details will be shared onhttp://www.ibrahimcörüt.com/MIT LicenseCopyright (c) 2022 ibrahim CÖRÜTPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: corvid
corvidcorvid is an opinionated simple static site generator.It processes Markdown files, static assets and directories producing a 1:1
heiarchy of compiled content.It includes a reloadable local development server, supports custom markdown frontmatter and Jinja templates.Installation$ pip install corvidRunningBy default corvid expects to be run in a directory with aninputdirectory. With no parameters, corvid will process all files in this directory into theoutputdirectory, and exit.$ corvid --help
Usage: corvid [OPTIONS]

Options:
  -l, --listen          Enable live reloading
  -b, --bind TEXT       Host to bind to
  -p, --port INTEGER    Port to run on
  -i, --input TEXT      Input directory
  -o, --output TEXT     Output directory
  -t, --templates TEXT  Templates directory
  --help                Show this message and exit.ExampleGiven the following directory layout:├── input
│   └── index.md
└── templates
    └── default.htmlWith the following file contents:input/index.md---
title: This is the Index
---

Welcometemplates/default.html<html><head><title>{{ title }}</title><body>{{ content }}</body></body>Runningcorvidwill produce the additionaloutputdirectory:.
├── input
│   └── index.md
├── output
│   └── index.html
└── templates
    └── default.htmlAnd the contents of the file will be:output/index.html<html><head><title>This is the Index</title><head><body><p>Welcome</p></body></body>Using templatesCustom templates can be set by specifying the path to the template inside thetemplatesdirectory as thetemplatefrontmatter.See/examplefor a full example.
Package: corvus-ml
Corvus - Search ShareableAI ModelsCorvus is a CLI tool that wraps the Searcher fromJackdaw. Designed for interactive and programmatic access to models saved via the ShareableAI toolkit.UsageSet API Keycorvussetapi_keyFollow the interactive prompt - this helps avoid having the API Key visible within the terminal history.List ModelsLocal Modelscorvuslist--localRemote ModelsRequires an API Key to be setcorvuslist--remoteSearchSearches for all models in the Jackdaw repo on the main branch.corvuslist--repojackdaw--branchmainSearch for all models on the main branch of any repo;corvuslist--branchmainSearch for all models on feature branches;corvuslist--repo'feat/%'
Package: corx
corx is a Python package designed to simplify the implementation of the Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) patterns. It provides essential components and utilities for developing systems that adhere to these patterns.InstallationInstall corx using pip:pip install corxArchitectureThe core components of the corx architecture include:Command: Represents a request or intention to change the state of the system.CommandHandler: Handles specific types of commands, executing the logic associated with each command.Event: Represents an occurrence or state change in the system, often triggered by the successful handling of a command.EventListener: Reacts to specific events by executing custom logic.ProcessManager: Orchestrates complex workflows by reacting to events and potentially issuing new commands.Dispatcher: Responsible for handling and routing commands or events to the appropriate handlers.
