Saravanan666/pypi-org huggingface dataset:
Package: gemmi
GEMMI can help if you work with:macromolecular models (from mmCIF, PDB and mmJSON files),refinement restraints (CIF files),crystallographic reflections (from MTZ and SF-mmCIF files),electron density maps (MRC/CCP4 files),crystallographic symmetries,or if you just read and write CIF/STAR files (where C=Crystallographic).GEMMI is a C++11 library accompanied by:command-linetools,Python bindings (supporting CPython and PyPy),Fortran 2003+ interface (in progress),WebAssembly ports (seehereandhere),and little data vizprojects.Documentation:http://gemmi.readthedocs.io/en/latest/GEMMI is an open-source project ofCCP4andGlobal Phasing Ltd,
two major providers of software for macromolecular crystallography.Citing:JOSS paper.License: MPLv2, or (at your option) LGPLv3.
© 2017-2023 Global Phasing Ltd.
Package: gemmi-program
gemmi executable in a wheelHere we provide the command-line programgemmiinwheels.It is, in PyPI, distributed separately from the Python extension modulegemmi,
because, unlike the module, it does not depend on Python version.notes for myself -- how to make wheels after gemmi releaseupdateGIT_TAGin CMakeLists.txt andversionin pyproject.toml(optionally) update version of cibuildwheel in .github/workflows/wheels.yml
and scikit-build-core in pyproject.tomltest locally withpip wheel .make source distribution of this repo:python -m build --sdistgit push changes to build wheels inGitHub Actionsdownload the wheels, check them, upload sdist and wheels to PyPI:twine upload dist/gemmi_program-$VERSION.tar.gz
twine upload wheels/gemmi_program-$VERSION-*.whl
Package: gemmr
gemmr - Generative Modeling of Multivariate Relationshipsgemmrcalculates required sample sizes for Canonical Correlation Analysis (CCA) and
Partial Least Squares (PLS). In addition, it can generate synthetic datasets for use
with CCA and PLS, and provides functionality to run and examine CCA and PLS analyses.
It also provides a Python wrapper forPMA, a sparse CCA implementation.Hardware requirementsGEMMR runs on standard hardware. To thoroughly sweep through parameters of the generative model a high-performance-computing (HPC) environment is recommended.Dependenciesnumpyscipypandasxarraynetcdf4scikit-learnstatsmodelsjoblibtqdmSome functions have additional dependencies that need to be installed separately if they are used:holoviewsrpy2The repository also contains anenvironment.ymlfile specifying a conda-environment with specific versions of all dependencies. We have tested the code with this environment. To instantiate the environment run>>> conda env create -f environment.ymlInstallationThe easiest way to installgemmris withpip:pip install gemmrAlternatively, to install and use the most current code:git clone https://github.com/murraylab/gemmr.git
cd gemmr
python setup.py installInstallation ofgemmritself (without potentially required dependencies) should take only a few seconds.DocumentationExtensive documentation can be foundhere.The documentation containsDemonstration of thegemmr's functionality, including exptected outputs (all of which should execute quickly)Juyter notebooks detailing generation of the figures for the accompanying manuscriptsAPI referenceTo generate the documentation from source, installgemmras described above and make sure you also have the following dependencies installed:ipythonmatplotlibsphinxnbsphinxsphinx_rtd_theme
and run (in thedocsubfolder):make htmland opendoc/_build/html/index.html.CitationIf you're usinggemmrin a publication, please citeHelmer et al. (2020)
Package: gemnine
Simple Wrapper For The Gemini AI APIThis module is designed for asynchronous usage and provides a simple interface to the Google Gemini API.It also provides a command-line interface for interactive usage.NOTICEImages are not tested and may not work as expected.Installationpipinstall-UgemnineUsageCommand LineConfigurationgemninerequires a model and an API key to be set.You can use config file, command parameters or interactive input to set these values.By default,gemninewill look for files namedconfig.jsonandsession.jsonin the user config and cache directory.gemnine-c/path/to/config.json# Use a config filegemnine-m"models/gemini-pro"# Set model, input API key interactivelygemnine-l# list available modelsParameters-c,--config: Path to a JSON file-m,--model: Model name-s,--session: Session history file-V,--version: Show versionPrecedence: Interactive input > Command parameters > Config fileInteractive ModeThis mode mimics a chat interface. You can type your message and get a response.Commands are started with a/and arecase-sensitive./exit: Exit the program/save <path>: Save the session history to a file/load: Load a session history from a file/rollback <step>: Rollback to the previous state/clear: Clear the session history/role: Switch role/model: Switch model/help: Show helpTo embed an image, insert#image(<url>)in your message.
