Saravanan666/pypi-org huggingface dataset:
Package: cereb-nest
cereb_nestpip installable NEST extension module, generated by pipnest.Installation instructionsUsing pipWhen installed through pip the module is built for you with CMake outside of the python
package folder, this means that pip cannot track the real installation status. To
uninstall and reinstall seeUninstallation.Install NEST following the instructions provided here (https://www.nest-simulator.org/)Install using pippip install cereb_nest --no-cache-dirLoad the module after importing nest:import nest
nest.Install("cerebmodule")UninstallationTo uninstall this module usepip uninstall cereb_nestand remove the(lib)cerebmodule.sofiles from thelib/nest/directory in your nest installation.Using CMakeExport an Environment Variable containing the installation directory of NEST. E.g.:export NEST_INSTALL_DIR=$HOME/nest-simulator-installClone this GitHub Repository in a directory outside NEST source and build directories. E.g.:cd $HOME
git clone https://github.com/dbbs-lab/cereb-nest/Move to the cereb-nest directory and create a new folder where you will build the extension modulecd cereb-nest
mkdir build
cd buildRun the following CMake command (Tested with CMake 3.2.2)cmake -Dwith-nest=${NEST_INSTALL_DIR}/bin/nest-config ../cereb_nestThe resulting output should be something similar to:[...]
-------------------------------------------------------

You can now build and install 'cerebmodule' using
  make
  make install

The library file libcerebmodule.so will be installed to
  /home/alberto/nest-simulator-install/lib/nest
Help files will be installed to
  /home/alberto/nest-simulator-install/share/doc/nest

The module can be loaded into NEST using
  (cerebmodule) Install        (in SLI)
  nest.Install('cerebmodule')  (in PyNEST)

The module will be installed into a non-default location!
Make sure to set the environment variables:
  export NEST_MODULE_PATH=/home/alberto/nest-simulator-install/lib/nest:$NEST_MODULE_PATH
  export SLI_PATH=/home/alberto/nest-simulator-install/share/nest/sli:$SLI_PATH

 [...]Execute the last two lines, generated by the CMake command.export NEST_MODULE_PATH=/home/alberto/nest-simulator-install/lib/nest:$NEST_MODULE_PATH
export SLI_PATH=/home/alberto/nest-simulator-install/share/nest/sli:$SLI_PATHThis passage should be repeated every time the Terminal is closed. It is possible to add the two lines in your .bashrc to avoid doing this every time.Make and install the modulemake
make installEvery time you need the module, you can install it in this way:import nest
nest.Install("cerebmodule")You can now use all the nodes and synapses contained in this NEST Module.
Package: cerebralcortex-restclient
# CerebralCortex-RESTClient
Cerebral Cortex is the big data cloud companion of mCerebrum designed to support population-scale data analysis, visualization, model development, and intervention design for mobile sensor data.You can find more information about MD2K software on our [software website](https://md2k.org/software) or the MD2K organization on our [MD2K website](https://md2k.org/).CerebralCortex-RESTClient is part of our [CerebralCortex cloud platform](https://github.com/MD2Korg/CerebralCortex). CerebralCortex Kernel is mainly responsible interact with CerebralCortex-APIServer to submit data without mCerebrum.## Documentation[Source code documentation](https://cerebralcortex-restclient.readthedocs.io/en/latest/)## Installation
CerebralCortex-RESTClient requires minimum [Python3.6](https://www.python.org/downloads/release/python-360/). To install CerebralCortex-RESTClient as an API:`pip3 installcerebralcortex-restclient`Note: please use appropriate pip (e.g., pip, pip3, pip3.6 etc.) installed on your machine## Contributing
Please read our [Contributing Guidelines](https://md2k.org/contributing/contributing-guidelines.html) for details on the process for submitting pull requests to us.We use the [Python PEP 8 Style Guide](https://www.python.org/dev/peps/pep-0008/).Our [Code of Conduct](https://md2k.org/contributing/code-of-conduct.html) is the [Contributor Covenant](https://www.contributor-covenant.org/).Bug reports can be submitted through [JIRA](https://md2korg.atlassian.net/secure/Dashboard.jspa).Our discussion forum can be found [here](https://discuss.md2k.org/).