Package: cos202finalbycharawi
cos202finalbycharawi is a Python library for dealing with points and
vectors.InstallationUse the package managerpipto
install cos202finalbycharawi.pipinstallcos202finalbycharawiUsagefromcos202finalbycharawiimportVectorv1=Vector(1,2)v2=Vector(3,4)print(v1)print(v2)v1.euclideandistance(v2)print(v1.lenght())print(v1.unit())print(v1.dotproduct(v2))#print(v1.crossproduct(v2))print(v1+v2)print(v1-v2)print(v1*v2)print(v1/v2)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.AuthorCharawi DetphumiLicenseMIT
Package: cos-alerter
COS AlerterCOS Alerter is intended to be used together with alertmanager and prometheus:Liveness of Alertmanager through an always-firing alert rule ("Watchdog")Liveness of COS Alerter itself from a metric endpoint it exposes and prometheus scrapesConfiguring AlertmanagerIn order to integrate with COS Alerter you need to a heartbeat rule to Prometheus and Add a route to the Alertmanager configIf you are using the Canonical Observability Stack, the alert rule is already created for you. If not, you can use a rule similar to the following:-alert:Watchdogannotations:summary:A continuously firing alert that ensures Alertmanager is working correctly.expr:vector(1)labels:severity:noneAdd the following to your alertmanager config to create the route:receivers:...-name:cos-alerterwebhook_configs:-url:http://<cos-alerter-address>:8080/alive?clientid=<clientid>&key=<clientkey>route:...routes:...-matchers:-alertname = Watchdogreceiver:cos-alertergroup_wait:0sgroup_interval:1mrepeat_interval:1mNote thatgroup_waitshould be set to0sso the alert starts firing right away.Configuring COS AlerterCopy the filecos_alerter/config-defaults.yamlto/etc/cos-alerter.yaml(If running without docker) or./cos-alerter(if running with docker). Edit the file with the appropriate values for your environment.Running COS AlerterDockerThe easiest way to run COS Alerter is to use docker.docker run -p 8080:8080 --mount type=bind,source="$(pwd)"/cos-alerter.yaml,target=/etc/cos-alerter.yaml,readonly -it ghcr.io/canonical/cos-alerter:latestPythonYou can also run cos-alerter by installing the python package.pip install cos-alerter
cos-alerterDevelopment BuildsSeeCONTRIBUTING.mdfor running development builds.
Package: cosamp
CoSaMPCoSaMP algorithm in Python.InstallationHow to install:pip install cosamporpip3 install cosampDependencies:numpy.Usage example@Input:  Phi - Sampling matrix
          u   - Noisy sample vector
          s   - Sparsity vector

@Return: A s-sparse approximation "a" of the target signalUsing CoSaMP algorithm to reconstruct a high-frequency signal from sparse measurements:importnumpyasnpimportscipy.linalgimportscipy.signalimportmatplotlib.pyplotaspltfromcosampimportcosampn=100# number of measurementst=np.linspace(0.0,1.0,num=n)x=np.sin(91*2*np.pi*t)+np.sin(412*2*np.pi*t)# original signal (to be reconstructed)# randomly sample signalp=103# random sampling (Note that this is one eighth of the Shannon–Nyquist rate!)aquis=np.round((n-1)*np.random.rand(p)).astype(int)y=x[aquis]# our compressed measurement from the random sampling# Here {y} = [C]{x} = [C][Phi]{s}, where Phi is the inverse discrete cosine transformPhi=scipy.fft.dct(np.eye(n),axis=0,norm='ortho')CPhi=Phi[aquis,:]# l1 minimization (through linear programming)s=cosamp.cosamp(CPhi,y,10)# obtain the sparse vector through CoSaMP algorithmxrec=scipy.fft.idct(s,axis=0,norm='ortho')# Reconstructed signalfigw,figh=7.0,5.0# figure width and heightplt.figure(figsize=(figw,figh))plt.plot(t,s)plt.title('Sparse vector $s$')plt.show()# Visualize the compressed-sensing reconstruction signalfigw,figh=7.0,5.0# figure width and heightplt.figure(figsize=(figw,figh))plt.plot(t,x,'b',label='Original signal')plt.plot(t,xrec,'r',label='Reconstructed signal')plt.xlim(0.4,0.5)legend=plt.legend(loc='upper center',shadow=True,fontsize='x-large')# Put a nicer background color on the legend.legend.get_frame().set_facecolor('C0')plt.show()Other infoMATLAB versions of the algorithm are readly available (seeherefor instance). This Python method is based on the MATLAB routine written by Prof. Bob L. Sturm.The original Needell and Tropp's 2008 paper can be foundhere.