Use double#to escape embedding.awaitbot.send("""What are these?#image(https://example.com/image.png)#image(file:///path/to/image.png)#image(base64://<base64-encoded-image>)""")All URLs will be downloaded and embedded as base64-encoded images.APIimportasyncioimportgemninebot=gemnine.Bot(model="models/gemini-pro",api_key="your-api-key")asyncdefmain():response=awaitbot.send("Hello, how are you?")print(response)asyncforrinbot.stream("I'm fine, thank you."):print(r,end='')# `Bot` instance it self doesn't track the session history# To keep track of the session history, use `Session` instancesess=bot.new_session()print(awaitsess.send("Hello, how are you?"))asyncforrinsess.stream("What was my last question?"):print(r,end='')asyncio.run(main())Save and load session historySessionandBotusepydanticmodels under the hood. You can save and load the session history usingmodel_dump_jsonandmodel_validate_jsonor pass arguments tonew_sessionmethods.importjsonfrompathlibimportPathsess_path=Path("session.json")sess=bot.new_session()data=sess.model_dump_json()sess_path.write_text(data)sess=bot.new_session(**json.loads(sess_path.read_text()))
Package: gempy-engine
gempy_engineGPU Install in LinuxCheck the instalation guide of tensorflow. It is very picky on the enviromentInstall nvidia drivers before trying to install cudathe cuda installer has also the drivers but apparently is more difficult to set upDownload the .run file instead the deb filecuDNN is always required an it is a painthis has to be installed with dev because it is IMPORTANT to install:Runtime librarydeveloper librarypykeopsIt needs cmake in the enviromentMake sure this is in bashrc:Adding environment variables:export PATH="/usr/local/cuda-11.2/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH"
for WSL:
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATHAdding the path to bash is not enough for Pycharm. It has to be added to the enviroment variables. In ubuntu is on the file/etc/environment. Edit it with the following commandsudo -H gedit /etc/environment.You can check that the variables are properly set in Pycharm looking in the Run Config
Package: gempyp
GemPyPAn out of the box Python Automation FrameworkWebsite•Docs•RepoTable of contentsOverviewInstallationRequirementsFeaturesUsageDocumentationsContributorsMoreCreditsOverviewGemPyP is a testing and reporting framework that allows automatic execution of testcases along with generation of the report, that enables effortless analysis and monitoring of the set.The setting and configurations, including the testcases are passed as a config file to the framework, then GemPyP handles the environments, execution, reporting, storing data and miscellaneous.It allows-Email integrationParallel execution of testcasesCustomization of test Suite reportsPlatform independence (Supports Linux, Windows and MacOS)InstallationFor installation make sure require version of python and pip is installed in the system.$pipinstallgempypRequirementsPython version 3.6 or aboveRequired Librariescertifi==2022.6.15cffi==1.15.0charset-normalizer==2.0.12coloredlogs==15.0.1cryptography==37.0.2humanfriendly==10.0idna==3.3lxml==4.9.0ntlm-auth==1.5.0numpy==1.22.4pandas==1.4.2pycparser==2.21pyreadline3==3.4.1python-dateutil==2.8.2pytz==2022.1requests==2.28.0requests-ntlm==1.1.0six==1.16.0urllib3==1.26.9These libraries will get downloaded automatically with the package.FeaturesEasy to use.Cross Platform.Multiple modes of execution.Static Report generated after execution.Saves TimeUsageCan be used in API Automation and testing python testcases.DocumentationsThe official documentation is hosted on Read the Docs:https://gempyp.readthedocs.io/en/latest/ContributorsWe welcome and recognize all contributions. You can see a list of current contributors in thecontributors tab.MoreFor more information visithttps://gempyp.gemecosystem.com/andhttps://gemecosystem.com/CreditsTODO
Package: gemsembler
GEMsemblerGEMsembler tool for assembling and comparing several types of Genome-Scale Metabolic
Models.THIS IS A BETA VERSION! BUGS CAN BE EXPECTEDInstallationInstall with the following command:pip install gemsemblerNote:you also have to install BLAST in advance.UsageInput models have to be COBRApy readable files. And models need to be
particular type. Currently models made by CarveMe (carveme), ModelSEED
(modelseed), gapseq (gapseq) and models downloaded from AGORA VMH database
(agora) are supported. Custom type is coming soon. Genomes, from which the
models are built will allow to convert and assemble genes as well.