## VersioningWe use [Semantic Versioning](https://semver.org/) for versioning the software which is based on the following guidelines.MAJOR.MINOR.PATCH (example: 3.0.12)MAJOR version when incompatible API changes are made,MINOR version when functionality is added in a backwards-compatible manner, andPATCH version when backwards-compatible bug fixes are introduced.For the versions available, see [this repository’s tags](https://github.com/MD2Korg/CerebralCortex/tags).## ContributorsLink to the [list of contributors](https://github.com/MD2Korg/CerebralCortex-Kernel/graphs/contributors) who participated in this project.## LicenseThis project is licensed under the BSD 2-Clause - see the [license](https://md2k.org/software-under-the-hood/software-uth-license) file for details.## Acknowledgments[National Institutes of Health](https://www.nih.gov/) - [Big Data to Knowledge Initiative](https://datascience.nih.gov/bd2k)
* Grants: R01MD010362, 1UG1DA04030901, 1U54EB020404, 1R01CA190329, 1R01DE02524, R00MD010468, 3UH2DA041713, 10555SC[National Science Foundation](https://www.nsf.gov/)
* Grants: 1640813, 1722646[Intelligence Advanced Research Projects Activity](https://www.iarpa.gov/)
* Contract: 2017-17042800006
Package: cerebras-appliance
cerebras_appliancecerebras_appliance package is a set of framework-agnostic utilities required by Cerebras's Wafer-Scale cluster software stack which offers a framework to train and evaluate a neural network with near-perfect linear scaling across millions of cores without the inconvenience of distributed computing.Website:https://cerebras.net/Documentation:https://docs.cerebras.net/Support:support@cerebras.netDiscord:https://discord.gg/ZqvYS2e2rYInstallationpipinstallcerebras_appliance
Package: cerebras-pytorch
cerebras_pytorchThe cerebras_pytorch package is a component of Cerebras's Wafer-Scale cluster software stack, offering a framework for training and evaluating neural networks with near-perfect linear scaling across millions of cores without the inconvenience of distributed computing. Additionally, it provides general ML helpers for running on CPU/GPU, empoweringCerebras' modelzoo.Website:https://cerebras.net/Documentation:https://docs.cerebras.net/Support:support@cerebras.netDiscord:https://discord.gg/ZqvYS2e2rYInstallationpipinstallcerebras_pytorch
Package: cerebrate-sdk
Cerebrate SDKInstallwith poetrypoetryaddcerebrate-sdkor with pippipinstallcerebrate-sdkExamplesFake email detectorfromcerebrate_sdkimportCerebratec=Cerebrate('YOUR_API_KEY')task="Detect if email is fake or real"examples=["qwertyuiooiu@ihdj.com: fake""support@cerebrate.ai: real",]result=c.predict(task,examples,"lajotig138@5k2u.com: ")print(result[0])# fakeWith optionsfromcerebrate_sdkimportCerebrate,Optionsc=Cerebrate('YOUR_API_KEY')options=Options(stop=['Q:'],temperature=0.7,max_tokens=100,top_p=1,presence_penalty=0,frequency_penalty=0,best_of=1)task="Detect if email is fake or real"examples=["qwertyuiooiu@ihdj.com: fake""support@cerebrate.ai: real",]result=c.predict(task,examples,"lajotig138@5k2u.com: ",options=options)print(result[0])# fakeRaw usagefromcerebrate_sdkimportCerebratec=Cerebrate("YOUR_API_KEY")result=c.raw("Suggest the next item for user's cart.""Cart: bacon, eggs, tomatoes""Suggested item: ")print(result[0])# sausageReusing presetsfromcerebrate_sdkimportCerebratec=Cerebrate('YOUR_API_KEY')# key-value pairs# variable_name: value# variables are defined in UI during preset creationvariables={"stock":"tennis balls, tennis rockets, soccer balls, soccer uniform, basketballs, tennis uniform, tents, backpacks, socks","cart":"tennis ball, socks"}result=c.preset_predict(preset_id="YOUR_PRESET_ID",variables=variables)print(result[0])
Package: cerebro
Ce projet a pour but de créer une application en javascript pouvant localiser
des lycéens et les informations qui leurs sont relatives sur une carte
géographique.ObjectifsPouvoir avoir une représentation géographique d’étudiants.Pouvoir ajouter des informations de plusieurs types (Laboratoires,
Univerisités, Associations,…)Etre rapide, facile d’utilisationInstallationfab installis the command to run on a debian-like system to launch
the application
Package: cerebro-image-processing
Image Processing PackageThis is a lightweight Python image processing package with utility functions that are frequently use in computer vision.InstallationThis library can be installed via pip command:$pipinstallcerebro-image-processingOr install from source:$gitclonehttps://github.com/crblab/imgproc.git&&cdimgproc
$pythonsetup.pyinstallMaintainersHieu Pham(author)LicenseMIT LicenseCopyright (c) 2021,Cerebro Labs. All rights reserved.