Package: cosapp-lab
CosApp Lab - Toolbox for managing and deploying CoSApp powered dashboards.IntroductionThe primary goal ofCoSApp Labis to help users transform existing CoSApp
modules into interactive dashboards, with almost no additional development or
configuration.ExamplesUsing CosApp Lab in JupyterLabCoSApp Lab provides a JupyterLab extension namedSysExplorerfor creating interactive dashboards. This extension allows users to dynamically customize the layout and fill their dashboard with multiple predefined widgets, such as charts, controllers (sliders,etc.), 3D visualization panels...Using CosApp Lab standalone modeDashboards defined withSysExplorerin JupyterLab can also be exported into libraries and served by CoSApp Lab as web applications.DocumentationA more detailedCoSApp Labdocumentation is available at:https://cosapp-lab.readthedocs.io/InstallationStable releaseThe easiest way to obtaincosapp_labis to install the conda package:condainstall-cconda-forgecosapp_labcosapp_labis also available on PyPI. However, sincepythonocc-coreis not, users can installcosapp_labwithpip, but the 3D viewer widget will not work.pipinstallcosapp-labJupyterLabis not a direct dependency ofcosapp_lab, but users need to have JupyterLab (>3.0) in order to create CoSApp dashboard in notebooks.DevelopmentSetup development environment# create a new conda environmentcondacreate-ncosapplab-cconda-forgepythonjupyterlabnodejscondaactivatecosapplab# download cosapp_lab from gitlabgitclone--recursivehttps://gitlab.com/cosapp/cosapp_lab.git# install JS dependencies, build and install JupyterLab extension in development modecdcosapp_labjlpminstalljlpmbuild:alljlpminstall:extension# install cosapp_lab in editable modepython-mpipinstall-e.Testing# Test python codepython-mpytest# Test typescript codejlpmtestBuild documentscddocs
sphinx-build-bhtml-d_build/doctrees._build
Package: cosasi
cosasi (COntagion Simulation And Source Identification)cosasiis a Python package for graph diffusion source inference, allowing users to:perform and evaluatesource inference using standard techniques from literature,contributeinnovative localization methods to a growing core library, andbenchmarknew techniques against a battery of comparable schemes.Table of ContentsTable of ContentsInstallationGetting StartedCode SnippetTestingContributionsSupportContactLicenseInstallationInstallation via PyPIpipinstallcosasiInstallation via GitHubClone the repo fromhere(this repo).Install requirements:pipinstall-rrequirements.txtGetting StartedOncecosasiis installed, feel free to review ourtutorialintroducing major functionality. Official documentation, including a detailedAPI reference, is available onRead the Docs.Code SnippetAbove:Carbonimage of example code snippet; copy-and-paste-able version below.importnetworkxasnximportcosasiG=nx.fast_gnp_random_graph(100,0.25)contagion=cosasi.StaticNetworkContagion(G=G,model="si",infection_rate=0.01,number_infected=3,)contagion.forward(100)I=contagion.get_infected_subgraph(step=15)result=cosasi.source_inference.multiple_source.netsleuth(G=G,I=I)result.evaluate(contagion.get_source())TestingExtensive unit testing is employed throughout, with ~97% code coverage.If you've cloned our repo fromGitHub, you can cd into the root directory and runpytestviacoverage:coveragerun-mpytestTo read the .coverage file:coveragereportContributionsWeâ€™d love your help! If youâ€™d like to make an addition or improvement, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.Our contributor guide ishere, and we itemize a few areas of development weâ€™d like to prioritize for the future ofcosasihere. If you find something wrong, please submit a bug report to theissue tracker. For other questions or comments, feel free tocontact usdirectly.Supportcosasiwas developed inForge, the technology accelerator of theLogistics Management Institute.ContactQuestions? Reach out:Lucas (email)LicenseMIT
Package: coscine
Coscine Python SDKCoscine, short forCollaborativeScientificIntegrationEnvironment, is a platform for Research Data Management.