First, we import gemsembler and get the path to data files:from gemsembler import GatheredModels, lp_example, get_model_of_interestFirst stage is the creation of gathered models, a class, that performs
conversion and contains results of all stages:gathered = GatheredModels()
for model in lp_example:
    gathered.add_model(**model)
gathered.run()Second stage is actual assembly of supermodel from the in formation in gathered
models. User has to provide output folder. And for gene conversion user hast
provide either final genes in fasta. Then all gene will be converted to ids in
these files. Or if user provides NCBI assembly ID for his organism of interest,
corresponding genome will be downloaded automatically and all genes will be
converted to the locus tags of the organism.supermodel_lp = gathered.assemble_supermodel("./gemsembler_output/", assembly_id = "GCF_000203855.3")After supermodel is assembled different comparison methods can be runsupermodel_lp.at_least_in(2)And results of comparison can be extracted as typical COBRApy modelscore2 = get_model_of_interest(supermodel_lp, "core2", "./gemsembler_output/LP_core2_output_model.xml")
Package: gemseo
GEMSEO stands for
“Generic Engine for Multi-disciplinary Scenarios, Exploration and Optimization”,
it features:Automatic creation and execution of MDO processes based on MDO formulationsIntegration of numerical techniques in MDO processes: multidisciplinary
coupling, optimization, design of experiments, visualization, surrogate
models, machine learning, uncertainty, …Various ways of interfacing software and models: Python, Matlab, Excel,
scilab, executables, …A Python library, standalone and easy to embed in simulation platformsBuilt on NumPy, SciPy and matplotlibOpen-source LGPL-3.0 licenseDocumentationThe full documentation of GEMSEO, including installation and tutorials,
can be found athttps://gemseo.readthedocs.io.Bugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo/-/issuesto submit bugs or questions.LicenseThe GEMSEO source code is distributed under the GNU LGPL v3.0 license.
A copy of it can be found in the LICENSE.txt file.
The GNU LGPL v3.0 license is an exception to the GNU GPL v3.0 license.
A copy of the GNU GPL v3.0 license can be found in the LICENSES folder.The GEMSEO examples are distributed under the BSD 0-Clause, a permissive
license that allows to copy paste the code of examples without preserving the
copyright mentions.The GEMSEO documentation is distributed under the CC BY-SA 4.0 license.The GEMSEO product depends on other software which have various licenses.
The list of dependencies with their licenses is given in the CREDITS.rst file.About usVisit the pageAbout usto find out more about GEMSEO (context, history, references, etc.)
and its contributors.
Package: gemseo-benchmark
A GEMSEO-based package to benchmark optimization algorithm.DocumentationSeehttps://gemseo.readthedocs.io/en/stable/plugins.html.Bugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo-benchmark/-/issuesto submit bugs or questions.LicenseThegemseo-benchmarksource code is distributed under the GNU LGPL v3.0 license.
A copy of it can be found in the LICENSE.txt file.
The GNU LGPL v3.0 license is an exception to the GNU GPL v3.0 license.
A copy of the GNU GPL v3.0 license can be found in the LICENSES folder.Thegemseo-benchmarkexamples are distributed under the BSD 0-Clause, a permissive
license that allows to copy paste the code of examples without preserving the
copyright mentions.Thegemseo-benchmarkdocumentation is distributed under the CC BY-SA 4.0 license.Thegemseo-benchmarkproduct depends on other software which have various licenses.