Package: cerebro-ml
CerebroDeep Learning library extended from Google Tensorflow which focus on enabling flexible prototyping features.Installation$pipinstallcerebro-mlIt is recommended to use with tensorflow-gpu because of heavy computation of deep neuron networks.MaintainersHieu Tr. Pham(author)LicenseGNU General Public License v3.0Copyright (c) 2021,Hieu Tr. Pham. All rights reserved.
Package: cerebro-refactoring
Cerebro Refactor PackageRefactoring is a systematic process of improving code without creating new functionality that can transform a mess into
clean code and simple design.Refactoring is a core package ofCerebrobecause we build everything with engineering mindset which means clean code and good
software architect is our top concern.We focus on the package in Python because it is our first programming language but we are working hard to make
cross-platform support as soon as possible.InstallationThe package can be installed by pip command:pip install cerebro-refactoringMaintainersHieu Pham(author)LicenseMIT License.Copyright © 2021Hieu Pham. All rights reserved.This project was initialized as a part ofCerebroopen source project which helps
to build  intelligence system that run everywhere for everyone.
Package: cerebro-toolkit
Cerebro Toolkit PackageThis package provide a systematic process of building and improving software codebase without creating new functionality
that can transform a mess into clean code and simple design architect.This is a core package ofCerebrobecause we build everything with engineering mindset which means clean code and good
software architect is our top concern.We focus on the package in Python because it is our first programming language but we are working hard to make
cross-platform support as soon as possible.InstallationThe package can be installed by pip command:pip install cerebro-toolkitMaintainersHieu Pham(author)LicenseMIT License.Copyright © 2021Hieu Pham. All rights reserved.This project was initialized as a part ofCerebroopen source project which helps
to build  intelligence system that run everywhere for everyone.
Package: cereja-ai
Cereja AI - Library of Pre-Trained Artificial Intelligence ModelsDescriptionThis repository is an open source library that contains pre-trained models and artificial intelligence architectures using TensorFlow. The purpose of this library is to facilitate the construction of AI models for the developer community, making the process more accessible and democratic.By contributing to this library, you are helping to democratize access to AI, enabling more people to build efficient and accurate AI models. Furthermore, your contribution can be used in a variety of different projects, from voice recognition applications to product recommendation systems.How to ContributeIf you are knowledgeable about AI and TensorFlow, contributing to this library can be a way to help the developer community speed up the process of building AI models. Even if you don't have AI experience, you can still contribute by providing support with documentation or building the library's infrastructure.To contribute to this library, follow these steps:Fork this repositoryCreate a branch with your contribution (git checkout -b my-contribution)Make the necessary changesSubmit a pull requestHow to useTo use this library in your project, just clone this repository on your machine and import the desired models or architectures. Check the documentation for more information on how to use each model or architecture.DocumentationThe documentation for this library is available at [LINK].
Package: ceresdb-client
CeresDB Python ClientIntroductionThe python client forCeresDB.CeresDBis a high-performance, distributed, schema-less, cloud native time-series database that can handle both time-series and analytics workloads.Support featuresQueryWriteAnexampleis provided to show how to access CeresDB.Installationpipinstallceresdb-clientContributingAny contribution is welcome!Read ourContributing Guideand make your first contribution!LicenseUnderApache License 2.0.Community and supportJoin the user group onSlackJoin the user group on WeChatWeChat QR codeJoin the user group on DingTalk: 44602802
Package: ceresfit
CEREsFitThe goal of CEREsFit (Correlated Errors Regression Estimate Fit)
is to provide a python package
that allows to calculate linear regressions on data sets with correlated uncertainties.
The calculations follow the methodology published byMahon (1996).
Typos and errors that were made in that work have been corrected.
A method to allow calculating a linear regression through a fixed point,
avoiding previously made errors,
is also provided.InstallationThe package can be installed frompypivia:pip install ceresfitUsageBelow is an example on how to use the package.>>>importnumpyasnp>>>fromceresfitimportLinReg>>># some data>>>xdata=np.array([1,2,3.1,4.9])>>>ydata=np.array([1.1,1.9,3,5.5])>>># some uncertainty and correlation>>>xunc=0.05*xdata>>>yunc=0.073*ydata>>>rho=np.zeros_like(xdata)+0.5>>># do regression>>>my_reg=LinReg(xdata,xunc,ydata,yunc,rho)>>># print out the parameters and their uncertainties>>>my_reg.slope(0.9983613298400896,0.06844666435449052)>>>my_reg.intercept(0.05545398718611372,0.11812746374874884)>>>my_reg.mswd2.5105964767071143Detailed example on how to use the class for fitting and plotting the results
can be foundin these Jupyter notebooks.Development & ContributingIf you would like to contribute,
clone the GitHub repo and then install the package locally from within the folder via:pip install -e .[dev]If you also want to install the full test environment,
run:pip install -e .[dev,test]Code auto formatting is implemented usingpre-commithooks.