The Coscine Python SDK is an open source package that provides
a high-level interface to the Coscine REST API. It enables you
to automate research workflows and offers the usual functionality
available in the Coscine web interface to python programmers.ShowcaseUploading a file to a resource:importcoscinefromdatetimeimportdatetimetoken="My Coscine API token"client=coscine.ApiClient(token)project=client.project("My Project")resource=project.resource("My Resource")form=resource.metadata_form()form["Author"]="Dr. Akula"form["Created"]=datetime.now()form["DAP"]=3.12resource.upload("file.csv","C:/research/file.csv",form)DocumentationInstallation instructions and an in-depth guide on using the Python SDK can
be found in the onlinedocumentation. The source code itself has been
heavily annotated with numpy-style DOCstrings. You can generate a local
copy of the documentation using Sphinx:py-mpipinstall-Urequirements.txtcddocssetSPHINXBUILD=py-msphinx.cmd.build
py-msphinx.ext.apidoc-o.../src/coscine
makehtmlContactTo report bugs, request features or resolve questions open an issue inside
of the current git repository. Contributions and any help on improving this
package are appreciated. To contribute source code you may fork
the repository and open a merge request or simply submit a short
and relevant snippet or fix inside of an issue.LicenseThis project is Open Source Software and licensed under the terms of
theMIT License.
Package: coscli
coscli - 一个简单的腾讯云 COS 命令行工具, 当前仅支持 Python2安装使用pip$ pip install coscli升级$ pip install --upgrade coscli使用配置文件默认为~/.coscli.cfg,/etc/coscli.cfg, 可以使用环境变量COSCLI_CONFIG来更改位置,
或者直接使用--config参数配置路径配置文件采用 INI 格式[cos]app_id=fooaccess_key_id=baraccess_key_secret=fooregion=bar使用命令$ coscli --help其他GithubChanges Log
Package: coscmd
介绍腾讯云COS命令行工具, 目前可以支持Python2.6与Python2.7以及Python3.x。安装指南使用pip安装pip install -U coscmd手动安装:python setup.py install使用方法使用coscmd，参照https://cloud.tencent.com/document/product/436/10976
Package: coscmos
coscmoscosCMOS( "correctsCMOS", pronounced as cosmos ) is a Python package designed to cancel the fixed-pattern noise of sCMOS cameras.The noise is estimated through a one-time calibration. The package contains methods to estimate the fixed-pattern noise component from the calibration data and remove it from the images.InstallationYou can installcoscmosviapip:pip install coscmosTo install latest development version:pip install git+https://github.com/LemonJust/coscmos.gitContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"coscmos" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: cosco-shipping-autotest-framework
pythonProject介绍{以下是 Gitee 平台说明，您可以替换此简介Gitee 是 OSCHINA 推出的基于 Git 的代码托管平台（同时支持 SVN）。专为开发者提供稳定、高效、安全的云端软件开发协作平台
无论是个人、团队、或是企业，都能够用 Gitee 实现代码托管、项目管理、协作开发。企业项目请看https://gitee.com/enterprises}软件架构软件架构说明安装教程xxxxxxxxxxxx使用说明xxxxxxxxxxxx参与贡献JasonZheng特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.mdGitee 官方博客blog.gitee.com你可以https://gitee.com/explore这个地址来了解 Gitee 上的优秀开源项目GVP全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目Gitee 官方提供的使用手册https://gitee.com/helpGitee 封面人物是一档用来展示 Gitee 会员风采的栏目https://gitee.com/gitee-stars/
Package: cosdem
COSDEMCosdem is a python package for comparing statistical differences of measurements in two-dimension data especially equivalent medical devicesIntroductionCosdemaims to compare two-dimension data with many statistical tests and graphs in one method.As an open-source tasking library,cosdem, provides one report on the following tests and graphs:TestsDescriptive StatisticsHomogeneity Tests of VariancesNormality Test: Shapiro WilkStatistical Difference TestsCorrelation TestsRegression Result###GraphsViolin PlotRegression Line in Scatter PlotBlant Altman PlotInstallationPrerequisites:python3 >= 5andpip3.pip install cosdemorgit clone git@github.com:yigitsener/cosdem.gitUsagefromcosdemimportCosdemimportpandasaspdimportrandoma=[]b=[]foriinrange(50):a.append(random.uniform(10,15))b.append(random.uniform(10,15))df=pd.DataFrame({"Feature A":a,"Feature B":b})tests=Cosdem(df)FunctionsText resultsprint(tests.report())Save all tables in Excel fileprint(tests.save_all_tables())Save all graphs/figures in Excel fileprint(tests.save_all_figures())