The list of dependencies with their licenses is given in the CREDITS.md file.ContributorsBenoit PauwelsAntoine DECHAUME
Package: gemseo-calibration
DocumentationSeehttps://gemseo.readthedocs.io/en/stable/plugins.html.Bugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo-calibration/-/issuesto submit
bugs or questions.LicenseThe GEMSEO-CALIBRATION source code is distributed under the GNU LGPL
v3.0 license. A copy of it can be found in the LICENSE.txt file. The GNU
LGPL v3.0 license is an exception to the GNU GPL v3.0 license. A copy of
the GNU GPL v3.0 license can be found in the LICENSES folder.The GEMSEO-CALIBRATION examples are distributed under the BSD 0-Clause,
a permissive license that allows to copy paste the code of examples
without preserving the copyright mentions.The GEMSEO-CALIBRATION documentation is distributed under the CC BY-SA
4.0 license.The GEMSEO-CALIBRATION product depends on other software which have
various licenses. The list of dependencies with their licenses is given
in the CREDITS.rst file.ContributorsMatthias De LozzoAntoine DECHAUME
Package: gemseo-fmu
GEMSEO-FMU is aGEMSEOplugin for
loading, interacting, and simulating Functional Mockup Unit models
(FMUs). FMUs are widely used by the simulation community and can be
generated by over 170 tools such as Dymola, OpenModelica, CATIA, ANSYS,
LS-DYNA, or MATLAB (see the full list herehttps://www.fmi-standard.org/tools).GEMSEO-FMU enables the integration and exploitation of FMUs in a
Multidisciplinary Design Optimization (MDO) context, via GEMSEO. For
that, it wraps the FMU model into a GEMSEO discipline named
FMUDiscipline.GEMSEO-FMU relies on theFMPylibrary for loading the FMU models, setting the model parameters and
evaluating model equations. Model Exchange and Co-Simulation types as
well as versions 1.0, 2.0 and 3.0 of the FMI standard are supported by
FMPy, and so by GEMSEO-FMU.Generic examplesMany examples are available to illustrate the main features of
GEMSEO-FMU. See in the examples directory.DocumentationThe documentation is not yet available.Bugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo-fmu/-/issuesto submit bugs or questions.LicenseThegemseo-fmusource code is distributed under the GNU LGPL v3.0 license.
A copy of it can be found in the LICENSE.txt file.
The GNU LGPL v3.0 license is an exception to the GNU GPL v3.0 license.
A copy of the GNU GPL v3.0 license can be found in the LICENSES folder.Thegemseo-fmuexamples are distributed under the BSD 0-Clause, a permissive
license that allows to copy paste the code of examples without preserving the
copyright mentions.Thegemseo-fmudocumentation is distributed under the CC BY-SA 4.0 license.Thegemseo-fmuproduct depends on other software which have various licenses.
The list of dependencies with their licenses is given in the CREDITS.md file.ContributorsJorge Camacho CaseroFrançois GallardAntoine DechaumeMatthias De Lozzo
Package: gemseo-java
Contains two Java-GEMSEO interfaces:One is based on JNIUS (https://pyjnius.readthedocs.io/en/stable)
to call a Java implementation from a standard Python GEMSEO scenario.The other is based on JEP (https://github.com/ninia/je)
to make a Java code create and use a GEMSEO scenario,
eventually containing GEMSEO disciplines implemented in Java.InstallationSetJAVA_HOME: path to the root installation directory of the Java JDK.Install with:pip install .At runtime, you shall configure:On linux,LD_LIBRARY_PATH: add the path to theJEPpython package,On MacOS,DYLD_LIBRARY_PATH: add the path to theJEPpython package,On Windows,PATH: add the path to theJEPpython package,CLASSPATH: add the jep package provided when installing jep in the Python
distribution to the java classpassth,See the JEP (https://github.com/ninia/jep/wiki/Getting-Started) documentations for further information.Java MDODisciplineThe Java abstract MDODiscipline is defined in the packagecom.irt.saintexupery.discipline.Examples for the Sellar problem:com.irt.saintexupery.problems.sellarThe analytical derivatives (gemseo.discipline.MDODiscipline._compute_jacobian) are not supported yet.JEP specific issuesFor the JEP interface,
you shall wrap theMDODisciplinewrapper using theJepMDODisciplineAdapter:importcom.irt.saintexupery.discipline.JepMDODisciplineAdapter;importcom.irt.saintexupery.problems.sellar.Sellar1;MDODisciplinesellar1=newJepMDODisciplineAdapter(newSellar1());ExamplesPlease look at examples/java_examples and examples/python_examples.Frequent issues“Exception in thread “main” java.lang.UnsatisfiedLinkError: no jep in java.library.path:”
Add Jep to the classpath.If Jep is still undetected, please check that the compiled “jep.dll” is well included as a native library.Some ideas on Java-Python bridge technologiesMany libraries provide Java-Python interprocess communications and serialization.