Full testing of the package can be done withnox.Please feel free to raise issues on GitHub
and open pull requests if you have a feature to be added.
Tests and adequate docstrings should be provided along with your new code.
Package: ceres-nav
CERESCelestialEstimation forResearch,Exploration, andScienceCERESis an API for aiding in the ismulation of spacecraft dynamics, and the devleopment of new navigation and mapping techniques.Releases areregisted on PyPI, while development is occuring on theceres GitHub page.  Any bugs should be reported to theIssue Tracker.  Documentation is located atdocs.ceresnavigation.orgNOTE: Currently, CERES is only supported on Linux.  Native Windows support is coming soon, though WSL2 (available on both Windows 10 and 11) is already supported.Installpip install ceres-navOnce installed, ceres can be imported using:import ceresCall for ContributionsTo contribute to this project, it is highly recommended that you create a virtual environment with either mamba or conda.Install mamba or conda:To install mamba (RECOMENDED):mambaforgeTo install conda:anacondaCreate the virtual environment:source the base environmentcreateceres_envenvironment with ceres dependencies:conda create -n ceres_env python=3 numpyInstall tools required for development:pip install sphinx pytest pytest-cov sphinx-rtd-theme setuptools wheel twineActivate the virtual environment withconda activate ceres_envInstalling CERES into the environment:Clone:git clone https://github.com/ceres-navigation/ceresInstall:cd ceres; pip install -e .Running tests:pytest --cov=ceres tests/If you are new to contributing to open source,this
guidehelps explain why, what,
and how to successfully get involved.ContactAll questions should be directed to Chris Gnam:crgnam@buffalo.edu
Package: cerespp
Ceres-plusplusThis package was written as an extension to the CERES reduction pipeline
(https://github.com/rabrahm/ceres) in the sense that it takes spectra reduced
by it and extracts some activity indicators (CCF FWHM, BIS, CONTRAST) and
calculates others (S index, Ha, HeI, NaID1D2)It's been tested to work on FEROS and FIDEOS spectra. Feel free to use it with
other instruments and let me know if it works :)InstallationYou can try runningpip install ceresppIf that fails you can clone the repository with$gitclonehttps://github.com/jvines/Ceres-plusplus
$cdCeres-plusplus
$pythonsetup.pyinstallDependenciesCeres-plusplus depends on the following packages:numpy (https://numpy.org/)scipy (https://www.scipy.org/)matplotlib (for plotting, but it's optional for the core functionality) (https://matplotlib.org/)astropy (https://www.astropy.org/)PyAstronomy (https://pyastronomy.readthedocs.io/en/latest/)tqdm (https://tqdm.github.io/)termcolor (https://pypi.org/project/termcolor/)UsageUsage is simple, start by importingcerespp, optionally you can useglobto
fetch the files. After importing (and having ready your files) theget_activitiesfunction, grab a coffee, and wait for your indicators!.Below there's an example scriptimportceresppimportglobfiles=glob.glob('path/to/fits/files/*.fits')act,header=cerespp.get_activities(files,'output/path/filename.dat')Herefilesis a list with the fits files to process, the outputactandheaderare the activities and the header of the file (i.e. the names of the
columns).ceresppautomatically saves the output in a file in the desired
location, but if you need the output on the session you're working in
(in a jupyter notebook, for example), that's whatactandheaderare for ;)Plottingceresppoffers some plotting tools to visually check things! The most
important ones are plots showing the activity lines and their surroundings.
Creating these is easy:importceresppimportglob# These are the available lines. You can choose which ones to plot herelines=['CaHK','Ha','HeI','NaID1D2']files=glob.glob('path/to/fits/files/*.fits')# This function creates the plots from a fits file directlycerespp.line_plot_from_file(files[0],lines,'output/path/','starname')There's a notebook exemplifying the usage in the examples folder!How it worksceresppfirst calculates a radial velocity to correct the spectrum to
rest-frame, after this has been done it extracts the available data from the
fits headers (CCF FWHM, BIS, CONTRAST) and finally it merges the echelle orders
and computes the activity indicators!The radial velocity is computed using the standard cross-correlation function
method, and thus you can specify which mask to use with themaskkeyword inget_activities. Available masks areG2, K0, K5,andM2.