However many of them have limitations such as Jython that does not support all the compiled
extensions of Python because it is a re-implementation of the Python interpreter in Java.
Others use sockets such as py4j and this can deal with performance and security issues.Both JNIUS and JEP are based on the C APIs of CPython and Java JNI (Java Native Interface).
This avoids memory copies,
so precision and performance losses, which is key for numerical computing.JNIUS allows to call Java code from python,
JEP allows to call Python from Java,
and re-enter in the Java code.
However both technologies cannot be mixed,
JEP cannot call JNIUS code.This is why the two solutions are proposed here.AuthorsFrançois GallardPascal Le MétayerAntoine DechaumeLicenseLGPL v3.0
Package: gemseo-matlab
MATLAB wrapper for GEMSEODocumentationCurrently, the documentation of this plugin is available via the
documentation of GEMSEO.InstallationExecuting a MATLAB discipline requires that a MATLAB engine as well as
its Python API are installed. The MATLAB Python API is not defined as a
dependency of this package, because until MATLAB release R2020b there
was no package available in PyPI. It shall be installed in the same
environment as the one in which this plugin is installed, please refer
to the MATLAB documentation for further information.Here are the current versions of the MATLAB Python packages per MATLAB
versions:PythonMatlabmatlabengine3.9r2021b9.11.193.9r2022a9.12.173.9, 3.10r2022b9.13.93.9, 3.10r2023a9.14.3To make sure that MATLAB works fine through the Python API, start a
Python interpreter and check that there is no error when executingimport matlab.DevelopmentFor testing withtox, set the environment variableMATLAB_PIP_REQ_SPECto point to the URL or path of apipinstallable
version of the MATLAB Python API, with eventually a conditional
dependency on the Python version:export MATLAB_PIP_REQ_SPEC="matlabengine==X.Y.Z"DockerTo create or update the podman/docker images for testing the plugin,
adapt with the proper version of matlab:podman build Dockerfile -t gemseo-matlab:r2020b --build-arg=MATLAB_VERSION=r2020bBugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo-matlab/-/issuesto submit bugs or questions.LicenseThegemseo-matlabsource code is distributed under the GNU LGPL v3.0 license.
A copy of it can be found in the LICENSE.txt file.
The GNU LGPL v3.0 license is an exception to the GNU GPL v3.0 license.
A copy of the GNU GPL v3.0 license can be found in the LICENSES folder.Thegemseo-matlabexamples are distributed under the BSD 0-Clause, a permissive
license that allows to copy paste the code of examples without preserving the
copyright mentions.Thegemseo-matlabdocumentation is distributed under the CC BY-SA 4.0 license.Thegemseo-matlabproduct depends on other software which have various licenses.
The list of dependencies with their licenses is given in the CREDITS.md file.ContributorsGEMSEO developers
Package: gemseo-mlearning
Miscellaneous machine learning capabilities.DocumentationHow to get the docs?Bugs/QuestionsPlease use the gitlab issue tracker athttps://gitlab.com/gemseo/dev/gemseo-mlearning/-/issuesto submit bugs or questions.LicenseThegemseo-mlearningsource code is distributed under the GNU LGPL v3.0 license.
A copy of it can be found in the LICENSE.txt file.
The GNU LGPL v3.0 license is an exception to the GNU GPL v3.0 license.
A copy of the GNU GPL v3.0 license can be found in the LICENSES folder.Thegemseo-mlearningexamples are distributed under the BSD 0-Clause, a permissive
license that allows to copy paste the code of examples without preserving the
copyright mentions.Thegemseo-mlearningdocumentation is distributed under the CC BY-SA 4.0 license.Thegemseo-mlearningproduct depends on other software which have various licenses.
The list of dependencies with their licenses is given in the CREDITS.md file.ContributorsMatthias De Lozzo