Package: ceres-raytracer
CERES Ray Tracer (CRT)This project was developed as part ofCERESand aims to provide scientifically useful path tracing capabilities for rendering images, simulating LiDAR, modeling radiation pressure, and flux computations for thermal and solar panel analysis.WebsiteAPI DocumentationSource codeBug ReportsAvailable EnvironmentsEnvironmentBuild Python BindingInstall from PyPIUbuntu 22.04Ubuntu 20.04Ubuntu 18.04Installation:It is recommended to install directly from the Python Package Index (PyPI):pip install ceres-raytracerBuilding from Source:git clone https://github.com/ceres-navigation/ceres-raytracer.git
pip install .sphinxis used to build documentation for the python API.  To installsphinx, simply use:sudo apt-get install python3-sphinx
pip install sphinx-autodoc-typehints sphinx-toolbox sphinx-licenseinfo sphinx-rtd-themeOncesphinxis installed, you can build the documentation on Linux systems by running:cd doc
make htmlDemos:After installingceres-raytracer, simply clone theceres-raytracer-demos:git clone https://github.com/ceres-navigation/ceres-raytracer-demos.gitand run any of the included.pyexample files:cornel_box.pyThis demo script demonstrates multi-bounce path tracing using the traditional cornell box scene.  It additionally shows how to render depth, instances and normal passes:comet67p.pyThis demo script demonstrates how to use CRT's SPICE support, and rendering in a body fixed frame (allowing for the BVH to be cached and therefore overall faster render times):Tasks:Python InterfacePython bindings with pybind11Binary distributions (cross compiled for Windows/MacOS on Linux)Python defined materials (compatible only with wavefront ray tracing)Path tracing implementationsAdaptive samplingWavefront ray tracingBidirectional path tracingPrimary Sample Space Metropolis Light TransportPhysicsPolarization of light raysWavelength of light raysSpectral power density of raysPhysically based radiance tracking for pathsMomentum of pathsMaterialsPBR Texture supportHapke function supportMicrofacet supportMajor architecture changesTBB for parallelizationAdd Embree single precision supportAdd OptiX (GPU) single precision supportAdd Vulkan (GPU) single precision supportAttributionsmadmann91's Modern C++ BVH Construction and Traversal LibraryThis project utilizes a BVH construction and traversal library built bymadmann91.  While we have made some modifications to their implementation, their work forms most of the basis of the bounding volume hierarchy used in this project.  Their originally source code can be found in thebvh repositoryNational Science Foundation Graduate Research FellowshipThis material is based upon work supported by theNational Science Foundation Graduate Research Fellowshipunder Grant No. 2020305048.  NSF GRFP gave Chris Gnam the flexibility and resources required to complete his research in spacecraft navigation, and this project is meant to serve as an open source implementation of his dissertation.ContactAll questions, comments, and concerns should be directed to Chris Gnam:crgnam@buffalo.edu
Package: cerf
cerfcerfis an open-source geospatial Python package for evaluating and analyzing future electricity technology capacity expansion feasibility.Purposecerfwas created to:Evaluate the feasibility of a future scenario-driven electricity technology capacity expansion plan as generated by a parent model,Site power plants in the least cost configuration when considering regional economics an on-the-ground barriers to siting,Assist planners and modelers of alternate future realizations of the electricity system to gain an understanding of how siting costs and service area congestion may respond under certain stressors.InstallcerfNOTE:cerfis not officially supported for Ubuntu 18 users due to a system dependency (GLIBC_2.29) required by thewhiteboxpackage whichcerfuses to conduct spatial analysis. Ubuntu 18 natively includesGLIBC_2.27.  It may be possible for Ubuntu 18 users to upgrade toGLIBC_2.29but this should be done with careful consideration.  Instead, we officially supportcerfuse for Ubuntu users for versions 20.04.2 LTS and greater.pipinstallcerfCheck out a quickstart tutorial to runcerfRuncerfusing the quicktart tutorial:cerfQuickstarterGetting startedNew tocerf?  Get familiar with whatcerfis all about in ourGetting Starteddocs!User guideOur user guide provides in-depth information on the key concepts ofcerfwith useful background information and explanation.  See ourUser GuideContributing tocerfWhether you find a typo in the documentation, find a bug, or want to develop functionality that you think will makecerfmore robust, you are welcome to contribute! See ourContribution GuidelinesAPI referenceThe reference guide contains a detailed description of thecerfAPI.  The reference describes how the methods work and which parameters can be used.  It assumes that you have an understanding of the key concepts.  SeeAPI Reference